WSR November 2015

November 2015

The Official Journal of the International Association for Human Resource Information Management

IHRIM.ORG

Compensation/Benefits Buyer’s Guide Page 22

Contents

Volume 6, Number 6 • November 2015

features

Compensation/Benefits Buyer’s Guide

Page 22

columns From the Editor

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Jeff Higgins, Lead Editor Eric Lesser, Contributing Editor Scott Bolman, Contributing Editor

Big Data. Big Challenge. Big Chance.

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By Michael Grimm and Christine König, Ingentis

Product Focus

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By Geri Cruz, Kanjoya

Big Data or Small Data: That Is the Question

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Aggregators and Analytics: Harnessing the Cloud

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By Rana Hobbs, Vestrics

By Helen Friedman and Anna Marley, Towers Watson Wherever you are on your workforce analytics roadmap, one thing is certain: Workforce analytics are becoming a more integral part of how we manage talent, and there’s no turning back now!

Dear HR: Stop Hiring Data Scientists until You’re ready for Data Science

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By Greta Roberts, Talent Analytics Corp.

Getting Started with Predictive Workforce Analytics

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By Wendy Hirsch, Dave Sachs and Mike Toryfter, Johnson Controls We often don’t want to see what might not be working as intended. While the findings may sometimes be unwanted, the insights add value and drive leaders to make better evidence-based people decisions.

Making Your Votes Count: Creating a Game Plan for Strategic Workforce Analytics in HR

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By Mick Collins, SuccessFactors, an SAP company

ROI of Leadership Training at National Cancer Institute

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By Teresa Estrada and Shannon Connolly, National Cancer Institute While some training benefits are intuitive, the key is focusing on how these benefits ultimately add value for the organization. When HR fails to communicate financial impact, the real language of business, it results in a lack of investment in training and budget cuts.

The Back Story

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Ready or Not? Is HR Ready for Analytics?

By Katherine Jones, Ph.D., Bersin by Deloitte

Workforce Analytics: Practical Guidance for Initiating a Successful Journey 14

By Dr. Sheri Feinzig, IBM Smarter Workforce Institute There is reason to believe that HR departments are now gaining competence in the realm of analytics much faster than was the case for any of the other functions with which it is regularly compared.

Executive Interview: Perspectives on Big Data and Data for HR An Interview with Larry Dunivan from Ceridian

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Workforce Solutions Review (ISSN 2154-6975) is published bi-monthly for the International Association for Human Resource Information Management by Futura Publishing LLC, 20505 Live Oak St., Leander, TX 78641-9273. Subscription rates can be found at www.ihrimpublications.com. Please send address corrections to Workforce Solutions Review at the address above. www.ihrim.org • Workforce Solutions Review • November 2015

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Volume 6, Number 6 • November 2015

Workforce Solutions Review is a publication of the International Association for Human Resource Information Management, whose mission is to be the leading professional association for know­ledge, education and solutions supporting human capital management. Opinions expressed herein are not necessarily those of the editors, the IHRIM board of directors or the membership. © 2015 All rights reserved

EDITORIAL COMMITTEE Managing Editor SCOTT BOLMAN, Director, Advisory Services, KPMG, Chicago, IL USA sbolman@kpmg.com.

Co-Managing Editor SHAWN FITZGERALD, Managing Director, Total Rewards and HR Technology, Blue Cross Blue Shield Association, Chicago, IL, USA shawn.fitzgerald@bcbsa.com

Associate Editors Roy Altman, HRIS Manager - HR Analytics & Application Architecture at Memorial Sloan-Kettering Cancer Center, New York, NY roy@peopleserv.com Julie Egbert, SPHR, HRIP, Executive HR Director, SQLC Dallas/Ft. Worth, TX USA Julesegg53@aol.com DAVID GABRIEL, Ed.D., Global Reach Leadership, Berkleley, CA davidcgabriel@gmail.com ROBERT C. GREENE, Channels Account Executive and Sales Training Manager, Ascentis, San Mateo, CA USA rcgreene@mindspring.com JEFF HIGGINS, CEO, Human Capital Management Institute, Marina Del Rey, CA USA jeff.higgins@hcminst.com ERIC LESSER, Research Director, IBM Institute for Business Value, Boston, MA USA elesser@us.ibm.com BRUNO QUERENET, HR Technology Executive, High-Tech and Medical Industries, Sunnyvale, CA USA bruno.querenet@gmail.com MICHAEL RUDNICK, Vice President, Principal Consultant, Logical Design Solutions, New York, NY USA michael.rudnick@gmail.com

JOSH BERSIN, Principal and Founder, Bersin by Deloitte, Oakland, CA USA jbersin@bersin.com NAOMI LEE BLOOM, Managing Partner, Bloom & Wallace, Fort Myers, FL USA naomibloom@mindspring.com YVETTE CAMERON, Research Director, HCM Technologies, Gartner, Littleton, CO Yvette.Cameron@garter.com LEW CONNER, Executive Director, Higher Education User Group, Gilbert, AZ USA lconner@heug.org ELENA M. ORDÓÑEZ DEL CAMPO, Senior VP Globalization Services, SAP AG, Frankfurt, Germany elena.ordonez@sap.com LARRY DUNIVAN, SVP Products and Technology, Ceridian larry.dunivan@ceridian.com GARY DURBIN, Chief Technology Officer, SynchSource, Oakland, CA USA hacker@synchsource.com Dr. CHARLES H. FAY, Professor, School of Management & Labor Relations, Rutgers University, Highland Park, NJ USA cfay@smlr.rutgers.edu

LISA ROWAN, Program Director, HR, Learning & Talent Strategies, IDC, Framingham, MA USA lrowan@idc.com Dr. DANIEL SULLIVAN, Professor of International Business, University of Delaware, Newark, Delaware USA sullivad@lerner.udel.edu MARK SMITH, CEO, Chief Research Officer, and Founder of Ventana Research, San Ramon, CA USA mark.smith@ventanaresearch.com DAVE ULRICH, Professor, University of Michigan, Ann Arbor, MI USA dou@umich.edu DR. MARY YOUNG, Principal Researcher, Human Capital, The Conference Board, New York, NY USA mary.young@conference-board.org

IHRIM BOARD OF DIRECTORS Officers Chair JAMES PETTIT, HRIP, HRIS Manager, Project Byrd – Kimberly-Clark Corporation

DR. URSULA CHRISTINA FELLBERG, Owner & Managing Director, UCF-StrategieBeraterin, Munich, Germany ucfell@mac.com

Vice Chair DAVE BINDA, HRIP, CHRP, CCP, President, HR Results, Ltd.

ALSEN HSEIN, President,Take5 People Limited, Shanghai, PRC Alsen@take5people.com

Chief Financial Officer GARY MORLOCK, HRIP, Senior TRM Project Manager, Qualcomm Inc.

CARL C. HOFFMANN, Director, Human Capital Management & Performance LLC, Chapel Hill, NC USA cc_hoffmann@yahoo.com

Secretary JOYCE BROWN, HRIP, Brinks Inc.

JIM HOLINCHECK, Vice President, Services Strategy & Marketing, Workday, Inc. james.holincheck@workday.com

Past Chair KEVIN CARLSON, Ph.D., Pamplin College of Business, Virginia Tech

CATHERINE ANN HONEY, VP, Customer Services, Radius Worldwide catherine.honey@comcast.net

Directors

DR. KATHERINE JONES, HCM Research, Bersin by Deloitte, San Mateo, CA USA kathjones@deloitte.com SYNCO JONKEREN, VP, HCM Applications Product Development & Management, EMEA, The Netherlands synco.jonkeren@oracle.com MICHAEL J. KAVANAGH, Professor Emeritus of Management, State University of Albany (SUNY), Albany, NY USA mickey.kavanagh@gmail.com

MIKE HARMER, Intermountain Healthcare JAMES LEHMAN, Results Driven Consulting, LLC KEVIN MURPHY, HRIP, Murphy Management Consultants STUART RUDNER, Rudner MacDonald LLP

IHRIM Executive Director TODD S. MANN

BOB KAUNERT, Principal, Towers Watson, Philadelphia, PA USA robert.kaunert@towerswatson.com

PUBLISHING INFORMATION

BILL KUTIK, Technology Columnist, Human Resource Executive, Westport, CT USA bkutik@earthlink.net

TOM FAULKNER, Publisher, Futura Publishing LLC, Austin, TX USA, tomf@futurapublishing.com

EDITORIAL ADVISORY BOARD

DAVID LUDLOW, Global VP, HCM Solutions, SAP, Palo Alto, CA David.ludlow@sap.com

PATTY HUBER, Advertising Manager, Austin, TX USA phuber2@austin.rr.com

CECILE ALPER-LEROUX, VP Product Strategy and Development, Ultimate Software, Weston, FL cecile_leroux@ultimatesoftware.com

RHONDA P. MARCUCCI, CPA, Consultant for GruppoMarcucci, Chicago, IL USA rhonda@gruppomarcucci-usa.com

MARK BENNETT, Oracle Corp., Redwood Shores, CA USA mark.bennett@oracle.com

LEXY MARTIN, Independent Consultant/Researcher, Meadow Vista, CA Lexy.martin1@gmail.com

ERIK BERGGREN, VP, Customer Results & Global Research, Success Factors, San Mateo, CA USA eberggren@successfactors.com

BRIAN RETZLAFF, Head of IT for HR, Legal & Communications, ING US Insurance Americas, Atlanta, GA USA brian.retzlaff@us.ing.com

FREDDYE SILVERMAN, CEO, Silver Bullet Solutions, Baltimore, MD USA, freddye.silverman@mysilverbulletsolutions.com

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feature Jeff Higgins, Lead Editor Jeff Higgins is the CEO of the Human Capital Management Institute, a driving force in workforce analytics helping companies transform data into intelligence via workforce planning and predictive analytics. He can be reached at jeff.higgins@hcminst.com.

from the editors Message from the Lead Editor, Jeff Higgins: Tapping Big Data for Information and Insight

We hope that the features, columns and interviews in Eric Lesser, this issue of WSR enlighten Contributing Editor and provide useful insights Eric Lesser is the research that enable a greater number director and North American of IHRIM members and their leader for the IBM Institute organizations to harness the for Business Value (IBV). He leads a global potential of “big data” and turn team of over 30 professionals responsible for driving IBM’s research and thought that data into information and leadership across a range of industry and true business intelligence. cross-industry topics. He can be reached Putting aside the ongoing at elesser@us.ibm.com. debate as to exactly what big data is and how much data conScott Bolman, stitutes big data, this issue covContributing Editor ers data for HR and its potential Scott Bolman is director, Advisory Services at KPMG. to add value from a variety of He has been helping Huperspectives. man Resources (HR) organizations become As a former CFO, workforce more efficient and effective for over 20 analytics and planning pracyears with various organizations including titioner, and consultant over Towers Watson and Mercer. He can be the last 27 years, I have seen reached at sbolman@kpmg.com. the rise in use and application of both predictive analytics and workforce planning that increasingly uses what many would call big data. While we are still in the early discovery stages of this journey, it is exciting to see the insights and progress shared in the following articles and case studies.

We start with a Shakespearian-like title “Big Data or Little Data: That Is the Question” by Helen Friedman and Anna Marley of Towers Watson to define and discuss the ongoing questions around what does or does not constitute big data in the context of HR technology solution providers, and the logical evolution of HR technology that leverages data for the future.

Next, we have two excellent case study examples of analytics, “Getting Started with Predictive Workforce Analytics” by Wendy Hirsch, Dave Sachs and Mike Toryfter of Johnson Controls, and “ROI of Leadership Training at National Cancer Institute,” by Theresa Estrada and Shannon Connolly at the U.S. National Cancer Institute. These case studies show how organizations turn HR data (big data or not) into insightful information that answers complex organization questions and how such insights can be translated into strategic advantage showing return-on-investment that improves productivity via lower staff turnover, better leaders, and lower workforce costs.

Following our case studies we have a practical guide to workforce analytics, “Workforce Analytics: Practical Guidance for Initiating a Successful Journey” by Sheri Feinzig of IBM. This article includes a 10-step model to help readers navigate and ultimately

implement analytics as a great use of workforce data. The next feature is “Perspectives on Big Data and Data for HR,” an interview with Larry Dunivan from Ceridian who has a great perspective on big data in the HR space, as well as some insights about technologies that are starting to turn both structured and unstructured workforce data into predictive insights.

Our columns for this issue include several great guides to better managing HR technology to create value with the ever increasing volumes of workforce related data inside today’s organization.

Our first column, “Big Data. Big Challenge. Big Chance” by Michael Grimm and Christine König from Ingentis, helps define parameters for HR and big data answering questions such as “What are big data’s sources? Which requirements should HR systems meet? What problems can be solved with big data? Is there a correlation between analyzed HR data and company goals?

The column interestingly ends with an inspirational comment that “Creativity has no limits.”

Our Back Story column, “Ready or Not? Is HR Ready for Analytics?” by Katherine Jones, Ph.D., with Bersin by Deloitte, asks us some hard questions to test if we are really ready to take on the big data and talent analytics challenge leveraging recent survey results from Deloitte’s 2015 Global Human Capital Trends Study.

Our Product Focus features Kanjoya, a company focusing on engagement and customer experience to analyze structured data and unstructured text to optimize organizational effectiveness and develop meaningful customer relationships. The goal is ultimately to eliminate “Gut Feel from Strategic Workforce Decisions.”

Next, “Aggregators and Analytics: Harnessing the Cloud” by Rana Hobbs from Vestrics, talks us through how to aggregate and integrate data to create value from multiple datasets of both structured HR data and possible unstructured data such as engagement and communications data, social media, and more (see acquisition of Volometrix by Microsoft).

In her article, “Dear HR: Stop Hiring Data Scientists until You’re ready for Data Science,” Greta Roberts of Talent Analytics Corp., implores HR organizations to learn how to handle change, and be willing to use analytics to reveal truth by answering tough business and workforce questions. In short, HR must be willing to allow data scientists to move beyond simple reporting.

We close with “Making Your Votes Count: Creating a GamePlan for Strategic Workforce Analytics in HR,” by Mick Collins from SuccessFactors. Mick compares the world of politics with a story about Richard M. Nixon and the difference between volume activity like voting or counting votes, to actually getting things done by making votes count. The column suggests that with better focus, strategic perspective and effort, even limited amounts of data can be used to solve complex workforce problems. In other words, analytics is not about big data, it’s about focus to achieve more with less.

Many thanks to my Contributing Editors for their assistance in bringing this issue to our readers.

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feature Big Data or Small Data: That is the Question

By Helen Friedman and Anna Marley, Towers Watson

B Employers strive to gain deeper insights about employees. And employees, for their part, expect their needs and preferences to be understood. Seventy percent of employees believe that their organization should understand them to the same degree that employees are expected to understand external customers. Yet, only 43 percent of employees reported having an employer that understands them in this way, according to Towers Watson’s 2014 Global Workforce Study.

ig data is a topic garnering much business leadership attention. Whether framed in terms of understanding customers, markets, or employees, the big data quest seems to be one in which many firms aspire to reach the “summit.”

Now, many have read articles defining big data in terms of “volume, variety, velocity, and veracity” and providing case studies where organizations have leveraged big data to identify unique areas of opportunity. Our objective is to address two key questions: • When is big data even needed in HR? • How do we manage such data to support better understanding of our own employees?

How did we get here?

Big data is actually not new to HR. For many years, HR has attempted to get a better handle on its own vast amounts (volume) of information through the use of human resources management systems (HRMS). While in the early days of the design of these systems, many organizations believed that they could “build it themselves.” Today, however, it is the rare few organizations that would even consider this approach as the preferred method to manage workforce-related data.

The HR system evolution timeline

Likewise, today’s organizations no longer think of HRMS data as being sufficient for their analytical and integrated workforce

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management needs. Now, organizations are implementing their own business intelligence tools “fitted” to support data across multiple talent management systems (variety) to better understand and evaluate the talent life-cycle consequences. These systems were generally designed to process and aggregate large amounts of data quickly (velocity), but were not designed specifically for the purpose of workforce data management. So, organizations are, in effect, building their own approach on top of a basic shell. Think back to the days of the home-grown HRMS…remember how that turned out?

And, while these systems have worked to a large degree to report static data, e.g., head count, age distribution, diversity mix, there have been some challenges when reporting workforce dynamics, e.g., internal employee movement, promotions, hours worked. Often, these challenges relate to how data are captured in the originating systems and attempts to work around these challenges without having to manage them at the source – the user entering the original data into the HRMS – not to mention the effects of organizational restructurings and other events that affect the reliability of these data (veracity issues).

What’s next?

If the HRMS timeline is any guide, it is likely that organizations eventually will move away from tools developed in-house (even those leveraging business intelligence platforms) for the same reasons that they moved away from proprietary HRMS platforms years ago. Even though there are fewer resource requirements to maintain these systems, and they can potentially be utilized beyond HR purposes (these issues certainly contributed to the demise of the proprietary HRMS), these systems are still unable to leverage learnings across organizations. Nor can these systems take advantage of combined investment efforts for more costeffective development, such as those available

When was your last organization restructuring, and were job titles, locations, markets, business units, or other such variables reset making it difficult to track workforce data over time? with Software-as-a-Service solutions in the workforce management domain.

Is the solution to leverage an existing ERP system to provide this capability? Not so fast!

While these tools are designed to be strong data management engines (allowing for greater data volume and velocity), there are some key challenges that these systems are still working to address. These systems tend to resolve the question of data variety (through disparate data sources) and veracity (data quality) by having organizations buy into their overall talent management software suite. While this approach is appealing and may, in fact, be what HRMS platforms look like in 2020 and beyond, there are some key shortcomings in their ability to deliver on this “promise” today.

There are a few current realities that need to be addressed: (1) These tools often are the combination of various systems that have been acquired and/or may have been developed independently, so data is not as integrated as it may first appear.

(2) The requirements and investment to maintain the core HRMS functionality tends to overshadow the capabilitybuilding efforts of auxiliary capabilities, e.g., workforce analytics and big data offerings.

(3) Niche, best-in-breed software providers focused on specific areas of market support in the workforce analytics space, still are able to secure customers that are looking for a better or more nuanced product to meet their needs.

To better evaluate what system might best suit your needs, consider what types of data are going to be included in your analytics roadmap and whether these systems can support those requirements going forward (not just visualizations and data management for today’s stakeholders). As an example of what you might

want to include in that longer-term roadmap, see the broad array of potential data elements shown below:

Relevant Big Data in the Workforce Domain Many systems today are well-positioned to manage structured data, but not as many effectively dissect unstructured data. Even fewer systems are capable of extending their data management to open-ended external data sources. So, technology selections today influence your analytics strategy tomorrow.

The 2020 Vision

This brings us to the vision of what might be in place by 2020. Imagine your senior business leaders logging on to their computers for the day, they could see: • The latest summary report on the state of the business and customer performance drivers; • Key market insights automatically pulled from any online, published network around the globe; and, • Targeted employee insights aligned to drivers of business value.

Some of you say: “We don’t have any of this information available today.” Others might say quite the opposite: “We already do this today.”

To see where you are, see the table on page 6.

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About the Authors Helen Friedman is a director in New York, NY, and global leader for workforce analytics and planning for Towers Watson. She has more than 28 years of experience in leading and supporting client efforts across a broad array of analytics, including workforce planning and optimization, dashboards and reporting, site selection and labor market analysis, and predictive analytics. Friedman also is one of Towers Watson’s key thought leaders supporting the development of strategic offerings and technology for global workforce dashboards, analytics and planning. In addition, she has implemented sophisticated statistical modelling techniques that are being applied to uncover drivers of key organizational outcomes, answering questions such as: who is successful at our organization, what drives our turnover, and what workforce factors accelerate revenue growth? She earned her B.A. in Mathematics at Haverford College and an MBA with highest honors at Columbia Business School in Finance and Management. She can be reached at helen.friedman@towerswatson.com.

Anna Marley is a senior consultant in Stamford, CT, with a focus on workforce analytics and planning at Towers Watson. She is a thought leader for the firm in building its approach to human capital analytics and has a wide range of experience supporting evidenced-based workforce management – understanding labor dynamics, building workforce dashboards, predicting labor shortages, driving greater returns from human capital investments. Her experience includes working with clients to ensure that their workforce and business strategies are appropriately aligned through the design and delivery of enterprise-wide workforce planning solutions and optimization, including sophisticated statistical modelling techniques to identify the drivers of business, operational, and workforce performance. Over her years in consulting, she has had both direct consulting responsibilities and global management roles with regard to service development, including leading efforts to build proprietary technology to support workforce analytics and planning. She holds an MBA from the University of Notre Dame and a B.A. in Economics from Bates College. She can be reached at anna.marley@towerswatson.com.

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When to Choose “Small Data:” For those organi- When to Choose “Big Data:” For those who zations just getting started, here are five immediate think that they already do this today, you tips to make progress near-term: may be well on your way to the 2020 vision.

Consequently, your key considerations may look quite different: Assign someone to have the responsibility to lead workforce analytics full time – having analytics be part of someone’s job or treating analytics as a one-off event is not enough to drive a different outcome.

Don’t wait any longer – today, it is extremely rare for a large organization, in particular, to be lacking any data to do some sort of analysis to inform workforce decisions. Start simple and start now.

Focus on the four “F’s” instead of the four “V’s” of Big Data – factors (what do you really need to measure), feasibility (what actually can be measured realistically today), flexibility (how you could approach the analysis to tell a story without getting overwhelmed by data proliferation), and frequency (are there enough outputs and inputs to measure effectively to draw actionable conclusions).

Have a network and career path in place for those on your workforce analytics team – team members may feel “different” than other parts of HR and may not be sure of where to get technical guidance and support for their own career development.

Manage leaders’ interpretations actively – when analytics are successful, they are often replicated or reused for other purposes, not always as originally intended, making active, ongoing messaging key to maintaining credibility in findings and overall approach.

Plan a strategy for long-term success – early “wins” sometimes create an avalanche of attention that is difficult to manage. Therefore, it is essential to have a plan in place to prioritize and eventually systematize core analytics.

Recognize that your organization comes first – many organizations think that they will gain ground by comparing themselves to others through benchmarks, but this strategy has one major shortcoming: It works when it tells leaders what they want to hear and gets challenged when it doesn’t.

Master all aspects of data collection and analysis – best-in-class capabilities typically bridge multiple analytical disciplines/methods and cover the spectrum of internal and external data found in structured and unstructured forms.

Know that a tool, by itself, is not enough – while tools can be essential enablers in building a sustainable workforce analytics capability. Too many organizations have had to reset their strategy after major technology purchases because they didn’t have other key pillars of an analytics strategy in place (including structure, staffing, process, governance, education, and change management).

Change the way the organization fundamentally manages talent – while we’ve seen several strong analytics capabilities that have addressed specific and important talent challenges, few organizations have developed comprehensive and cohesive talent strategies based on data and insights.

While this approach might be considered “small data,” it sure feels big for an organization in the earlier stages of its analytics journey.

In a “war” for analytical talent and data scientists, short-term success can lead to strong personal brands and long-term retention challenges.

Wherever you are on your workforce analytics roadmap, one thing is certain: Workforce analytics are becoming a more integral part of how we manage talent, and there’s no turning back now!

feature

Getting Started with Predictive Workforce Analytics

By Wendy Hirsch, Dave Sachs and Mike Toryfter, Johnson Controls Johnson Controls is a global, multi-industrial company serving customers in more than 150 countries. Its 180,000 employees create quality products, services, and solutions to optimize energy and operational efficiencies of buildings including lead-acid automotive batteries and advanced batteries for hybrid and electric vehicles, and interior systems for automobiles. Johnson Controls’ commitment to sustainability dates back to its roots in 1885, with the invention of the first electric room thermostat. Through its growth strategies and by increasing market share the company is committed to delivering value to shareholders and making its customers successful.

Making the Business Case

Building the Team

We often get asked how the Workforce Analytics Center of Expertise (COE) at Johnson Controls came to be. It’s not an uncommon story. In 2011, Johnson Controls realized that as the business moves beyond borders, so too must our HR processes, services, and technologies. We chose to transform our HR service delivery model to encompass Service Centers, COEs, and HR Business Partners to provide effective and efficient HR services to managers and employees. At the same time, we chose to start the journey towards a single global HR information system to enable a global repository for HR data that will allow us to leverage standard analytical dashboards and scorecards to aid decision-making. The Workforce Analytics COE was commissioned as a result of this transformation and, of course, plays a significant role in this process – that is, in leveraging and analyzing global HR data to inspire, influence, and shape how Johnson Controls makes people decisions.

It also helped that our CEO, Alex Molinaroli, is exceedingly data-driven. He repeatedly emphasizes that Johnson Controls will be a data-driven, not an anecdotally-driven, company. This is true of every function, including HR. “We want new ideas” he says “but make sure that they’re supported with data, not with information from the last person you talked to, or with all the experiences you’ve had in your past. Base it off of what’s really happening in the markets and what’s really happening with our people.”

Job Posting: “Want to be a part of the exciting and burgeoning area of workforce analytics? Johnson Controls has a newly created function within their Human Resources organization focused solely on workforce analytics. Help enable data-driven people decisions and share the insights from the data. Ride the wave of enthusiasm for workforce analytics and help build the function from the ground up, as it grows into a global Center of Expertise. The mission: create an evidence-based culture that endorses analytics at the highest levels. The vision: give global leaders the support they need to test hypotheses and address real workforce and organizational priorities.”

This was the pitch to attract the right talent. It wasn’t easy. For almost a year, the COE had just a leader who struggled to find the right people. But there was a vision – a team comprised of advanced statistics skills, the capability to manage large databases (preferably with an understanding of HR data), and consultative skills. You can be the greatest statistician or the greatest data builder, but if you can’t have a conversation with business leaders about their pain points related to people, then the workforce analytics function will fail. While it took almost a year, the vision was realized. We now have a mighty team of four – an executive director, two managers, and an analyst. Members of the team are aligned to our various business units and HR COEs to provide analytic support and measurement guidance. www.ihrim.org • Workforce Solutions Review • November 2015

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Demonstrating the Capability With Alex Molinaroli at the helm since October 2013, each function was asked to create a scorecard to be presented as part of their business’ monthly operations review meeting.

Human Resources was no exception, and so the People Scorecard was born. (Note that it is intentionally not called an HR scorecard, because it’s not about HR. It’s about people.)

The People Scorecard includes metrics that reflect the flows of talent in, up and out of the organization – with a particular focus on diversity and engagement, two key areas of focus for our CEO. Every business unit’s People Scorecard includes the same metrics, calculated and tracked the same way, and presented in the same format. This has allowed us as an organization to look at potential issues that are enterprise-wide, as well as highlight how each business unit is making progress in key strategic areas.

After launching the People Scorecard, we started to see a slow upward trend . . . there were some in voluntary turnover. This became the universal findings across business case to look at the drivers of voluntary turnover. The project was also the organization. For an opportunity for us to do a proof of example, we found that performance management concept for workforce analytics. It would allow us to show the business what was a really big analytics look like in HR and how leadcontributor to retention. ers can leverage (and act on) workforce analytic results.

For the predictive turnover analysis, we didn’t use the reasons for turnover that are in our HR system. Instead, we examined all of the events that are coded in our HR systems and assessed what it is about these events that happened to people over time that lead some employees to stay and some to leave. We looked at what jobs employees are working in, how long they’ve been in their role, how long they’ve been with Johnson Controls, how much they’re paid, individual performance ratings, training participation, history of promotion, performance ratings of their supervisor, work location transfers, job transfers, international assignments, and so on. We used a multivariate regression model to conduct the analysis and performed separate analyses for each business, including

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corporate.

The results varied a little by business as you might expect. However, there were some universal findings across the organization. For example, we found that performance management was a really big contributor to retention. At Johnson Controls, we tend to keep our high performers and our low performers tend to self-select out. And, like most organizations, we tend to see voluntary turnover when employee transitions are not managed well. Sometimes transitions, such as being assigned a different supervisor, having your supervisor leave the organization, or transferring jobs or locations, are not managed well and result in turnover.

Assessing the Value There were two goals for our turnover analysis. One was to understand the drivers of turnover; the other was to do a proof of concept for HR analytics. And, as a result of this project, our organization has a better understanding of what predictive analytics in HR entails and what analytic support and insight the Workforce Analytics COE can provide.

And more people are coming to the team with requests for analytics as a result.

We keep track of the specific actions that people are taking as a result of our insights and analysis. We don’t keep track of team activity; instead, we keep track of all the things that our customers are doing as a result of our guidance. For us, this is a measure of whether or not we’re having an impact – the degree to which people are able to take action based on our insights.

One example of action taken based on our analytic results is around performance management. As noted earlier, performance management is a critical driver of retention for us.

Employees who don’t get a performance rating are at much higher risk of leaving the organization. In our performance management system, if you don’t have an approved goal plan, you aren’t eligible to get a performance rating. One of the biggest pushes we’ve seen from our turnover analysis is ensuring that people are having performance conversations,

getting their goal plans approved, and having those performance reviews at year end. These actions were taken because we proved (with evidence) how tightly performance management is tied to retention.

Beyond the actions taken, we still want to prove the “hard value” of our analytic work by linking our HR data with financial and operational data. This will allow us to identify what it is about how we staff, manage, reward, supervise, and develop talent that drives financial and operational measures.

Generating Lessons Learned Along with these successes, we’ve certainly garnered many lessons learned. First, wait for the right talent on the team. Don’t give up on the vision for the mix of skills you think is critical. It may take a while, but it will pay off in the long run.

Second, resist the urge to go right to predictive analytics out of the gate. It was almost a year and a half before we presented the initial predictive turnover findings. It is essential to first establish credibility with business leaders. They need to have a comfort level with you, and trust that you understand their data.

Don’t undervalue the importance of establishing this credibility first and don’t underestimate the importance of validating your data with your end customers. That was a key part of our process for the predictive turnover work. Ensure that you have several weeks to several months for back-and-forth dialogue with the business. The key is to ensure that when you get to the predictive analytics work, they’re not questioning the data. Instead, at that point, they’re taking the data as is, and spending time interpreting the results you have to share.

Another lesson learned that coincides with establishing credibility and data validation is ensuring that you socialize the results in a systematic (and thoughtful) way. We used to believe that 90 percent of predictive analytic work was data building and analysis, and 10 percent was delivery and socialization of results. But, we were sorely mistaken! In

actuality, half of the work is in the dissemination and socialization of the results. It takes a while for the messages to sink in. Don’t give up.

Keep sharing. Keep driving leaders to action.

Third, recognize that because the interest in predictive analytics is so great, it can be tempting to take on more analytic work than your customers can realistically consume. Be sure to move at a pace that is aligned with the needs (and interests) of your business leaders. We’ve been at this for two years now and we’ve done some impactful work. Could we have done more? Absolutely! But, we likely would have failed. We needed to give business leaders time to act on the insights we provided before producing more.

Exercising the Analytic Muscle Through all of this, we can’t underscore the importance of consulting, networking, and staying in touch with business leaders and HR business partners. You have to keep connected and ensure you’re spending time on relevant opportunities.

A leader recently said to us, “You can’t hide from analytics!” That’s very true. We often don’t want to see what might not be working as intended. While the findings may sometimes be unwanted, the insights add value and drive leaders to make better evidence-based people decisions. You might even equate us to “personal trainers.” You know you should go to one, you hate how they push you to your limits, but you feel better and stronger about yourself and your capabilities when you’re done; and, you go through that same mental cycle every time you have to meet the trainer. Just keep pushing your customers to exercise their analytic muscle!

About the Authors Wendy Hirsch is the executive director of Workforce Analytics at Johnson Controls. She is responsible for the design and implementation of Johnson Controls’ global workforce analytics strategy and program, overseeing all aspects of workforce analytics across the enterprise and providing accountability and oversight for the Workforce Analytics COE. Prior to Johnson Controls, she spent 15 years with Mercer’s Global Workforce Analytics and Planning Group. At Mercer, Hirsch led workforce analytics initiatives for organizations across various industries, helping them improve bottom-line results through human capital strategy and measurement, workforce planning, attraction and retention, pay equity, and diversity. She holds a Ph.D. in Industrial/ Organizational Psychology from the University of Illinois at Urbana-Champaign. She can be reached at Wendy.L.Hirsch@jci.com.

Dave Sachs is a Workforce Analytics manager for Johnson Controls. As a member of Workforce Analytics COE, he assists both business HR leadership and corporate COE leadership with predictive analytics, reporting and workforce planning.

Previously, he was a senior associate at Mercer for 10 years in both the retirement and talent business where he used data and analytics to help companies with people measurement, assessing retirement risk, workforce planning, attraction and retention, pay equity, and diversity. Sachs holds a bachelor’s degree in Actuarial Science and Economics from the University of Iowa. He can be reached at mike.sachs@jci.com.

Mike Toryfter is a Workforce Analytics manager at Johnson Controls. As a member of the Workforce Analytics COE, he helps drive business performance and enterprise initiatives using predictive analytics, advanced reporting, and workforce forecasting. He has 10 years of experience with Kohl’s in workforce analytics, HR reporting, and HR technology. In his role as senior manager of People Analytics, he helped Kohl’s drive an evidence-based decision culture, execute workforce planning/ modeling, and performance review design. He holds a bachelor’s degree in Finance and Economics from the University of WisconsinMilwaukee. He can be reached at mike.toryfter@jci.com.

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feature ROI of Leadership Training at National Cancer Institute

By Teresa Estrada and Shannon Connolly, National Cancer Institute

Most organizations agree that employee training programs are a valuable part of human capital strategy, yet few have successfully quantified the ROI of that investment. A framework for measuring training impact isn’t a new idea, however. The Kirkpatrick-Phillips model of evaluating training was published in the U.S. Training and Development Journal in 1959, and consists of five levels: 1) Reaction and Planned Action, 2) Learning, 3) Job Applications, 4) Business Results, and 5) ROI. So why have organizations struggled with the practical implementation of this model?

While some training benefits are intuitive, the key is focusing on how these benefits ultimately add value for the organization. When HR fails to communicate financial impact, the real language of business, it can unfortunately result in a lack of investment in training and budget cuts. However, before organizations can move to levels four and five of the Kirkpatrick-Phillips model, they must track, gather, and integrate the right data. The workforce is complex, but it can be measured. The endgame for truly measuring training effectiveness starts with linking training outcomes, such as improving retention and controlling workforce cost growth, to the bottom line.

For the National Cancer Institute (NCI), a component of the National Institutes of Health (NIH), it’s critical to develop a dynamic workforce empowered to solve cancer’s greatest research challenges. NCI’s Office of Workforce Planning and Development (OWPD) is responsible for training and developing a high performing workforce that optimizes organizational capacity. Understanding and illustrating the impact of OWPD training programs allows NCI to better execute on their mission and implement continuous improvements.

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Background In partnership with Human Capital Management Institute (HCMI), NCI’s OWPD conducted a detailed analysis to quantify the outcomes and ROI of its supervisory and leadership development programs for both individuals and the organization. We looked specifically at the impact that participation had on these programs: 1) individual participants, 2) participant workgroups, and 3) the larger organization.

The analysis focused on long-term, supervisory, and leadership development courses offered to employees at various career stages, including future, newly appointed, experienced, and senior leaders. Specific training courses included: Senior Executive Enrichment and Development (SEED), Leadership Education and Action Program (LEAP), Knowledge Management (KM) Mentoring Program, Executive Coaching (EC), and The Empowered Supervisor (TES). This article will refer to these programs as the Academy and to its participants as Alumni. The Academy focuses on the nontechnical skills needed to be an effective leader and supervisor. Examples of topics covered include conflict resolution, communicating for results, delegating work, leading change, and influence. The curriculum for the Academy is informed by the NIH Leadership and Management competencies, benchmarking other similar programs, and best practices in the field of leadership and management science.

SEED is a yearlong program for supervisory GS 14-15 and higher level employees. Program components include: half- and whole-day workshops facilitated by internal and external experts, individual coaching, and other experiential activities. SEED Alumni are invited to continue learning as a community of practice

and to build networks, foster collaboration, and improve communication across the Institute.

LEAP is a yearlong program for non-supervisory GS 13 and 14 level employees. Program components include: half- and whole-day workshops, group coaching, and practical application sessions. LEAP Alumni are also encouraged to participate in a post-program community of practice.

KM is a yearlong mentoring program open to any employee in good standing. KM promotes sharing and teaching of critical skills and institutional knowledge, and nurtures professional growth. Program components include: monthly one-hour mentoring sessions, monthly two-hour program workshops, and individual and team presentations.

EC is open to employees at the GS 13 level or higher and consists of twelve, 1-hour individual sessions with a trained, certified Executive Coach. Coaching focuses on improving workplace effectiveness by minimizing unproductive behavior and maximizing productive behavior.

TES is a five-month program for new and experienced supervisors. Program components include eight 4-hour workshops, six 2.5-hour group coaching sessions, and book and article readings and discussions. This highly interactive program is designed to aid supervisors in maximizing their ability to manage and develop employees, manage workflow, influence the organization, and manage themselves.

Analysis Process To build the analysis model, over six years of Alumni training results were integrated with other workforce and financial data. As part of the data gathering process, the first step was identifying the appropriate information to combine with the Alumni training data. The core project team focused on the following data categories to include in the model: workforce head count and transactions, terminations, promotions and transfers, employee demographics, supervisory reporting relationships, performance and monetary awards, training costs, and replacement costs. In addition, the team gathered

training satisfaction surveys from the OWPD training courses, and also an extract from the organization-wide learning management system to evaluate the impact of the broader training offering across NCI. Once the requirements were identified, the next hurdle was building the data extracts. While existing reports were already in place, the NCI team had to both modify and build new ones to pull the relevant data.

Not surprisingly, all the required data were not maintained in a single system or data warehouse. The OWPD team leveraged data sources including NCI’s core HRIS, HR data warehouse, secondary HR systems, historical databases, surveys, and financial reporting.

The data gathering was primarily led and conducted by the OWPD team. Following some initial challenges of data access and data extract validation, the team thoroughly reviewed and anonymized the data before passing it along to HCMI for positioning and analysis. Throughout this process, data gaps were identified, issues corrected, and updates were made as required. As the data model came together, positioning was done to create categories for analysis that included custom workforce groupings, training cohorts, tenure groups, retirement and age categories, manager and department groupings, and more.

As the analysis process continued, the team also focused on identifying the primary evaluation measures that would define success for this project. These included retention, monetary awards, performance ratings, and promotions, which were investigated for both training Alumni and the employees they managed. Post-training results for Alumni were trended and compared to similar demographic cohorts across the organization that didn’t receive training, as well as the Alumni population prior to receiving training. Ultimately, after months of data gathering and analysis, the results were reviewed, validated, and presented to key stakeholders across NCI.

Analysis Findings The analysis showed that Academy training has a significant positive impact across all workforce measures evaluated. Academy

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Alumni have lower turnover, higher performance, more frequent and higher value monetary awards, and higher promotion rates than Non-Alumni. In addition, these trends are not limited only to Alumni, but also extend to employees they manage.

Highlights include: •

Alumni are more than twice as likely to be retained as Non-Alumni, and Alumni high performers are almost half as likely to turnover.

•

Alumni are more successful at developing and retaining talent. The employees they manage are more than twice as likely to be promoted and approximately 35 percent less likely to turnover.

•

Alumni are 35 percent more likely to be high performers than Non-Alumni, and also receive almost 40 percent more value in monetary awards than NonAlumni.

•

Projected ROI of OWPD training is between $3.9 and $5.5 million annually over the next five years.

Figure 1 shows the turnover trend of training Alumni as compared to Non-Alumni between 2009 and 2014. On average, turnover is approximately 50 percent lower for training Alumni than Non-Alumni. This trend also holds true for high performer turnover, and is consistent across job groups and organization units. With organization-wide retirements projected to increase significantly over the next five years, OWPD training programs will be an integral component to mitigate turnover and knowledge loss going forward.

Figure 1. NCI Alumni and Non-Alumni Overall Turnover Rate.

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Perhaps more impressive, Alumni leaders are more successful at developing and retaining talent; the employees they manage are more than twice as likely to be promoted, and approximately 35 percent less likely to turn over compared to those managed by Non-Alumni.

Figure 2 shows the managed promotion rate trend between 2009 and 2014, highlighting significantly higher promotion rates for employees managed by Alumni graduates. This means that Alumni graduates are creating value for NCI by participating in training. Furthermore, Alumni leaders do a better job of promoting participation in NCI’s broader training offering, and are also more frequently selected for larger roles across NIH.

Figure 2. NCI Alumni and Non-Alumni Managed Promotion Rate.

Due to significantly higher rates of retention and promotion, the projected ROI of Academy training programs is between $3.9 and $5.5 million annually for NCI through 2019. In addition to cost savings from improved retention, employees that are promoted have lower salary and total compensation costs than their externally hired counterparts. Figure 3 shows the ROI projections over the next five years, and includes both direct expense reductions (hard costs) and future efficiency savings (soft costs), less Academy training program costs and unpaid salaries due to vacancies. The largest portion of the savings estimates are anticipated from reductions in replacement hire costs and salary savings from internal promotions, with additional savings projected due to reduced transaction processing, severance, vacation payout, and benefits continuation costs.

About the Authors Teresa Estrada is a program analyst with the Office of Workforce Planning and Development (OWPD) at the National Cancer Institute (NCI) and has over 15 years’ experience in training, education, and evaluation with a particular emphasis on fostering the careers of aspiring scientists. Dr. Estrada is responsible for evaluating the OWPD’s training portfolio and for managing the use of workplace assessments. She oversees NCI’s participation in Project SEARCH, a workforce and career development program that prepares young adults with intellectual disabilities for employment, and is a member of NIH’s Project SEARCH Sustainability Team. She is a Certified Action Learning Coach and a Sherpa Executive Coach. She earned a Ph.D. in developmental psychology from the University of Notre Dame.

Training Academy ROI Components:

Note: transaction processing costs include time and resources spent on advertising, compensation review, interviews, and HR package processing. Costs are estimated and allocated based on reduced levels of terminations.

Figure 3. NCI OWPD Training Academy ROI.

Looking Ahead Based on these findings, OWPD is eager to expand NCI’s training offerings, with an emphasis on high potentials and missioncritical job roles. As part of a comprehensive workforce planning strategy, training programs will be leveraged to help mitigate the risk of future knowledge loss for job groups and departments with the highest percentage of employees approaching retirement eligibility. In addition, increased focus on leveraging training to foster opportunities for internal mobility will help build defined career paths across NCI.

Ultimately, Academy training programs will help NCI and NIH build management bench strength for the future, increasing the percentage of internal promotions into management roles over the next five years. Internal promotions also result in lower salary costs, which help control Total Cost of Workforce, and mitigate future budget constraints.

Currently, NCI and the OWPD team are evaluating additional analytics project opportunities and implementing HCMI’s training effectiveness dashboard to monitor and optimize future results.

Shannon Connolly is a Certified Executive Coach in the NCI’s Office of Workforce Planning and Development (OWPD) where she is the chief of the workforce development branch. She is responsible for the oversight and implementation of OWPD’s organizational and individual employee development services, including its supervisor training program, leadership development programs, and coaching program. She is also the office’s lead trainer/facilitator. Prior to joining the federal government, she worked in the public and non-profit social service sectors, providing direct-service work, and then first-line supervision and program management.

She received a Master of Social Work from New York University in 1997 and a Master of Public Administration from the University of Delaware in 2006.

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feature Workforce Analytics: Practical Guidance for Initiating a Successful Journey

By Dr. Sheri Feinzig, IBM Smarter Workforce Institute The potential for big data and analytics to have a game-changing impact on the Human Resources (HR) function has been discussed extensively in business publications, trade journals, and professional conferences. By embracing an analytical approach, it is argued that HR can “earn a seat at the table” and demonstrate with confidence the impact of people-related decisions and actions on business outcomes. Despite these bold possibilities, however, most organizations are at a relatively early stage of adopting analytics for HR. There appear to be two main reasons for this. First, HR has struggled with how best to show the returns that justify the capital expenditure required to establish analytics capability. Second, HR has only recently been staffed by professionals focused on the business consequences of HR actions, rather than purely policy, procedures, people, or performance.

These two legacy characteristics of HR have proven stubborn obstacles to the adoption of analytical approaches to decision-making about people, leading to claims that HR has a great distance to go before reaching the type of analytical capability we see in other business functions such as Marketing.

However, there is reason to believe that HR departments are now gaining competence in the realm of analytics much faster than was the case for any of the other functions with which it is regularly compared. First, many of the business case challenges that Marketing has competently navigated have parallels in HR, and in learning from Marketing’s experience, HR can reduce the time for implementation. For instance, concepts and relationships in Marketing such as customer dissatisfaction leading to customer churn have analogous concepts and relationships in HR, in this case, employee engagement and staff turnover. This

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means HR can directly apply many of the techniques marketers have developed.

Second, where Marketing and Finance developed analytic capabilities in an age when technology was prohibitively expensive, technologies such as cloud and delivery mechanisms such as Software-as-a-Service (SaaS) mean it is now feasible for organizations to purchase the required technologies on corporate credit cards with a modest budget. Finally, professional HR degrees and qualifications are emerging that focus specifically on analytics, meaning any historic lack of analytic capability will not exist for much longer.

These positive developments are certainly encouraging, but it is important to recognize that becoming analytically enabled will not simply happen organically. Instead, HR needs to systematically plan for success in the area of analytics. This leads to the question, how should an HR department get started? One approach is to learn from those who have been down that road, who have found ways to navigate the challenges and overcome the obstacles, and who have built the capability and reaped the benefits from an analytical approach to HR. Such learnings have been revealed and encapsulated from interviews with dozens of experts,1 with the goal of providing a clear, straightforward framework and practical guidance to HR professionals and business leaders ready to begin the journey. The framework is described below. But first, it will be helpful to ensure a common understanding of the topic at hand. “Analytics” can be defined in many different ways, from simple reporting and benchmarking to the application of advanced mathematical algorithms. Within the context of the framework below, workforce analytics is defined as: a diverse collection of data analytic

approaches for uncovering unique insights about people in organizations that enable faster, more accurate, and more confident business decision-making.

With that definition as a foundation, the following 10-step framework is offered as a guide to successfully launching a workforce analytics function.

Step 1 – Articulate your Objectives Conversations about workforce analytics often begin with data, but the better starting point is to clearly articulate the objectives for your HR function. Without knowing what the vision is for analytics in HR, the developmental path of the function is likely to wander without direction.

In addition to stating a vision that communicates intent, direction and energy, the scope should be clearly delineated. The scope can be more modest and contained, e.g., reports and dashboards of key HR metrics, although a broader scope encompassing advanced analytics such as predictive modeling is recommended, as the impact on the business will be much greater.

Step 2 – Define your Governance Model Once the analytics objectives are established, it is important to communicate expectations and specify working relationships with stakeholders. The workforce analytics function should help ensure that the work of HR contributes positively to business outcomes, so it is essential to work with business leaders and HR partners to identify business challenges and opportunities that can be addressed through people-related actions. Specifically, members of the analytics function will need to understand the key metrics used to manage the business, and current (baseline) levels of those metrics.

Human Resources partners can help identify which HR-related actions influence the business metrics.

It is also necessary to understand data privacy legislation in all countries where data will be collected and analyzed. These rules and regulations must be respected and adhered to at all times. A recommended approach is to encourage employees to actively participate and contribute their data to workforce analytics efforts. This can be achieved by gathering

feedback from employees on the goals of the analytics efforts, allowing employees to opt in or out, providing recognition or reciprocation for sharing of data, and being transparent about the entire analytics process.

Finally, at this early stage, the analytics team should already be planning for action that could result from the analytics efforts. The whole point of analytics is to generate actionable insights to positively affect the business.

Without action, analytics findings may be interesting, but of limited relevance and no payback.

In addition to planning for action, plan to evaluate the effectiveness of those actions and be prepared to course-correct as needed.

Step 3 – Get a Quick Win The selection of an initial analytics project is an important early decision that can set the stage for establishing the legitimacy and value of the analytics function, or alternatively, result in an uphill battle for demonstrating The first project should relevance. The first project should yield a quick win, with evidence of a yield a quick win, with positive impact on the business within evidence of a positive a reasonable time frame. While the impact on the business ultimate steady-state goal is to have a within a reasonable portfolio of projects that collectively deliver great value to the business, it time frame. is best to start with a project requiring little change management to implement and realize benefits. The quick win will establish credibility and secure the funding necessary to grow capability further.

Step 4 – Know your Data Once the objectives are defined and agreed to, the stakeholders are engaged and onboard, and the first project has been selected, it is time to talk data. Data quality is a very real issue – the usefulness of data analytics hinges on the quality of the data being analyzed. The well-worn phrase “garbage in, garbage out” is wholly appropriate in the context of workforce analytics. That said, all hope is not lost when faced with imperfect datasets and it is, in fact, unrealistic to expect data perfection.

A common issue is missing or outdated data.

When faced with a scenario where the available data are simply not good enough to answer the business question at hand, options include refreshing the data, initiating new data collec-

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tion, or identifying a creative way to fill the gap with high quality data that are available and can approximate the needed information. For example, if promotion data are needed but do not exist in the HR systems, promotion history could be approximated by using a combination of salary and title change data.

A good approach to assist with data quality decisions is to enlist the advice of stakeholders familiar with the data of interest, as they will be best positioned to know when the data quality is sufficient to produce useful results.

While the world of big data opens up a new realm of possibilities for workforce analytics, it is important not to lose sight of the basics. In some cases, it may be better to collect a small new dataset rather than analyze a big existing dataset that does not contain the necessary variables or cases to answer your question accurately. A pragmatic approach is recommended: as long as the data are sufficiently valid and the analysis prompts rigorous discussion of alternative courses of action, it will have served a useful purpose.

While considering technology options, it is important to be aware of the local legal environment. Given the sensitive nature of much HR data, the location of and access to the information will need to conform to legislative and regulatory requirements.

Step 5 – Know your Technology Options

Step 7. Identify Roles and Skills

Historically, the technology required to properly equip an analytics team involved a substantial investment in systems, software and infrastructure, along with specialized technical expertise for implementation Endnotes and maintenance. This amounted to 1 Guenole, N., Feinzig, S., Ferrar, J. & Allden, J., Starting the workforce analytics journey: a substantial technology decision and the first 100 days, IBM Smarter Workforce capital expenditure requiring approvInstitute report, 2015. al from the chief technology officer and chief financial officer. Today, the situation is very different. Rapid advances in cloud technology and as-a-service delivery models have shifted the discussion around the cost of analytics in HR. Large up-front capital expenditures are no longer required, and the technology solutions needed are more likely to fall within the operating budgets of HR departments.

Other recent technological advances include visualization techniques, which help transform analytics findings into powerful storytelling, and cognitive computing accessible to business leaders through natural language querying. In-depth technical expertise is no longer required.

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Step 6 – Know your Partner Options Establishing an HR analytics function does not necessarily mean building a large, inhouse team. While that is certainly one option, it is not the only one. An alternative option is to outsource the capability to an external provider, and yet another is to partner with existing internal analytics capability residing in another function in the organization. All of these options require a core set of foundational capabilities, including business knowledge and acumen, HR expertise, and organizational transformation capability. Beyond that, there is flexibility depending on budgets, availability of required skills, and the depth of analytics capability elsewhere in the organization.

Regardless of how the analytics function is structured, a leader will be required. This is where the business knowledge and acumen are essential. Ideally, the leader will also have a depth of HR knowledge. For example, it will be important to understand which variables and characteristics are irrelevant to business outcomes or even off-limits from a legislative perspective, despite any apparent relationships that might be revealed in the data analysis. As an example, things such as marital status or number of children should not be entering the conversation. Analytics savvy is important, as the leader will need the ability to stress-test findings when necessary, and to stand behind recommendations knowing the underlying analyses are sound.

Change management is another important skill. Analysis of workforce data should be followed by action, and the actions will typically require people to do things differently. The changes might range in scope from process and system changes that affect a small group of people in a function, to fundamentally changing the way work is performed by thou-

sands of people. The ability to manage a transformation in the business is essential for fully realizing the benefits of analytics initiatives.

Other skills that will be needed in the analytics function (either in-house or supplied through a partner) include data science and technology skills, statistical analysis capability, and knowledge of sound research design and methodology. Core consulting skills are also important for defining business problems to be addressed with workforce analytics, and for project management, problem solving, facilitation, and stakeholder management.

Step 8 – Complete your Business Plan Once the team is established, the operating model needs to be defined. One option is to enable all HR staff to apply analytics as an integral part of their jobs. While the payoff of this approach is considerable, it may not be feasible given time constraints, backgrounds, and interest levels of traditionally trained HR staff. Another option is to create a dedicated HR team or center of excellence. This will yield a focused team with a clear analytics mission, although the process associated with establishing the group may detract from executing the work. A third option is to join forces with an existing analytics function elsewhere in the company, which has the benefits of a ready supply of broad and deep analytic skills. The downside is potential distraction from peoplerelated business problems. The use of external service providers is a fourth option, which has the benefit of outsourcing the data and technology challenges, but offers less control over the actual analyses. Trade-offs need to be considered in the context of an organization’s specific circumstances and requirements.

When establishing a workforce analytics function, it is helpful to adopt the discipline of a consultancy model: clearly define project deliverables, timing, resource allocation, and internal client agreements. A business case will

need to be established, and emphasis should be placed on linking people-related issues to business performance. Set clear expectations of the type of results expected from initiatives, and explain the meaning of statistical relationships in business terms. Solid case studies of similar work can be helpful here.

Step 9 – Build Momentum When the team is in place and projects are underway, proactive communication and influencing are needed to ensure the business value is widely known. This begins with understanding the target audience, planning messages specific to the audience’s needs, identifying potential obstacles to be overcome, selecting the best communication channels, and following up to ensure the messages were properly received and understood.

Step 10 – Implement

About the Author

The final step is ongoing implementation. Apply analytical models to understand linkages among data sources. Demonstrate the relationship between HR policies and practices, workforce effectiveness and business outcomes. Take action based on the insights revealed by the analyses. And, finally, evaluate the impact of those actions with methodological rigor to rule out competing explanations for the observed results, and make any necessary adjustments to optimize outcomes.

With proper planning and disciplined execution, workforce analytics can help an organization achieve the aggressive goals that may have seemed elusive prior to the inclusion of people data as an essential part of the equation.

Dr. Sheri Feinzig is the director of IBM’s Smarter Workforce Institute, and has over 20 years of experience in human resources research, organizational change management, and business transformation. She has applied her analytical and methodological expertise to numerous research-based projects on topics such as employee retention, employee engagement, job design, and organizational culture. She has also led several global, analytics-based multi-year sales transformation initiatives designed to optimize seller territories and quota allocation. Additional areas of expertise include social network analysis, performance feedback, and knowledge management. She received her Ph.D. in Industrial/Organizational Psychology from the University at Albany, State University of New York. She has presented on numerous occasions at national conferences and has co-authored a number of manuscripts, publications and technical reports. She has served as an adjunct professor in the Psychology departments of Rensselaer Polytechnic Institute in Troy, New York and the Illinois Institute of Technology in Chicago, Illinois, where she taught doctoral, masters and undergraduate courses on performance appraisal, tests, and measures. Dr. Feinzig can be reached at sfeinzig@us.ibm.com.

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feature Perspectives on Big Data and Data for HR An Interview with Larry Dunivan at Ceridian WSR: Thank you for taking the time to talk to us Larry. Ceridian, being both a talent management software provider with Dayforce Human Capital Management solutions, and one of the largest payroll solution providers, gives them an inside perspective into both management of big data and how it might apply to HR. Can you share with us a bit about your background in the space?

Larry: I have been in charge of global products and technology, as well as a company CIO at Lawson. I’ve spent 30-plus years in the HCM technology arena. I worked for a company called Cyborg in the 1980s and 1990s, and then I worked for Lawson for about 15 years. I’ve been at Ceridian since late 2011. Most of that time I’ve spent in product management and product development.

WSR: And how would you contrast your time at Lawson versus the time with Ceridian?

Larry: Well, it’s different. I spent the majority of my time at Lawson building a talent management system from scratch, particularly for the last five years I was there. And then I ran HCM business units.

So that was about making a more metered transition to the cloud, whereas at Ceridian, the vast majority of my work in the last four years has been transforming Ceridian from being a pure service bureau to being a cloud HCM vendor; a little more of a dramatic shift.

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WSR: That really leads us into our next question which is what is big data to you? What is big data to Ceridian? Because I think a lot of readers hear a lot about big data, but don’t necessarily know if they are touching it or if it is something that’s touching them? Everyone gets that it’s somehow on the Internet, but besides the social media aspects, how would you characterize it in particular from the HR and an HRIT systems space?

Larry: Yeah, sure, I think there’s nothing particularly unique about it. When you ask interesting questions about the vast volumes, the terabytes of data that get collected in the operational work of HR, when it gets big, how do you do something more interesting with it? We always have this contrasting discussion with customers about the day-to-day activity to get data out of the system for much more operational activities versus more strategic activities.

Operationally, whether it’s interfaces to a 401K provider, or it’s onboarding for system access to a provisioning system, contrasted with how I can do something that’s much more interesting when you start thinking about big data. The challenge and the opportunity is how do you derive the relationships between those bits of data? And more importantly, establish the algorithms that take that data that’s much more unstructured and turn it into insight?

WSR: So, would you say that big data is real, or is it just another catch phrase that organizations are just selling?

Larry: I’m always a cynic with the terminology. And literally, I have said in certain situations that using the term big data gets you credit probably beyond your experience or education. I’m confident that the vast majority of the world knows better, but you start to wonder. However, I think it’s real and I think Google is an example we can all appreciate. Whenever I’m looking for information about a hotel, Google is always my user interface because it will find that hotel and the direct link in a fraction of the time it would take me to go to the hotel chain website and look it up. I mean, a tenth of the time. And it’s because they’ve figured out a brilliant way to harness big data.

They know that a few unstructured pieces of data combined can find that Marriott in the suburbs of Cleveland, Ohio. And then they’ll even find the rate for tonight through a simple instructional query. That’s a really advanced, mature use of an enormous amount of data. Big would be an insult. It’s a decent analogy of what we’re trying to do in HR, but we’re a two-year old when Google is a 30-something.

WSR: Certainly Google is a very real example of how big data works. I use Google Maps when I drive, and it not only tells you how to get there, but it looks at traffic in real-time and tells you whether there’s a faster route, which is using some really big data.

Larry: The analogy for HR is to come in with all this data and predict that: “Harry Jones is probably going to resign because of all that data I collected!” Obviously, you would not make a black and white decision to trust the data, like we do to take us to the restaurant, but it would give us an indication of something that’s actually pretty actionable.

WSR: What other value or potential do you see for big data in the HR space?

Larry: There are all kinds of opportunities to look at the abstractness to derive generalization. And the Google example is a really good one. I think that there are great examples where you look at the kinds of activities that are logged about people to establish trends that might have either compliance, discrimination, or other ramifications. A good example that comes to mind is 401K discrimination testing, obviously a very transactional compliance thing. So, there are tons of opportunities, but we’ve only scratched the surface.

WSR: Which data is most valuable from an HR standpoint and why?

Larry: I don’t know. As I think about it, I would say that it’s a dilemma because you have very objective data, but in large volumes the data imputes decisions which traditional reporting has not gotten us to.

For instance, advancing people of color compared to people that aren’t of color. You promote people, you change jobs, you do add or removes, but doing the analysis of it in the context with other variables, such as race, gender, etc., and we find that traditional reporting never does a very good job.

The other view is just the data that is inherently unstructured about talent. And that’s probably where there’s a bigger opportunity.

WSR: Can you talk about where IT directors and CIOs can start to capture the potential of unstructured combined with structured data for some quick wins or valuable insights?

Any guidance or examples you can share?

Larry: If I put my hat on as a CIO I would say that the stuff that interests CIOs the most is not HR data. I’ve always joked about

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my career in HR as number 11 on a list of 10. Because, guess what, when it comes to big data, the CIOs pay way more attention to issues about financial data or sales data or customer satisfaction data.

So what are those things that I can conclude from HR data that directly will save us money or increase revenue or lower cost to serve?

When you start trying to engage them (CIOs) in that discussion of “Hey, there’s this trend in terms of how people are being evaluated that could mean that the workforce as a whole isn’t maturing all that well.” Let’s not fool ourselves. I don’t think the (HR) industry, from a technology perspective, has evolved to the place where we can put those kinds of tools in the hands of HR users and really move the needle yet.

WSR: Every technology provider is advertising analytics and predictive this and that. So, how do you reconcile what everyone’s saying with where the industry seems to be?

Larry: I think this is the normal evolution of the market cycle. The market hype is often ahead of the technology reality. And that’s fine, I mean, that’s just typical. Until entrepreneurs develop those technologies, frankly, HR folks are going to only pay a limited amount of attention to them until they see a product in front of them that they can use and experience. I think we’re seeing lots of it, but you’re not reading a lot of proof statements yet. For example, to say “I reduced turnover three percent because I learned this bit of data from my performance appraisal information that translated into this employee engagement process change; because I would have never known if I hadn’t dug through the big data, right?”

WSR: We have heard it often said people don’t go into HR for the numbers.

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Larry: Let’s be honest, HR departments don’t get the kind of investments in analytics that operational functions do, right? I have a whole operations team that spends nothing but time working and managing and crunching data. Even in companies of our size, HR would be lucky if they had a couple of HRIS or analytics people; whereas, I might have a team of five or six just for sales at Ceridian.

WSR: So how does data security fit into this?

Larry: Obviously, people data is pretty sensitive in a variety of ways. How does data security fit into big data in the combining of very large datasets? This is something that could become unethical or illegal in the future. It’s enormously important, and has really big technology implications.

Good HCM platforms do a great job of allowing dynamic data security. Whether it’s what data I can see about a person or what data I can change or manipulate or report against, or the kinds of documents that I can look at. We hear about these tools that are designed to magically crunch big data and this is what you’re seeing to some extent in the vendor marketplace, an evolution of those tools on top of the single platform, which is certainly our vision.

I mean what if somebody’s playing around with big data and they decide to download that dataset to their laptop and go home, and their laptop gets stolen from their car.

WSR: So, what are some of the uses of big data that Ceridian is working on, or even better, already has?

Larry: We’re driving a bunch of activity around that. It starts at the operational level in how we can help organizations better leverage the way they build their systems. So one of the things we do is we leverage configuration data. Not about our “customers’’ data,

but about how our customers have configured our software.

We look at that across dimensions. We look at it across best practices for how they’ve set up their system. But we also compare it to how others have decided to configure their systems as well. This has nothing to do with the data. It has everything to do with the usage of the system.

For some, there is the vision for this magical benchmarking across many customers in a cloud. I don’t think that that has a ton of long-term legs until we can, with absolute certainty, protect the anonymity of that data.

WSR: My last question then is really what is the future of big data? So you talked about an area where some of the cross-company benchmarking potential may or may not be real based on some of the security issues.

What really do you see is the future of big data from a people data analytic standpoint?

Larry: I think there are tons of opportunities because there are now tools with the ability to crunch those large volumes of data in abstract ways. The more data you collect, the more

interesting it gets. It’s the challenge; how do I determine what the interesting algorithm is?

The technology has to provide ways to enable the analysis. But only people that understand the data and the environment are going to be able to define those algorithms.

We need experts in HR to merge with the technical IT savvy experts and the tools to kind of come together. Once that starts to happen and the return-on-investment starts to get higher, or the cost and the complexity starts to come down, that’s when you’ll see the adoption escalate.

And how do we derive those? Let’s go back to the Google example. They figured out some amazingly interesting ways to index all of this data on the Web so that when you put three words in place, Cleveland South Marriott, it shows the hotel?

They’ve been working on that for 15 years.

The same thing will have to be true for us. It is super early in terms of that for HR, because not only do we have to derive what the algorithm model needs to look like, but it has to be embedded in technology, and then be easy enough to use that people who only get two hours a week to spend on it can use it.

WSR: Thank you for sharing your insights with our readers.

About Larry Dunivan As executive vice president of Sales for North America, Larry Dunivan is focused on revenue growth of the complete Ceridian product portfolio, and aggressively focused on the cloud business and continued success with the award-winning Dayforce HCM. He drives the sales team to maximize opportunities and performance. His straightforward communication approach, commitment, and extensive industry knowledge leads the team to get results.

Dunivan joined Ceridian in 2011 as executive vice president, Products and Technology and was instrumental in the transformation of Ceridian to a cloud-based company. Prior to Ceridian, he served as senior vice president of HCM Products at Lawson (now Infor) and had responsibility for product management and product development for human capital management solutions globally.

He is a recognized HR industry influencer and held a variety of leadership positions with both Lawson and Cyborg Systems (now Accero).

He has undergraduate and graduate degrees from the Northwestern University and the J.L. Kellogg School of Management. He can be reached at larry.dunivan@ceridian.com.

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2015 Compensation/Benefits Buyer’s Guide The 2015 Compensation/Benefits Buyer’s Guide will serve as a valuable reference tool. For your convenience, the guide has two sections: a Categorical Listing and an Alphabetical listing. In the Categorical Listing, companies are listed under the product and service categories of their choice. For information on a specific company and its products and/or service, please refer to the Alphabetical Company Listing.

While a listing in this guide does not constitute an endorsement by IHRIM, it does indicate that these companies are interested in serving the needs of HRIS professionals. We hope this Buyer’s Guide will assist you in your 2015 purchasing decisions.

Product Categories

COMPENSATION

General Ceridian HCM, Inc. Decusoft HCR Software Deferred Compensation Decusoft Executive Compensation Decusoft Incentive Compensation Decusoft HCR Software International Compensation HCR Software Rewards & Recognition CrystalPlus.com

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Paid Advertising

BENEFITS

General Ceridian HCM, Inc. Benefits Administration Ceridian HCM, Inc. COBRA Ceridian HCM, Inc. Flexible Benefits Ceridian HCM, Inc. FMLA Administration Ceridian HCM, Inc. WorkForce Software Retiree/Retirement Ceridian HCM, Inc.

November 2015 • Workforce Solutions Review • www.ihrim.org

2015 Compensation/Benefits Buyer’s Guide

Alphabetical Company Listing* *Systems and applications referred to in this section are trademarked, registered, or in progress. These names should not be used generically.

Ceridian HCM, Inc. 3311 E. Old Shakopee Road Minneapolis, MN 55425 Resource Center 800-729-7655 onesource@ceridian.com www.ceridian.com Ceridian is a global human capital management technology company serving over 25 million users in more than 50 countries. Our offering includes the award winning, cloud-based Dayforce HcM, Global Solutions, Small Business Payroll, and LifeWorks Employee Assistance and Wellness programs. Ceridian Makes Work Life Better www.ceridian.com

Crystal Plus.com 18475 E. Valley Blvd.

City of Industry, CA 91744 Michelle Smith 888-779-8803 888-669-0838 service@crystalplus.com www.crystalplus.com CrystalPlus.com is a leading supplier / manufacturer of crystal awards and corporate gifts. We offer free engraving and no setup charges on all of our crystal awards and gift products. We have in house professional graphic designers, engravers and customer service specialists to serve our customers making ordering crystal awards and gifts easier than ever.

At Factory direct prices and with huge inventory selection at our California warehouse, you can’t find any better prices and faster turnaround for the same premium quality of custom engraved corporate awards, sports trophy and personalized gifts.

Decusoft 70 Hilltop Rd. Ste. 1003 Ramsey, NJ 07446 Karie Johnson 201-258-1414 201-785-0774 karie.johnson@decusoft.com www.decusoft.com You have an HCM software suite but are still using spreadsheets to manage compensation outside of the system. Now what? COMPOSE, our specialized compensation management software solution, helps you eliminate manual processes and easily administer complex variable pay plans, leverage your existing HCM data and provide limitless comp administration flexibility. See ad on inside front cover.

WorkForce Software

38705 Seven Mile Road Livonia, MI 48152 734-542-0635 info@workforcesoftware.com www.workforcesoftware.com WorkForce Software is the leading provider of cloud-based workforce management solutions for large organizations. Through its award-winning EmpCenter suite, WorkForce Software helps organizations automate time and attendance processes, manage employee absence and leave, optimize staff scheduling, gain real-time visibility into labor costs and productivity, and mitigate employee fatigue risks.

HCR Software 13400 Sutton Park Drive South, Suite 1102 Jacksonville, FL Jamie Davis 904-838-5470 sales@hcrsoftware.com www.compensationxl.com CompensationXL is a flexible and affordable compensation planning software which enables effective pay-for-performance strategies. It simultaneously reduces the administrative burden and cycle time while improving process integrity and regulatory compliance. CompensationXL automates merit and variable compensation without requiring changes to your process or retraining of your managers. See ad on page 31.

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Big Data. Big Challenge. Big Chance.

By Michael Grimm and Christine König, Ingentis Big data is on everyone’s lips. The seemingly endless amount of data captured in many of today’s HR systems can be an opportunity for HR to create added value to its function while providing a competitive advantage to the company. But the challenge is to glean what data is important and what is not.

What are big data’s sources?

The first step of every big data analysis is to know the data sources. Where does big data come from? In the field of HR, there are a lot of internal systems, i.e., Human Capital Management, Talent Management, and Recruiting forming the basis of this data. But these are no more the only existing data sources. There is unstructured data that could bring relevant information to HR. Examples could be social media activities of employees, emails, videos, documented HR processes, etc.

Which requirements should HR systems meet?

Dealing with big data can be a big challenge for HR systems. Numerous datasets have to be processed. Huge amounts of data need to be imported in a short time into the system. Furthermore, imported data should be available immediately. Different types of information such as images, figures, or text often have to be evaluated in one analysis. Overall, big data demands a lot from the HR system. Then again, it is also a good chance to get special insights into HR processes and to derive measures for the HR strategy and business operations.

What problems can be solved with big data?

The analysis of relevant HR data has to be chosen carefully. Not all information is useful.

Clear goals have to be defined before starting the analysis. There are principal goals, i.e., being innovative, maximizing profits, strengthening the competitive position, and increasing employee retention. Each company has its primary goals with underlying sub-goals.

So, for example, what can we do to reach a primary goal of being innovative? The underlying HR sub-goal could be making the best use of employee skills, promoting, and retaining talented employees. In this case, an HR analysis

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of talents and competencies within the company can support the principal goal of being innovative. Amounts of data regarding talents and skills have to be evaluated.

Another company goal might be increasing employee retention. The underlying HR sub-goal could be motivating employees. Big data can be useful in extracting information about employees needs for work/life balance, or performance bonus. Through analysis of this data, HR can determine what motivates the employee and aid in retention.

Is there a correlation between analyzed HR data and company goals?

Analysis of big data may result in colorful diagrams and pie charts, but how is this useful?

Human Resources may extract age structure, gender distribution, span of control, vacancy rates, risk of loss, and so on. But these datasets must match the goals of the company, and there must be a correlation. In other words, the visual data has to be in context, which can be proved in a scale analysis.

For example, within the last 10 years the average age of employees within the company has increased. In the same time period, the number of employees has also increased. It won’t make sense to show these as key figures in a correlation diagram. Most probably the increase of average age shouldn’t be seen in context with the increase of the number of employees. To ensure this, the scale analysis can help. If there is no correlation, both figures should be shown separately in a chart.

Creativity has no limits.

Creative combinations of HR data can sometimes result in new ideas and solutions for the HR strategy, as well as business operations. Of course, the combination must match the goals of the company.

For example, almost every company visualizes its organizational structure in an org chart. In this organizational chart data about employees and positions is shown. HR managers and employees can retrieve information such as contact data, job titles, or members of a special team.

Some companies spread the org chart via its intranet; others store it locally on the desktop.

Another common component is HR report-

ing. It provides detailed information about HR key figures of the organization. This could be, as mentioned, the age structure, gender distribution, vacancy rates, risk-of-loss, and much more.

The reports depend on the primary and sub-goals of the company. Here’s a creative combination: Combine the org chart with HR reports? Does it make sense? It does.

Actually, the organizational chart offers an ideal framework for HR key figures. The integration of HR reports into the org chart can give HR managers a new insight into the organization.

Beyond that, the clear presentation of relevant key figures in employee, position or department boxes can be the basis for HR analysis (see Figures 1 and 2). HR managers are supported with structured information to derive strategic and operational HR measures. For instance, visualizing the risk of loss of an employee in the org chart can be the basis for succession analysis.

Showing competencies and talents of employees in the org chart is a good starting point for each talent management strategy. These visualizations can be realized with special HR tools like the Ingentis org.manager. The software is connected to major HR systems, evaluates the data, and visualizes them automatically.

Figure 1.

Figure 2.

Of course, not all key figures should be shown to all employees. Sensitive information should only be accessible for defined persons such as HR managers and/or executives.

Big Data: Best Practice.

About the Authors

An efficient HR data analysis requires time and effort. The following “3 D’s” can help:

Michael Grimm is managing director for International Sales and founder at Ingentis, the German house of HR-add-ons. He has more than 17 years of experience in the field of HR data visualization, HR product management, and sales. Ingentis has established three successful HR-add-ons on the international market, called Ingentis org. manager, Ingentis easy.pes and Ingentis distribution.list. Grimm can be reached at michael.grimm@ingentis.com. For more information about Ingentis, visit www.ingentis.com.

1. Data quality An HR analysis cannot be useful or meaningful without high-quality data. The devil is in the details. Make sure that the data that you use for your analysis is complete, correct, and up-to-date.

2. Data availability Be aware of data availability. The best analysis is worthless, if you don’t have access to it. The huge advantage of a SaaS application is that you can access your data quickly at any time. The data is stored externally on a server and can be retrieved online. You don’t have to worry about maintenance or updates. This is handled by the external service provider. Ensure that access to data is controlled by an authorization system, so that the information is only available to certain individuals.

3. Data security

Christine König has been responsible for Public Relations and Online Marketing at Ingentis since 2013. She graduated with a bachelor’s degree in Business Economics and a master’s degree in Marketing at the German university “Friedrich-Alexander-Universität”. In her bachelor thesis, she delved into customer retention in the sector of motor vehicle insurance. The thesis was awarded by the North-Bavarian insurance institute with the “Forum V-Preis 2010”. Finally, in her master thesis she dealt with the influence of emotions on consumer behavior. She can be reached at christine.koenig@ingentis.com.

Your data has to be safe. HR information about your personnel has to be protected. Be sure that sensitive information, e.g., salaries, absence days, or overtime hours cannot be accessed by everybody.

In this case, an access protection system can be useful. If you have your HR data in the cloud, be sure that it is saved and monitored by the external computer center 24 hours a day.

Reputable providers are certified to ISO and other quality seals.

Qualitative analysis of big data can yield increased value for HR. There is a huge range of data in the HR department. Some datasets are already structured, but there is also a lot of unstructured information from emails, documented HR processes, and much more. That’s why HR data sources have to be located before starting the big data analysis.

Furthermore, primary goals and sub-goals should be identified. What problems need to be solved with the analysis is the key question. It is not the quantity of data that increases the value of an HR analysis, it is the quality. The 3 D’s, as well as a creative combination of HR data can support this goal of quality. Finally, it is in the hands of each HR department how to profit from big data – it’s a big challenge but a big chance to succeed.

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Product Focus

Transforming the Unmeasurable with Authoritative Data: Eliminating Gut Feel from Strategic Workforce Decisions

By Geri Cruz, Kanjoya Kanjoya Perception pushes the frontiers of people analytics by uncovering employee emotions, motivations, and intent from text-based data and predicting what employees will do and why. Created with state-of-the-art natural language processing and machine learning, the solution delivers answers to critical workforce questions like: What topics are employees concerned with? How do we develop a more diverse workplace? How do we proactively retain high performers at risk of leaving?

Building the best workplace is critical for attracting, retaining, and motivating the right people for your organization. To do it effectively, you need color and context on the employee experience and a genuine understanding of how employees think and feel. The data you need is everywhere – in employee surveys, social media, collaboration tools, and a whole host of internal documents and third-party data sources.

The challenge is generating the insights you need. Many businesses rely on the easiest data to analyze – survey questions in ratings, ranking, and yes/no formats. But what you get is a numeric, and often unreliable, representation of employee emotions. If, instead, you delve into the text-based data sources, you will get richer insights. But the process of analyzing text-based data has been manual, expensive, and prone to error, so businesses avoid it. The result: employee emotions, motivations, and intent are unmeasured and unused for strategic workforce decisions.

Kanjoya addresses this challenge with its Perception platform, built on a powerful text analytics engine that surfaces meaning in seconds – and at scale. Our solution embodies our nearly-decadeslong experience with language and the expression of emotions, which began with the launch of our first product, Experience Project, an online social networking site where people anonymously share experiences, knowledge, and emotions.

Through the conversations of the 15 million users each month, we have accumulated a proprietary dataset of how people across different geographies, genders, and other demographic segments express emotions. Our data is a treasure trove for teaching machines to be sensitive to emotions in text and understand communications at the level humans do.

In collaboration with Stanford linguists and computer scientists, we leveraged the data and natural language processing and machine learning techniques to create software that interprets meaning from text. We refined our technology for HR applications to help businesses know what employees are discussing and the emotions they express. Armed with this information, businesses can design and build the best employee experience: • Themes and Topics: Today, Perception

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identifies over 70 HR-specific workplace and performance concepts. For example, we can determine whether a person is discussing benefits and compensation, leadership, or teamwork — without the person explicitly using the terms “compensation,” “leadership,” or “teamwork.” Similarly, we can detect if someone is discussing performance-related concepts, such as “strategy” or “communication skills.”

• Emotions and Sentiment: Kanjoya discerns over 100 different emotions and can detect multiple or conflicting emotions in a single expression or thought.

Figure 1. A conceptual view of how Kanjoya Perception evaluates and identifies themes and emotions in text.

Why is this important? By knowing what employees are discussing and how they feel, you can develop more targeted and effective action plans.

After all, the action plans created to address people who are confused about a new benefits program will be dramatically different from the ones designed to address those who are disappointed or angry.

With Perception, an organization’s ability to seize opportunities and mitigate risks increases dramatically. The next sections highlight the types of challenges Kanjoya customers address with Perception.

Workforce Challenge #1: What are people concerned with?

Surveys give business and HR leaders a pulse on what is top of mind to employees. Informal,

open-ended questions, such as “What’s going on?” allow employees to discuss what’s most important to them. What they discuss can reveal a lot about their concerns, emotions, and engagement.

Figure 2 shows how Perception would analyze open-ended questions. In this sample set of employee responses, the top themes are diversity, teams, and management, all of which elicit positive responses. But employees are unhappy about benefits/compensation, the 5th most important theme discussed. To dig deeper, you could click on any of the terms surfaced to read verbatim responses regarding the low base salary and equity.

The insights suggest that a reasonable next step for the company is to compare its salaries against industry averages and confirm whether employee perception matches reality.

Figure 2. Analysis of employee survey for top employee values.

Workforce Challenge #2: How do we develop a more diverse workforce?

Kanjoya Perception quantifies differences in the career trajectories of employees across different demographic groups and guides business leaders in understanding why.

For example, to remedy differences in promotion rates between men and women in the same departments and roles, a company can use Perception to analyze whether the evaluation process is equitable across demographic groups. Figure 3 shows the analysis of performance reviews.

At this representative company, men tend to be evaluated on leadership, analytical skills, and drive – and they do well against these criteria.

In contrast, women tend to be evaluated against teamwork, communications skills, and organization. The evidence suggests that unconscious bias exists, and one critical step in promoting uniformity across all performance reviews may be the use standard criteria for all people in the same roles and departments.

Traits against which men are evaluated

Traits against which women are evaluated

Figure 3. Comparison of the performance traits against which men and women are evaluated on.

Workforce Challenge #3: How do you proactively retain high performers at risk of leaving?

About the Author Geri Cruz is VP of Marketing at Kanjoya, leading all demand generation, product marketing, and branding initiatives. In her 15 years of experience, she has held marketing and sales strategy roles at LexisNexis, Gartner, and Bill.com. She is a noted speaker and published author. She can be reached at geri@kanjoya.com.

How do you keep high performers engaged and happy to stay with your company? By integrating performance review data with employee surveys through Perception, as shown in Figure 4, you can drill down into the engagement and intent-to-stay scores, and identify issues that could drive the best employees to leave.

In the example shown, we examine what employees value, and filtered the responses of high performers with a low intent to stay: they value efficient processes, alignment with the company vision, and innovation – but are disappointed with all of them! There seems to be a lack of communication about changes, lack of agreement with the company direction, and dismay at a lack of product innovation. Many things need to change to prevent the departure of valuable employees. Communication and transparency top the list.

Figure 4. Integration of Performance and Engagement Data: What High Performers with Low Intent to Stay Value.

Concluding Thoughts Data to help you build the best future for your organization is everywhere. Kanjoya synthesizes the data by navigating the complexities of language and human emotion. We take you where you’ve never gone — inside the heads and hearts of your employees. With Perception, you can delve into employee attitudes, motivation, and intent; predict their behavior, and make the best decisions. For more information, visit our website at www.kanjoya.com. www.ihrim.org • Workforce Solutions Review • November 2015

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Aggregators and Analytics: Harnessing the Cloud

By Rana Hobbs, Vestrics There has been a long-standing challenge facing HR analytic teams – how to harness the inherent power of workforce data. The specific data issues tackled by leading organizations have not changed over the years, they have only expanded. What has occurred is a seismic shift from on-premise systems to SaaS platforms housing data in the cloud, or in a jumbled mix between the two. Being able to successfully align processes, ensure data quality, and aggregate sources still remains the difference between those organizations with embedded analytics leading in their respective industries, and those that keep waiting at the starting line.

Data, more than ever, is everywhere. Big data is a new label for a long-standing landscape. We do not lack for data and we rarely ever have. But the current analytic approach to big data is to blindly data mine in hopes of finding some pattern or trend, which could explain existing business results. From structured to unstructured data, the hope is to glean insight into what is happening within our workforce. What is missing is the expertise and precision of optimization in workforce investments. Advanced analytics and algorithms seem disproportionately focused on predicting a candidate’s performance or in identifying flight risk employees. The fact that those two aspects of the employee life cycle are where so much energy is going is troubling. It seems to remove the onus off of the employer in providing proactive career development and investment, and rewards employees for negative behavior rather than creating a culture of performance. For example, say you use email sentiment and network reach as key inputs into your flight risk algorithm. It won’t take long for employees to see that those who are disgruntled will be given unfair attention and opportunities in a hope to retain them. With the acquisition of VoloMetrix by Microsoft, we see analysis at the incremental level by measuring communications, meetings, and the depth and breadth of networks. It is an upfront monitoring of our daily activities to see where

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there is wasted time and space, and atrophy in employee involvement in colleagues. It is the aggregation of very operational activity.

Aggregating to break down siloed walls, be it on-premise or cloud software, remains the critical gate to pass before any other advanced analytic activities can happen. Initially, the data aggregation topic started to be addressed by integrated talent management vendors. As they combined data from across the functions, analysis of the workforce became far more rich and robust. However, it always feels like data creation is one step ahead. We in the software space are constantly striving to bridge that divide by providing more dynamic and agile platforms in which to combine and analyze the growing datasets. Our ultimate goal is to always better define and answer the workforce questions that are impacting our business.

Analytics is a varied practice encompassing everything from descriptive dashboards to predictive and prescriptive statistical modelling.

While there is a place and need for operational reporting, the argument to undertake data aggregation is so that efforts and investments may be optimized in a proactive manner, and not just about getting rosters and segmentation attributes on your workforce. Understand the question you are trying to answer and choose the right tool for the job. There are dashboards, there are insights, and there are investigations.

The aggregators, like OneModel, can port aggregated and transformed data out into another vendor’s platform for analysis. And then there are the analyzers, like Vestrics, whose predictive analytics for workforce optimization uses the aggregated and transformed data like high octane fuel to unleash statistical modelling previously unavailable to the business on the workforce and investments.

Integrated talent management vendors have promoted an integrated view of the workforce, but the outputs have been operationally focused with little advancement beyond basic correlations. Leading organizations are taking that integrated data and pairing it with powerful analytic tools and platforms to uncover cau-

sality and calculate ROI. Truly knowing not just Who but What Could, and most importantly, What Should happen.

Data mining vs. programmatic analysis for optimization are far ranging activities that draw from the same aggregate data source and require a centralized governance and oversight best served by a Center of Expertise (COE) and larger Network of Expertise. COE effectiveness is driven directly by its ability to provide: • Prescriptive models for workforce investment optimization; • Analytics-driven corporate strategies; • Interventions designed for key workforce segments; • Standards in reporting and analytics; • Accountability across the organization for incorporating quantitative insight into decisions; and, • Expertise developed and distributed internally.

A COE is the aggregation of people and skills to match and leverage the aggregation of data.

Whether it is a centralized, decentralized, or a hybrid approach to the team, the varied skills and perspectives allow specific business analytic needs to be met while still maintaining a cohesiveness and alignment to larger strategic initiatives. By leveraging cloud-base solutions, analytic teams are no longer bound by slow, expensive, and quickly outdated or non-scalable solutions from internal IT and consulting firms.

This is a critical component and value proposition of cloud-based solutions. As the pace of business accelerates, so do the enabling tools to support it. The scalability, speed, and agility of cloud-based software allow equally more efficient aggregation of the data into the cloud, as well. As mergers and acquisitions continue and systems are inherited or discontinued, there is no other way to manage the ever-changing data landscape of an organization without taking advantage of the cloud.

It is also about integrating the business processes, not just the data systems. Aggregation needs structure and alignment and definitions.

Merely dumping vast amounts of data systems into a cloud-based warehouse only brings you incrementally closer toward its inherent value.

That is why so many organizations are undertaking large HR transformation initiatives so that there is a collective and connected view on the functions, the data they produce, and the definitions in how that data is applied to the business.

Data aggregation and cloud software is the culmination of the pace of business and its need to untether from the confines of on-premise in the same way they have freed the shackles on a remote workforce. We are doing business more and more with mobile teams and colleagues distributed globally. They only way to truly match the needs of this new workforce is to harness cloud software that not only allows for ease of accessibility, but also ease of aggregation across disparate platforms. The cloud has matured to a point where concerns of security or scalability have more than been addressed. Companies like SAP and Oracle are moving more and more of their business into their cloud software offerings. Those with on-premise solutions will still have a place, but with limited supported and dated features. The only way to realize big data as something more than big data mining is to connect to cloud platforms and to leverage statistical predictive optimization software. That is the power of cloud aggregation The challenge is understanding that aggregating and analyzing are two different disciplines. The aggregation in the cloud allows for a scalable analytic practice without one-off data extractions and transformations.

About the Author The cloud and the software are Rana Hobbs is a recognized industry expert with more than 15 housed as the enabler for the work years of experience helping we do. It allows us to run faster leading companies across the and more efficiently so as to realloglobe design, implement, and cate our precious resources toward execute successful workforce analytics and planning programs. As the vice president of the highest value-add work. The Customer Success at Vestrics, she is responsible old adage, “that which is measured for delivering value across the entire life cycle for gets managed,” is true once again, clients of Vestrics’ Vision platform. Her focus is on so be mindful of only applying business strategy and human capital alignment, SaaS technology and implementation, Center of analytics toward predicting a candidate’s performance before hiring, Expertise design, and change management.

Hobbs previously held similar positions with and in allowing an employee to SuccessFactors, Infohrm, HumanConcepts, become a flight risk before you inAasonn, and the Corporate Leadership Council.

She is also an in-demand speaker at HR.com, vest in him/her. Think in terms of IHRIM, SHRM, IQPC, SAP, and other industry optimizing workforce investments events. She can be reached at that align and invoke your strategic rhobbs@vestrics.com. priorities proactively. www.ihrim.org • Workforce Solutions Review • November 2015

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Dear HR: Stop Hiring Data Scientists until You’re ready for Data Science

By Greta Roberts, Talent Analytics Corp.

I had yet another call today with a brilliant data scientist working inside of a Human Resources department of a major business.

This HR data scientist has both a strong analytics and predictive analytics background.

She has a Bachelor’s Degree in Statistics and a Master’s Degree in Predictive Analytics.

She excels in R, math, predictive modeling, machine learning, and all things quantitative.

She is also excited about applying data science from other domains, to solve interesting workforce optimization challenges.

She applied for a quantitative HR role that promised to let her use her skills and interest in solving difficult employee-based challenges. She was hired for this role. What’s the problem you ask? HR won’t let her do data science.

Over and over again she has suggested a data science approach to help solve employee focused challenges that have plagued the organization for years, and cost many millions to the organization’s bottom line. Over and over again she is denied the ability to move forward.

Her comment is that HR seems to be scared or hesitant in moving forward to a new way of solving solutions. The real concern is that the “reason” was not fully discussed so she could learn.

Instead, she is asked to work on generating monthly or weekly reports that the organization has grown addicted to. When she is allowed to solve an interesting problem using analytics, and brilliantly does so, the executive HR leadership won’t give it executive visibility or implement it in production. Results are found “interesting” but not deployed.

Then, she’s back to generating reports.

She isn’t alone. And, this article isn’t about one unique HR data scientist. Not by a

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long shot. I hear this all the time. As a result, I also see brilliant HR data scientists jumping from one company to another. I can see it on LinkedIn updates as brilliant HR data scientists move from one company to the other. I hear it in the conversations I have with them about why they left and their angst before they leave.

What is my plea to HR (and any other department hiring a data scientist)? Stop hiring real data scientists until you’re ready to do real data science.

I think I understand some of the problem.

Perhaps the pressure on HR to begin using an analytical approach has led them to hire data scientists, but when it comes to actually using this approach it’s too foreign, or scary, or “not what we’ve done before.” HR needs to learn from these brilliant people they’re bringing into their domain or stop hiring them to begin with. In the words of the data scientist I recently spoke with: “Anyone can hire a data scientist. Not every HR department or organization is ready for data science. Generating reports are not analytics – even if they’re prettier or faster reports. Dashboards are not analytics even if they’re really pretty dashboards. More than any department, HR should understand the devastating impact of changing a job description on someone that’s been hired.”

Ironically, the data scientist hire is perhaps one of the most brilliant and strategic hires your HR department has ever made – perhaps ever. But only if they let her do what she was hired to do. HR data scientists can help move HR from being tactical to strategic, using an analytics approach to highlight never seen before patterns, make decisions based on data, and the like.

Tips on letting that brilliant HR data scientist you hired be one of your most valuable hires:

introduce HR’s new expertise to solving business challenges that affect the bottom line.

1. Assign reporting to someone else. It’s a very important task, but it doesn’t require a data scientist. Reporting will quickly bore them to tears and they’ll resign.

5. When they complete an analytics project, give them a chance to talk and present the results, regardless of the outcomes. Did it help or not help?

Don’t keep the results inside of HR.

2. Don’t block them from talking directly to your business areas. (I often hear they have to go through the HR business partner who protects the business leader and blocks them from access). Working with the HR business partner makes sense. Being blocked by the HR business partner doesn’t.

6. Admit that you’re a little nervous about what they do. They’re nervous about what you do, too.

3. Task HR business partners with finding either high turnover roles or low performance roles that your data scientist can work to help with. These are great projects for your data scientist.

4. Have them focus first on solving business challenges (like financial advisor turnover) not HR challenges like Compliance issues. This will give visibility to the great work they do and

7. Trust your data scientist. Stop being scared. You hired them because they have an area of expertise traditional HR doesn’t. Embrace their area of expertise. You need to trust their advice and approach, or yes, they’ll leave.

And mostly, don’t hire a data scientist if you’re not ready for data science. If you thought you were and you find out later you really aren’t, let them know and let them go.

Be honest. Don’t put them in a different role and block them as they keep trying to be successful.

About the Author Imagine a compensation planning process without

Greta Roberts is the CEO and co-founder of Talent Analytics, Corp. Follow her on twitter @gretaroberts.

✓ Copying & Pas-ng ✓ Emailing Workbooks ✓ Audi-ng Recommenda-ons ✓ Working Nights & Weekends ✓ Mail Merge Salary Statements

The Flexibility of Excel™ + The Power of Cloud Compu:ng www.compensa-onxl.com

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Making Your Votes Count: Creating a Game Plan for Strategic Workforce Analytics in HR

By Mick Collins, SuccessFactors, an SAP company On September 9, 1969, President Richard Nixon delivered a eulogy at the funeral of former Senate Minority Leader Everett Dirksen. In the text, written by White House speechwriter William Safire, Nixon used several statements to define political skill of the highest caliber, stating that “a politician knows not only how to count votes, but how to make his vote count.”

According to Safire/Nixon’s rhetoric, it is not sufficient merely to take a roll call of those in favor or against a particular bill, but to use judgment, influence, and skilled oratory to translate individual actions into aggregate political capital for the purpose of achieving grander outcomes.

The modern HR leader aspires to leverage big data (“counting votes”) to optimize investments in talent (“making votes count”). The ability to mine structured and unstructured people data, combined with information about customers, markets, products, and financials, is a huge competitive advantage for those companies that are equipped to apply these insights to major business decisions: how to generate revenue, minimize expenses, mitigate risks, and execute strategic plans.

The urgency for such data stems from one (or both) of two sources: reactive pressure from leaders outside HR for the function to bring data with a level of rigor similar to that provided by its corporate peers, or a proactive opportunity for HR to take the initiative and bring analytics to executive attention.

Without big data, HR risks “flying blind” when making investments in talent programs. Imagine that your HR leadership team is faced with a decision to increase base salaries by 10 percent for 500 employees that possess a unique and highly desirable skillset, in the hope of boosting retention rates. Lacking analysis of data on individual productivity, likelihood of retention, engagement/ motivation, the mix of incentive versus base pay as components of total rewards, and other factors, your organization could be saddled with a massive expense that yields little in return.

Yet, the reality is that HR often defaults back

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to “counting votes” – amalgamating transactional data of past performance into spreadsheets for the purpose of reporting – which limits the capacity, let alone the capability, for staff to translate raw data into behavior-changing insights. HR metrics, such as termination rates, offered in isolation of other datasets, have limited applicability to business outcomes and, consequently, are of limited interest to the C-Suite.

Contrast that low-value approach with Walmart’s experience of using big data to effect manager action. Profiled in the aptly-named 2014 book, “Big Data,” authors Vicktor MayerSchonberger and Kenneth Cukier recount how Walmart, starting in 2004, recorded every purchase by every customer for future analysis and that, based on data mining, the company found a strong correlation between reports of an impending hurricane and a significant increase in sales of Strawberry Pop-Tarts in affected areas. Of course, data-driven insights count for little if they don’t influence action. Armed with numbers, Walmart instructed store managers to stock Pop-Tarts near the entrance of stores in towns about to be hit by bad weather.

While stocking Pop-Tarts is a much different endeavor than managing people (when was the last time you heard a Pop-Tart ask for a new job?), HR can learn much from the Walmart example.

For example, analytics seeks to find meaning in the data, e.g., does an improvement in quality of hire correlate to better productivity, at least for some groups, rather than build a perfect dataset and prove causality? That’s not to say that data quality isn’t important; rather, that given the subjectivity and incompleteness of workforce data, HR should educate non-HR on confidence levels and realistic conclusions.

Similarly, HR needs to close the gap between “intent” and “action,” moving beyond conclusions to driving manager behavior change. The holy grail of analytics is to use data to make a better decision. How many HR functions can honestly say “Yes, we changed a policy or program for the

better, based on the analytics we had?”

So what does HR need to do next? Over the last five years, I have partnered with numerous HR organizations to go through “rollout planning” – creating a game-plan for how the function will build their capability for analytics. The following visual includes the elements I discuss with HR executives:

I’ll concentrate here on the first three –Mission, Stakeholders/Champions, and SWOT Analysis – and suggest some ideas for consideration in your own organization.

1. Mission: Determine what “workforce analytics” means for your organization and HR function.

Remember the phrase “If you don’t know where you’re going, any road will take you there?” It could also mean that you don’t even begin your analytics journey. This is often where HR gets stuck – not being sure of the destination makes it difficult to decide the appropriate next steps on the roadmap. That’s where a workforce analytics mission statement can help, especially if analytics is unchartered territory. Clearly outlining the rationale for why HR is deploying workforce analytics has two principal benefits: a. It communicates what workforce analytics “is” and “is not” – setting expectations about how the use of data fits into talent management decision-making.

hypotheses that he wanted to test with data, one example being that “retirees are our biggest net promoters” – newly-retired staff that had a positive work experience would be very willing to share that opinion with others. Outlining a set of testable statements meant that the function’s limited resources could be applied to finding answers to a finite set of well-defined questions.

Customer Example: “The company is investing in analytics as a way to more effectively assess needs and opportunities that should be addressed to further differentiate our talent from that of our competitors – in the immediate, near, and longer term. This insight will help drive focused investment in our workforce where it will drive the greatest return for the enterprise. Expected results of workforce analytics include supporting the ongoing differentiation of the company through its workforce by bringing visibility of workforce issues and opportunities, in order to drive increased workforce effectiveness in the near term, and the right workforce composition in the near and longer terms.”

As with the vision, communicating a clear set of realistic priorities limits the initial risk of project over-reach or misaligned resources.

2. Building a Network Map of Stakeholders and Champions

When developing the roll-out plan, it is important to keep in mind certain roles and responsibilities that will affect the planning and implementation process. Stakeholders have formal accountability for the success of workforce analytics. Champions are informal supporters of the program and can broadcast success stories.

Customer Example: A sample stakeholder profile:

b. It streamlines the future deployment of scarce resources, prioritizing those activities and investments which align best with the mission.

The statement can also include two-to-three analytics “wins” that are achievable within the next 12 months. During a workshop on talent management KPIs, one company’s head of HR shared five

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References

William Safire, “Lend Me Your Ears: Great Speeches in History,” W. W. Norton, 1997.

Corporate Executive Board, Bridging the CHRO/ CFO Divide: Four Areas Where CFOs and CHROs Disagree, www.executiveboard.com/blogs/ bridging-the-chrocfo-divide-four-areas-wherecfos-and-chros-disagree/?business_line=finance, September 10, 2014.

In many cases, the leading stakeholder will be the chief financial officer, a relationship known to be adversarial when reporting on head count data.

In the spirit of collaboration, research from the Corporate Executive Board suggests that the partnership will be strengthened by:

1. HR improving its knowledge of quantitative metrics: CFOs expect CHROs to have the expertise to engage in financial analyses of talent decisions and strengthen the financial acumen of the HR team. On the other hand, Finance must understand the limitations of “communication by metrics.”

2. HR taking action to closely monitor and report details on head count and expenses: CHROs should work with the line to set realistic expectations about current and future head count, updating, and communicating workforce needs to Finance to allow for disciplined decision making.

The demands placed on CFOs to understand how workforce strategies and policies impact both the top and bottom lines of their organization require much better communication between the two functions.

3. Conduct a SWOT Analysis of Current Practices for Delivering Data to EndUsers: Consideration of your function’s strengths, weaknesses, opportunities, and threats for workforce analytics facilitates dis-

cussion of quick wins and helps to prioritize resources towards problem areas

Questions to consider include:

• Strengths: What organizational or functional assets/resources do you possess that increase the likelihood of success in workforce analytics?

• Weaknesses: What are your current deficiencies/limitations on the deployment of analytics?

• Opportunities: In the next 6-12 months, how can analytics help your organization better achieve its goals?

• Threats: In the next 6-12 months, what might hinder the attainment of those goals?

For example, one of the major threats to success in HR’s big data analytics is key person dependency – anchoring the effort to a single individual whose future career path is unknown and may exit the organization at any point in time. Evolving from a loosely-structured and under-resourced skunkworks project to a sustainable competency across HR requires that leaders establish an appropriate structure today, a Center of Excellence being most common, and anticipate future staffing requirements.

In summary, HR must embrace the opportunity for big data analytics, but do so in a manner that anticipates the long-term success of investments in building the capacity and capability for data analysis. Establishing a game-plan can help jump-start a fledgling project and ensure a smooth transition to a core competency for HR.

About the Author Mick Collins is vice president Workforce Analytics & Planning at SuccessFactors, an SAP company. He is currently the head of a global Sales Acceleration team responsible for supporting the SuccessFactors Workforce Analytics & Planning product.

In this capacity, he divides his time between product strategy & marketing, sales enablement, prospect & customer engagement, alliances, and thought leadership. With 10+ years of experience in analytics & planning, Collins has delivered hundreds of presentations and workshops on how organizations can build their capabilities for data-driven decision-making in HR. Previously, he was vice president of Marketing, Infohrm; senior consultant, CLC Metrics; sssociate director, Corporate Strategy Board; and senior manager, Corporate Leadership Council. His recent articles include “Executive Interview with Mick Collins & Steven Hunt” (IHRIM Workforce Solutions Review, 2012); “Workforce Data in the Boardroom” (IHRIM Workforce Solutions Review, 2011); and “Beg, Borrow, or Steal” (Talent Management, 2010). He can be reached at mick.collins01@sap.com.

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The Back Story

Ready or Not? Is HR Ready for Analytics?

By Katherine Jones, Ph.D., Bersin by Deloitte

Big data, small data, medium-sized data—your initiatives around talent analytics may well prove a challenge, no matter what size your corporate datasets are. The series of potential obstacles – or at least issues—may seem formidable, but with planning they can be managed. But here I pose a question for you – are you ready? Our 2013 research revealed that one in five HR organizations surveyed had invested in additional analytics software.1 Yet in the 2015 Global Human Capital Trends report, only 22 percent of respondent organizations said that they are “ready” to leverage talent analytics.2 In fact, it was the area within human capital management where respondents felt the very least prepared (see Figure 1).

Figure 2. Little Year-over-Year Change in Analytical Use by HRSource: Global Human Capital Trends Study 2015, Deloitte, 2015.

Percentages deviated little from year to year, with the only rise a one percent increase in respondents indicating that they used HR operational reporting and scorecards, and the other areas actually decreasing from 2014 to 2015.

Despite the Best Intentions…

Figure 1. Are We Ready to Tackle Talent Analytics? Area: SelfAssessed Degree of Readiness Source: Global Human Capital Trends Study 2015, Deloitte, 2015.

Perceived readiness, however low-scoring, is only one part of the equation. Really being “ready” to manage a talent analytics initiative implies that the skills set or capability to ask the right questions, gather the data, synthesize and analyze the data, and then do something intelligent with the results is available. This led to another interesting survey question: Is HR developing and using its analytics skillset? The response was negligible, as shown in Figure 2.

Despite the lip service given to analytics by many HR professionals, the reality appears to be a bit more dismal. And despite the widespread support for analytic tools in products today, they are generally not being used—beyond the slight increase in reporting, which is often spreadsheet based. Today’s software solutions are growing in sophistication; many tools that predict outcomes in employee retention and performance are now available – but the data indicates that their adoption may be slow.

Perhaps human capital managers are not seeing the need to become data-driven. Conflicting interests often result in people doing what is most important at the time; given that, there may be other HR concerns that are seen as more critical to the business than mastery of analytics or conquering big data. Indeed, as seen in Figure 3, this seems to be the case. Here we show the percentages of respondents who rated each trend as important or very important.

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Figure 3. What is Important to HR and Business Professionals? Source: Global Human Capital Trends Study 2015, Deloitte.

HR and people analytics is about threequarters down the list of important trends as perceived by survey respondents ― and the data issues commonly referred to as big data are dead last, with only 14 percent of respondents indicating it is “very important.”

Endnotes

1H igh-Impact Talent Analytics: Building a World-Class HR Measurement and Analytics Function, Bersin by Deloitte / Josh Bersin, Karen O’Leonard, and Wendy Wang-Audia, October 2013.

2 Global Human Capital Trends 2015: Leading in the New World of Work. Deloitte University Press. 2015.

3 Op. Cit.

About the Author

Dr. Katherine Jones is a vice president, focusing on human capital management (HCM) technology research, at Bersin by Deloitte, Deloitte Consulting LLP. She analyzes the underlying technologies and services that support the management of a global workforce, including HR, hiring and performance management and workforce planning.

Jones is a veteran in enterprise workforce and talent management applications and a recognized expert in cloud computing.

Prior to joining Bersin by Deloitte, she was a research director at the Aberdeen Group for eight years where she established Aberdeen’s HCM practice, focusing on research and consulting services in HR, talent acquisition, workforce management, ERP and mid-market companies. Later, she was the director of Marketing for NetSuite Inc., a cloud-based ERP company. She has written on many areas of talent management, technology and business practices. With over 300 works published to date, she is also a frequent speaker in the U.S. and abroad. Prior to a high-technology career, Jones was a university dean, involved in academic administration, research, and teaching. She has a master’s degree and a doctorate from Cornell University. She can be reached at kathjones@deloitte.com.

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Technology is not the Issue Over the past several years, HR and talent applications and standalone analytics solutions have proliferated. Rare is there a product that does not at least advertise analytic prowess. Often processed “in the cloud,” analytics in the form of graphs, charts, and banners come raining down on the dashboards of today’s HR practitioners. Solutions include reporting tools which, while not moving the needle toward mastery of big data, may make standard reporting easier for end users. Reporting tools are generally included within HRIS applications, and increasingly within the range of talent management applications – from recruiting to succession management—that are used in businesses today. Standalone analytics packages vary in de-

gree of sophistication required of the user; some present analytics that are beyond the skillsets of the end users to understand or meaningfully apply.

Predictive and prescriptive analytics do raise the bar. But again, the users have to be ready to make use of the predictions and prescriptions. For these more technicallysophisticated products, an understanding of the algorithms that led to the prediction is an imperative, as well as a strategy as to the response to the prediction. If software predicts an employee will leave, is it a good or a bad thing? Only when the organization has a methodology for reading the results in the light of the individual employee and has a playbook to guide managerial responses to the information, will companies likely be ready to embrace a raft of predictive tools. Prescriptive analytics may be even further in the future; even if the prescription is right on target; the “human” side of HR professionals may prefer people-made propositions rather than algorithmicallyderived ones.

The Dilemma of Data Often organization data is quite a mess: like a sticky child eating taffy applies, your data will require cleansing. Here a washcloth is to no avail. Issues such as errors in data entry, duplication of items or fields, and mis-matched data models all emerge, to name only a very few. The data cleansing process can be lengthy and potentially costladen, if outsourced.

Here is where the age-old adage “garbage in, garbage out” likely originated.

The best tools in the world cannot provide great actionable analytics without accurate input. The most actionable of analytics will also be of no avail if the HR team for whom they are intended is unclear how they are to respond or act on them. And finally—given HR practitioners’ priorities as reported in the Global Human Capital Trends Report 2015,3 there may be many issues of greater concern in their organizations today than mastering big data.

There are many challenges of implementing analytics in organizations. We know because we work with those very same challenges. But we also know there is great value in learning from people in the field that are actually creating value from their workforce analytics efforts.

Join IHRIM and HR tech industry leaders March 1-3, 2016, to explore real-world case studies in workforce analytics. You will have lots of opportunities for networking with your peers to learn from their challenges and successes to see where innovation is happening. In addition, you will learn from practitioners in the field at the educational sessions as opposed to hearing sales pitches from vendor-driven presentations.

TOPICS

WHAT TO BRING

• Building & Leading an Analytics Team

• Your business cards Make important connections at our networking receptions, conversation breaks and interactive sessions.

• Preparing your HR Business Partners for Analytics • Business Cases Solved: Four Industry Cases • Data Visualization Workshop • Solving Data Challenges

WHO SHOULD ATTEND? • CHRO’s & their HR teams • HR analysts • HR systems & analytic students

• Your questions Tackle your real-world problems with peers and our experts.

• Your devices Expect experiential learning and extended chat online.

• Your team Make the Forum your team retreat to build skill and design your analytics strategy.

SPONSORSHIP OPPORTUNITIES

LOCATION

Every year IHRIM implements ideas suggested by our sponsors. As the industry changes from year to year, the IHRIM Workforce Analytics Forum will help you get ahead of your competitors. See our sponsorship opportunities that are available – we have a price point that meets your needs!

Doubletree by Hilton – Chicago Magnificent Mile 300 E. Ohio Street Chicago, IL 60611 Phone: 312-787-6100 Reservations: 800-222-8733 From the warm and delicious DoubleTree cookie upon arrival, to the authentic Chicago hospitality and contemporary design, this downtown Chicago hotel will leave you with an unforgettable experience.

Join us in the Windy City with your HR teams to understand the types of business problems analytics could help you solve at your organization.

IHRIM’s Human Resource Information Professional (HRIP) Certification Program will help you define, establish and distinguish yourself professionally.

APPROVED

Passing the exam indicates a demonstrated comprehensive understanding and proficiency of the defined body of knowledge in HR information management.

EDUCATION PROVIDER

Intended to recognize individuals who have comprehensive HR technology knowledge, the HRIP credential is an excellent tool to assist in your career progression by:

• Demonstrating your expertise.

• Building your credibility.

• Distinguishing you as an industry leader.

• Expanding your knowledge through recertification.

IHRIM offers a variety of products to help you prepare for the examination. Please visit www.ihrim.org and click on the Certification menu for more information about IHRIM’s HRIP Certification Program or contact us ihrimhrip@ihrim.org.

International Association for Human Resource Information Management

International Association for Human Resource Information Management