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A HARVARD BUS I N E S S R E V I E W A N A LY TI C SERVICES REPO RT

THE ENTERPRISE LACKS A BIG DATA STRATEGY FOR IoT TRANSFORMATION

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Many of the Internet of Things (IoT) solutions in place today are ad hoc, disjointed point solutions. However, things are moving fast, and expectations are high. This research reports that 44 percent of companies aim to use IoT to transform their business model, which is in line with our experience in the field.

TONY TURNI GLOBAL VICE PRESIDENT, PROFESSIONAL SERVICES VERIZON ENTERPRISE SOLUTIONS

IoT is simultaneously revolutionary and evolutionary. Its origins lie in the fleet management solutions of 20+ years ago, but it is in the past couple of years that the volume and variety of solutions have mushroomed. That is partly why companies are at such different stages of exploitation and integration. Some companies are already seeing significant tangible benefits of IoT, while many others are just exploring and trying to figure out what it means to them.

Achieving the full potential of IoT is not just about implementing solutions like energy management and condition-based monitoring. While each of these can be very valuable technologies in its own right, it’s when the data from IoT solutions is fully integrated into an enterprise big data strategy that the biggest benefits can be achieved and advances can be made. Knowing where your vehicles are in near-real time is great for efficiency, but imagine if data from your production line could directly feed the automatic creation of routing for your vehicles. And that routing could be updated minute by minute based on traffic and weather information. And you could monitor the condition—temperature, humidity, vibration, etc.—of the goods to maintain quality and security. But that’s only the start.

44% aim to use IoT to transform

When IoT, big data, and machine learning technologies are combined, truly magical things can happen. That’s when instead of just doing stuff better, faster, and often more cheaply, genuinely surprising insight emerges. Connections that we’d never have imagined—like the correlation between whether somebody fills out a form in CAPS and the likelihood of defaulting—can drive new ways to increase efficiency, improve customer service, and create competitive advantage.

is preventing them from acting on more big data.

their business model.

78% are acting on only a limited amount of IoT data—or aren’t using any at all.

42% say lack of skills/capabilities 51% are struggling with big data variety and complexity.

78% say that new networking

technologies are important to their big data strategies.

That’s the good news. Getting to this ideal state is going to require companies to totally rethink how they store and share information. It’s going to demand networking, security, and analytics skills far beyond anything that most companies have today. And it’s going to require guidance on how to break down organizational silos and become more data-led. Few organizations can do all of this alone, but I hope that after reading this report you’ll be convinced that it’s something you should be doing. Now.

THE ENTERPRISE LACKS A BIG DATA STRATEGY FOR IoT TRANSFORMATION Internet of Things (IoT) technology holds incredible promise for transforming the enterprise, but one of the biggest hurdles for companies implementing IoT will be extracting insight from the incredible volumes of fast-moving data these systems produce and integrating that resulting intelligence into business processes in real time. An organization’s ability to manage big data—the level at which they’ve invested in and have implemented big data architectures and analytics—is critically important to the realization of IoT value. “The ecosystem that you build to process big data is the same kind of ecosystem that you need when you bring IoT on board,” explains Ashish Verma, managing director, analytics and information management with Deloitte Consulting. However, most companies are taking a largely ad hoc approach to big data today, according to a September 2016 survey of 306 business leaders conducted by Harvard Business Review Analytic Services. Nearly half of respondents said they pursue big data initiatives on a per-project basis, with just 18 percent saying they have an enterprise big data strategy and approach. figure 1

FIGURE 1

DEVELOPING AN IoT STRATEGY Percentage of respondents indicating which statement describes their organization’s current approach to big data and analytics (including IoT). 47

We are pursuing big data initiatives on a per-project basis.

We are developing an enterprise big data strategy and approach.

We have an enterprise big data strategy approach. SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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Most companies don’t have the skill sets to put in place the technical infrastructure required to ingest, store, and analyze the volume of often real-time data presented by IoT. ANDY DAECHER, PRINCIPAL, DELOITTE CONSULTING

It’s not surprising then that most companies are taking a makeshift approach to their early IoT initiatives. Six out of 10 respondents said they have pursued some IoT point solutions or proofs of concept but have yet to develop an IoT strategy. figure 2 “Most companies don’t have the skill sets to put in place the technical infrastructure required to ingest, store, and analyze the volume of often real-time data presented by IoT,” says Andy Daecher, principal with Deloitte Consulting and leader of the firm’s cross-industry IoT practice. Those that have that infrastructure in place may not have the data scientists on staff to make use of it. Many don’t have the master data they need to generate meaningful insights from IoT. Others have yet to build a business case around the IoT. And many companies don’t know where or how to start. “The result is that these IoT efforts are conducted like science projects with no clear intention,” Daecher says.

FIGURE 2

IoT IS STILL IN EARLY STAGES Percentage of respondents indicating how they would describe their organization’s current IoT maturity level. 32

We have some ad hoc point solutions in place but little to no integration or enterprise strategy.

We have done some IoT proofs of concept, but we have no solution in place.

We have an enterprise IoT strategy and some IoT solutions deployed at scale, but we are not using IoT big data for internal efficiencies or to drive new revenue.

We have an enterprise IoT strategy and integrated solutions that provide the business with data to drive internal efficiencies.

We have a seamless enterprise IoT platform that drives internal efficiencies and is also enabling transformation and new revenue opportunities. SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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GREAT EXPECTATIONS Nonetheless, business leaders have high hopes for IoT technology. Their expectations for the business benefits of IoT implementation are significant. They’re looking for the integration of IoT devices and data not simply to cut costs or increase efficiency but also to transform the business in some way. Some of the biggest business drivers behind the current usage of IoT are improving customer experience, creating new products and services, transforming business models, and increasing revenue, according to the survey. figure 3 The integration of IoT technology, data, and intelligence into the enterprise has the potential to create exponential rather than linear change, says Verma. “It’s disruptive technology without a doubt,” he says. “Those companies that aren’t investing in IoT will fall behind.” The business cases for IoT will vary by industry, says Chris Curran, chief technologist at PwC. “In some situations, the value will be obvious. We’ve had data streaming out of jet engines forever, and it’s clear that getting smarter about using that data could lead to improvements not just in operational efficiency and costs but improving the customer experience or creating new services,” Curran says. “In other industries, the exact applications and benefits are less clear.”

FIGURE 3

IoT BUSINESS BENEFITS Percentage of respondents indicating which of the following are the biggest business drivers behind their current usage of IoT. 58

Improved customer experience

Creating new services

Transforming the business model

Increasing revenue

Improving internal efficiencies

Saving money Creating new products

SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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In most industries, the threat of disruption is a powerful driver for IoT investments. “There’s nothing like an imminent threat to clarify the thinking,” says Daecher. “So companies are starting to figure it out. But the initial activity is very fragmented. One company I’m working with has more than a dozen IoT projects that started organically, and they’re still popping up. They’re now realizing they will need to rationalize those and create a more scalable IoT environment.” Respondents to the survey said their organizations are implementing IoT for both internal operations and customer-facing solutions. figures 4 and 5

FIGURE 4

IoT FOR MORE EFFICIENT OPERATIONS

Percentage of respondents indicating whether their organization is currently using IoT technology for internal operations such as supply chain, facility monitoring, and ck on pie chart to edit data asset utilization.

ck on pie chart to edit data

39% YES 49% NO 13% DON’T KNOW

SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

FIGURE 5

IoT FOR BETTER CUSTOMER SERVICE Percentage of respondents indicating whether their organization is currently using customer-facing IoT technology.

42% YES 45% NO 13% DON’T KNOW

SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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One company I’m working with has more than a dozen IoT projects that started organically, and they’re still popping up. They’re now realizing they will need to rationalize those and create a more scalable IoT environment. ANDY DAECHER, PRINCIPAL, DELOITTE CONSULTING

Today’s IoT initiatives are most likely to be found in the areas of customer experience (49 percent), operations (39 percent), customer service (38 percent), IT (32 percent), sales (28 percent), and logistics (27 percent). And companies are beginning to implement some specific programs based on IoT data and insight in the areas of customer relationship transformation, employee productivity improvements, asset tracking, and marketing programs. figure 6

A NEW DATA PARADIGM Many of the initial use cases of IoT are focused on incremental improvements. “It’s the low-hanging fruit,” says Pareekh Jain, research vice president at HfS Research. “But those small projects will not yield transformation.” In part, that’s because companies have never dealt with anything comparable to IoT before. “It’s a different kind of data than we’re used to collecting and organizing—and there’s a lot of it. The volume and velocity are bigger and faster than we’re used to. It makes us have to think about hosting it, sorting it, aggregating it, archiving it, and integrating it in ways we’ve never done before,” says

FIGURE 6

IMPLEMENTED IoT Percentage of respondents indicating which programs have been implemented based on IoT data or insights. [SELECT ALL THAT APPLY] 28

Employee productivity improvements

Transformation of customer relationships

Marketing programs

Asset tracking programs

New revenue generation/monetization

Asset maintenance programs SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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If we’re going to have hundreds of thousands of devices spitting out this data, we have to figure out how to do those things in a way that’s not going to slow the whole network down. CHRIS CURRAN, CHIEF TECHNOLOGIST, PWC

Curran of PwC. “And if we’re going to have hundreds of thousands of devices spitting out this data, we have to figure out how to do those things in a way that’s not going to slow the whole network down.” Survey respondents were clearly aware that big data in general and IoT specifically will require a new approach to networking. More than three-quarters of them (78 percent) said that new networking capabilities and technologies are very or somewhat important to their big data strategies. figure 7 Massive, fast-moving, and broadly sourced IoT data will create new network challenges. Nearly half of respondents (49 percent) said they are either exploring, investing in, or already have edge computing capabilities to conduct analytics at the source of the data.

FIGURE 7

NETWORKS ARE KEY FOR IoT Percentage of respondents indicating how important new networking capabilities/ technologies are to their big data strategy. 48

Very important Somewhat important Don’t know

Neither important nor unimportant

Not very important SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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THE UNIQUE CHALLENGES OF IoT The organizational and technical challenges presented by IoT are substantial. Many enterprises simply don’t know where to start. More than any other factor, a lack of big data skills and capabilities is preventing organizations from using or acting on more of the big data that may be generated by IoT systems, according to the survey. figure 8 “A big part of it is finding people experienced in collecting and managing the environments for processing this kind of data,” says Curran. “Then on the analytics side, you have to find people who know how to aggregate and analyze this data.” Some companies are able to find experienced hires to lead the charge, he says, but many are partnering with vendors and service providers to bring in experienced talent and IoT-specific thinking. Another significant organizational challenge is changing existing business processes to incorporate IoT-driven insight. If companies are to advance beyond collecting insight from stand-alone IoT initiatives to integrating IoT intelligence seamlessly into the organization, the business process change will be significant. “You have to recognize that IoT is going to dramatically change processes and plan for the redesign and change management that goes along with that,” says Daecher. “That’s a big deal, and you don’t get the benefits if you don’t do that.” Those process changes can impact customers as well. Another major issue: one-third of companies have yet to identify what problems to solve with IoT. “Without contextual relevance and appreciation of business value, all you have is a lot of data,” says John Ferraioli, managing director with Deloitte Consulting. “You have to move beyond just pushing data into a data lake and analyzing what you can to actually building a structured governance process around how you are going to ingest, analyze, and put context around this IoT data to get the most relevant outcomes.”

FIGURE 8

WHAT’S HOLDING BACK BIG DATA? Percentage of respondents indicating which issues are preventing their organization from using or acting on more of the big data being collected. 42

Lack of skills/capabilities

Changes required for existing business processes

Challenges of correlating multiple data formats (e.g., sensor data and other unstructured data)

Lack of identification of what problems we want to use big data to solve SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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Companies think that IoT will fit in somewhere, but it is so different from any data they’re used to dealing with, and they don’t know how to deal with that on a large scale. PAREEKH JAIN, RESEARCH VICE PRESIDENT, HFS RESEARCH

There are also the distinct technical challenges of IoT data and systems, chief among them big data variety and complexity, the lack of IoT standardization and protocols, and the difficulty of real-time data monitoring. figure 9 Facing these hurdles, the vast majority of organizations are utilizing just a fraction of the IoT data they currently generate—if any of it. Half of respondents indicated that they are using or acting on a limited amount of IoT data, while 28 percent of respondents said they are not using or acting on any IoT data yet. figure 10 “Companies think that IoT will fit in somewhere, but it is so different from any data they’re used to dealing with, and they don’t know how to deal with that on a large scale,” says Jain. “It’s not a known problem with a known solution.” The oil and gas industry, for example, which has as many as 30,000 sensors on a single offshore oil rig, is using less than 1 percent of the information gathered from those devices for decision making, according to a study by McKinsey & Company.

FIGURE 9

TACTICAL CHALLENGES PREVENTING BIG DATA AND IoT LIFTOFF Percentage of respondents indicating which of the following they think are the biggest tactical challenges that accompany the big data that Internet-connected devices generate. 51

Big data variety/complexity Lack of IoT standardization/protocols

42 41

Real-time data monitoring

Big data collection Difficulty securing distributed data

Inadequate network infrastructure SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

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FIGURE 10

A MINORITY OF COMPANIES ARE GETTING THE MOST OUT OF IoT AND BIG DATA Percentage of respondents indicating how much of the big data being generated by IoT their organization is actually using or acting on in some way (e.g., analyzing, making decisions based upon, using for automated intervention). We are using or acting on a limited amount of IoT data.

We are not yet using or acting on any IoT data.

We are using or acting upon the majority of relevant/applicable IoT data.

We are using or acting upon nearly all relevant/applicable IoT data. SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

A PATH FORWARD FOR IoT TRANSFORMATION Six out of ten companies said they use IoT and other sources of big data today for descriptive analytics—“what has happened”—but more than half say in the future they’ll be using it not only for predictive analytics but for prescriptive analytics as well. figure 11 But in order to build those capabilities on top of their IoT systems, organizations will need to take a more comprehensive approach to IoT strategy and implementation. The narrow focus of early IoT projects is understandable. “Most organizations are done with the idea of a giant enterprise data strategy. By the time they’ve created one, everything has changed,” Curran says. “If you look at the energy space, companies are trying to figure out how to make sense of data coming off their oil wells and pumps and are specifically focused on creating a case for predictive maintenance of that equipment. It’s incremental and iterative. A lot of people don’t want to spend any more than necessary to solve the problem.” But taking a more comprehensive approach now “will save a lot of headaches down the road,” Curran says.

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FIGURE 11

DESCRIPTIVE ANALYTICS IS MOST PERVASIVE Percentage of respondents indicating which of the following types of analysis their organization performs on IoT and/or other sources of big data today and will perform two years from now. ● TODAY

● TWO YEARS FROM NOW

Descriptive analytics (what has happened)

Predictive analytics (what will happen)

28 Prescriptive analytics (what we should do)

Not applicable

SOURCE HARVARD BUSINESS REVIEW ANALYTIC SERVICES SURVEY, SEPTEMBER 2016

Organizations committed to advancing their IoT approaches to be able to extract insight from these sensors and devices and integrate the resulting intelligence into the real-time stream of business can take a number of steps to pave the way for such transformation.

1. Think Big, Then Start Small The tendency today is to focus on very specific IoT use cases, but experts advise looking at where IoT-enabled insight might benefit the entire business value chain. “Think about the bigger picture and where the opportunities are for this technology to transform the business,” says Daecher. “Then start small, and scale quickly and iteratively. Start with the big goal, and then begin proving the value.”

2. Revisit and Rationalize Context is key. Determine how specific IoT pilots or initiatives, planned or already under way, map to the business strategy and digital transformation plans, advises Ferraioli, whether the focus is creating new growth or improving operations or transforming the customer experience. “Business leaders should ask themselves ‘Is it additive to the company’s maturation, or is it just a science project?’’’ Ferraioli says. “Leverage the former, and scale them based on the company’s ability to execute.”

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3. Consider Network Impact Early Next-generation network technology and processes will be critical to dealing with the deluge of big data from IoT systems. Developing and implementing a network strategy that supports enterprise IoT initiatives should happen very early in the process.

4. Break Down Silos Assign a multidisciplinary team to set the company’s IoT course. “Often these efforts are siloed—very heavy engineering teams that are missing business value knowledge or business strategists that are not very deep in their understanding of solutions or architecture,” says Daecher. “It has to be a cross-disciplinary team.”

5. Experiment More Effectively “The most important thing you can do is to develop a systematic approach to experimentation. You have to take that outcome you’re looking to achieve and use that as the lens for how to design those experiments,” Curran says. “The experimentation will be ongoing, and you need a process for continuing to evaluate ideas and bring them to life.”

6. Find Experienced Partners In this rapidly evolving technology space with little standardization and few protocols or proven best practices, strategic partners’ IoT-related resources and skills will be critical, says Jain. “One or two experts within your organization’s walls won’t get you very far,” adds Verma. “When you’re embedding technology into parts of the business where that hasn’t been the focus in the past—particularly in operational technology areas—and doing it at a rapid pace, you need top talent.”

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NOTES

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