Hot 10 Beyond Powerful Big Data

HOT 10 BEYOND POWERFUL BIG DATA

TECHNOLOGY

Issue 08 Autumn 2018

10 DELIVERING DATA INTELLIGENCE BACKED

BY SESSION REPLAY Yaron Morgenstern CEO Glassbox

Data Science for Startups: Introduc on P.26

Why SQL is bea ng NoSQL, and what this means for the future of data P.38

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EDITOR’S CORNER

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umerous reports say that 90% of the accessible data has been created over the last two years, and the term Big Data has been around since 2005, when it was propelled

by O’Reilly Media in 2005. However, in the previous couple of years, there has been a huge increment in Big Data businesses, all attempting to manage Big Data and helping organizations to understand the technology, and now more and more organizations are gradually adopting and moving towards Big Data. Not to forget, the large Big Data revolution is still ahead of us, so, a lot will change in the coming years. Yet, few companies are thriving ahead of the Big Data curve while delivering exclamatory Big Data services to the customers. Therefore, it becomes vital for us to showcase these big data solution providers that contribute so much to the economy. The “Hot 10 Beyond Powerful Big Data” brings readers the journeys of such big data solution providers and their forerunners who goes beyond the conventional way to make the exclamation happen. The publication particularly highlights big data companies that are accelerating along with this technology while going beyond the bits and bytes of Big Data.

As our cover story, we have Glassbox which is helping enterprises gain a deep understanding of their online customers. Led by CEO, Yaron Morgenstern, Glassbox develops and delivers digital customer experience solutions that empower organizations to manage and optimize the entire digital lifecycle of their web and mobile customers worldwide. Glassbox offers Customer Experience Analytics, a solution to optimize web and mobile customer experiences; Customer Support Optimization, a solution to streamline digital support interactions in call centers; Risk Management & Compliance, a solution to record, store, and retrieve the digital truth; and Experience Performance Analytics, a solution to measure, monitor, and enhance channels performance.

Following this we have Actimirror: The leading IoT smart mirror platform at its best; Bigstep: A big data cloud provider providing with tailor-made big data; Lynx Analytics: Solving real, complex problems with the power of Big Data graph analytics; BigTapp: Providing customers with Big Data & Analytics solutions; SalesChoice: An AI/Cognitive Sciences SaaS provider, with a registered Salesforce AppExchange solution; and Virtual Tech Hereos: Bringing-forward enterprise-level expertise with deep knowledge.

Editor in Chief CHRISTINE [editor@beyondexclamation.com]

Managing Editor JACK [jack@beyondexclamation.com]

Art Director VIJAYKUMAR [design@beyondexclamation.com]

Graphic Artist NICK [nick@beyondexclamation.com]

Project Manager JENNIFER [jennifer@beyondexclamation.com]

Development Manager JUSTIN Apart from these profiles, we have articles, Data Science for Startups: Introduction and Why SQL is beating NoSQL, and what this means for the future of data penned down by Ben Weber, Principal Data Scientist at Zynga and Ajay Kulkarni, Co-founder and CEO of Timescale.

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In addition to our print magazine, we also provide relevant industry news and updates, as well as some thoughtprovoking articles and blogs on our website. Make sure to follow the same as we at Beyond Exclamation are looking forward to interact with our readers. Let’s connect on the web!

What’s Inside...

Business Boulevard

B E O N 10

Helping enterprises gain a deep understanding of their online customers

Omniscient Voyage

48

Solving real, complex problems with the power of Big Data graph analytics

Excellence Causeway

20 The leading IoT smart mirror platform at its best

26 Data Science for Startups: Introduction

Newsmakers Locale

54 Providing customers with Big Data & Analytics solutions

Younick Corner

Y D

32 A big data cloud provider providing with tailor-made big data

38 Why SQL is beating NoSQL, and what this means for the future of data

DeďŹ nitive Destination

60 An AI/Cognitive Sciences SaaS provider, with a registered Salesforce AppExchange solution

66 Bringing-forward enterprise-level expertise with deep knowledge

DELIVERING DATA INTELLIGENCE BACKED

BY SESSION REPLAY

B

ig Data, AI, Machine Learning are some of the latest technological buzzwords that have been spreading like wildfire in the business world owing to their massive potential. Organizations across different sectors are leveraging these technologies to turn data into actionable insights.

Incepting in the heart of Israel, back in 2010, Glassbox has made use of these capabilities to help enterprises gain a deep understanding of their online customers by identifying what is happening with their digital channels and why is it crucial.

Websites of enterprises and mobile applications are often almost like the black hole; no one is aware of what’s happening inside them; why are people struggling to complete transactions, why are they abandoning sessions, and so on. Glassbox brings-in clarity and transparency to the digital customer journey.

Shaping the idea

Yaron Morgenstern CEO Glassbox

Initially, the vision for Glassbox was actually anchored in Digital Compliance, and not in Customer Experience or UX. Back in 2010, the founders of the company had a very clear understanding concerning how the customer journeys will become increasingly digitalized and personalized. The founders, Yoav Schreiber, Yaron Gueta and Hanan Blumstein, believed that just like how regulation had made it mandatory for call centers to record calls in many industries, the same would apply to digital channels as well.

Enterprises, especially in datasensitive environments, like the finance industry, would need to keep evidential records of digital sessions, both on the web and on mobile applications. And they would not only need to keep these records at scale, they would also need to retrieve them easily and share them internally and externally in a way that could be consumed by business people. This was the idea behind 100% accurate session replay. Capturing 100% of the data with a solution that is easy to deploy and maintain was key, as websites and mobile apps keep on being updated. Thus, the solution needed to be able to keep collecting the data, no matter what.

Solving challenges of enterprises The initial vision of Glassbox holds true even today, and the fact that we started from compliance made us take a ’capture everything in real time’ approach that now days allows us to address many use cases and needs around the Digital Customer Management (CX improvement, Compliance, Production support, Fraud, etc.) in a unique way.

Along the way, the company has expanded its range of services. It helps solve various challenges of enterprises, including Digital Customer Experience Management (CEM), Conversion Rate Optimization (CRO), IT Production Support, and many more.

“Today most of our focus is on

generating Automatic Insights based on the data our customers collect: our clients are expecting us to tell them in real-time what’s going on with their digital channels: what are the main struggles people are experiencing both on web and mobile apps, what is impacting their business the most in terms of revenue loss, etc.,� mentions the CEO of Glassbox, Yaron Morgenstern.

To cater to this customer demand, over the last 12 months, Glassbox has added deep AI and Machine Learning capabilities to its platform. This is done, so as to ensure that the company learns by itself what normal data

behavior looks like and can set up thresholds and alerts automatically, with no need for users to configure anything in the system.

Providing unparalleled benefits Today, we are all familiar with

buzzwords like Digital Economy or Uber Economy. The underlying disruption such technologies has enabled is that when you ask millennials, they have an inclination of preference towards access instead of ownership.

Access to car rather than owning a car or access to real estate rather than buying a house. “I think the same phenomena is happening within Enterprises, with regards to data,” claims Yaron.

Organizations are starting to realize that they can enable the entire workforce to access data in a very friendly manner. Data doesn’t need to be owned by IT departments anymore. This is exactly what Glassbox allows them to do. Its use case goes beyond just improving the online customer experience.

“We see the usage of Glassbox expanding inside organizations for very different use cases, from Compliance to Customer Care via Production Support. All departments are using our data in the form of session replays and understand why are people struggling online. When everybody sees the same picture, cooperation is easier, siloes are breaking down and agility is increased. Most of our competitors can’t live up to this promise.”

searching our smartphone devices, the next we are buying a product or a service from our laptop or tablet.

Glassbox is leading the pack when it comes to having a combined solution that supports both channels on a single platform.

Leading by example Being the CEO of the company, Yaron brings a lot to the company and to its productivity level. One of his key attributes is defining a crystal clear and powerful strategy, and he makes sure that it is communicated to the entire company.

Going beyond the startup nation Apart from several VC’s who are backing the company, Glassbox benefits from the Israeli startup ecosystem and an extraordinary pool of R&D talent. The great level of innovation especially in the areas of Machine Learning and AI ensures Glassbox is always at the forefront of innovation. “I’d say that our main advantage is probably that Israel holds some of the world’s best creative minds and developers. We’re very proud that all our R&D team is based out of Israel,” asserts Co-founder and CTO, Yaron Gueta.

The sky is the limit

Another benefit that Glassbox provides to its customers is the convergence of web and mobile. As a consumer, we don’t operate in siloes; at one moment we are

2017 was a breakthrough year for Glassbox as the company went on to close massive deals with world famous companies and brands and

tripled its revenues. But Yaron and his colleagues know that there is still a long way to go in the future.

“As we like to say at Glassbox: The reward for good work is… more work!”

This year, the company is on track to triple its revenues once again.

Glassbox is expected to keep growing at a rapid pace as it sets more and more aggressive targets to achieve. “We’re already working with the top 3 US Banks and expanding into many other verticals such as Insurance, Travel, Telecommunication, Retail, Etc.” From the company’s perspective, the sky is the limit for Glassbox.

The latest breakthrough that strengthens the market position th

Glassbox on 4 Oct, 2018 announced the closing of a $25 million round of financing led by Updata Partners, a leading software-focused growth equity firm based in Washington, D.C., and joined by Ibex Investors, CEIIF, the venture arm of CreditEase, and Gefen Capital.

This takes its total capital raised since inception to $32.5 million.

The investment will fuel Glassbox’s hyper growth globally while accelerating product development efforts related to its automatic insight capabilities.

company now has the capital to scale up ahead of exponentially increasing demand from our existing and future customers,” said Yaron Morgenstern, CEO at Glassbox. “We believe Updata’s investment and expertise in growing leading enterprise software companies will enable us to become the unrivaled leader in digital customer management.”

“While successfully serving some of the largest and most digitally mature enterprises in the world, the

From the beginning, Glassbox architected its technology platform to capture all activity in real time,

an approach that today allows for Digital Customer Management use cases and needs that others in the market struggle to address. For example, Glassbox’s platform is a single collaborative solution for business and IT teams to optimize digital customer experiences across web, mobile web, and mobile app.

This approach, coupled with an advanced application of machine learning in the digital analytics space, enables Glassbox to surface automatic insights, resulting in unmatched ROI for customers.

Leading enterprises are leveraging the Glassbox platform to optimize web and mobile customer experiences, identify and fix IT performance issues, address risk management and compliance use cases, and guide real-time customer support in contact centers.

“Glassbox serves some of the world’s largest and most demanding enterprises, which gives us confidence the platform is proven and there is tangible ROI for customers” said Carter Griffin,

General Partner at Updata Partners.

“Updata is excited to work alongside management to build on this existing momentum and support the company’s investment in expanding both the go-to-market and product.”

of mind. Glassbox’s unique ability to capture 100% of the largest enterprises’ digital activity in realtime across web, mobile, and mobile app is what gets us excited to partner with Glassbox and for the opportunity ahead.”

Braden Snyder, Vice President at Updata Partners added, “Digital channels for enterprises are becoming increasingly important, often a differentiator, and greater visibility into customer experience, conversion, and performance is top

As part of the transaction, Griffin and Snyder will join Glassbox’s board of directors.

Victor Ruiz Co-founder & CEO actiMirror

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Mirror Intelligence at its Best

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irrors are timeless decoration pieces, not affected by fashion, and naturally attract the attention of the consumer. In fact, on average, women glance to their reflection 70 times per day during 21 minutes and men 80 times but only during 8 minutes. But what would you say, when you hear about mirror intelligence acquiring a place in our daily lives? It would be out of the box idea and fascinating at the time, wouldn’t it? When talking about mirror intelligence industry, the name that comes across first is actiMirror, the leading IoT smart mirror platform delivering real-time personalized experiences to consumers and data analytics insights to businesses. By using a high resolution display that turns on when activated by sensors and which also returns to beautifully designed and timeless decoration piece once the interaction is taken place, actiMirror is driving the next wave of IoT.

actiMirror is the leading smart mirror platform at the forefront of smart mirror technology that delivers Real-Time Personalized Experiences to consumers and Data Analytics Insights to businesses. The company is focused in the Retail, Hospitality, Fitness and Healthcare verticals.

Simply put, one can consider actiMirror as an oversized cell phone behind a true mirror. This means the company offers:

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· A scalable open platform: actiMirror allows a wide variety of customized Apps and User Experiences in the same way one uses its cell phone. ·

The power of digital: actiMirror has machine learning capabilities as well as data gathering and analytics insights along with a proprietary cloud management system to deliver a true 360 degrees omni-channel ecosystem.

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A perfect mirror (NOT a screen): actiMirror is essential in most lines of business, particularly Retail, and instrumental to attract customer attention.

·

Innovative in-store customer journeys: actiMirror has integrated a wide variety of sensors in its mirrors, in fact more than you can find in your standard tablet or cell phone. The said sensors are to gather data and interact with your customers in real-time providing a resounding experience.

Sensors can be as simple as a proximity sensor to detect a customer’s presence in front of a mirror, or actiMirror can detect anonymous biometrics (fully compliant with the European Union General

Data Protection Regulation) such as gender, age, ethnicity and even the mood of the customer to offer them different in-store user experiences.

Meet the maestro The company’s Co-founder and CEO, Victor Ruiz is a Hong Kong based European entrepreneur with extensive global exposure having lived and worked in Europe, North America, Asia, Africa and the Middle East. Victor and his partners co-founded actiMirror in 2014 after a stint of 18 years delivering growth as a corporate entrepreneur for various Fortune 500 US and European corporations.

Victor, over the last four years, has led actiMirror from being a Hong Kong start-up to become the global leader in its sector. The company is headquartered in Hong Kong with offices in Silicon Valley, soon in Shanghai, and a network of international distributors.

Victor earned his Executive MBA at Kellogg-HKUST, ranked number one worldwide by the Financial Times.

Collect and analyze consumer data brilliantly This IoT platform collects and analyzes consumer data to increase in-store traffic and grow sales while helping customers transform stores into omni-channel hubs and putting ‘Brick & Click’ under one roof.

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actiMirror also provides personalized and differentiating, instore consumer centric experiences.

By going ahead of the competitors, actiMirror uses both custom designed as well as generic apps along with built-in sensors to display personalized media content in real-time; that is in addition to the customers’ reflection. Content is selected to match the consumers’ interests based on a combination of their anonymous demographic profiles and/or the objects they are trying, transforming the mirror to display: related interactive product demonstrations, augmented reality experiences, tutorials or infotainment videos to deliver high consumer engagement and brand loyalty.

Furthermore, the platform also collects and analyzes anonymous data from consumer interactions, such as sales conversion rates, instore behavior, preferences, choices, etc. The brands, in turn, receive customized data analytics insights based on machine learning and apply to their specific metrics and KPIs to identify, calibrate and optimize targeted actionable business levers to increase in-store traffic and deliver sales growth.

“Our target is to consolidate actiMirror as the undisputed number one smart mirror platform for Retail Worldwide, getting a sizeable market share the Offline to

Online Retail Trillion Dollar Opportunity,” Victor asserts.

Leading the industry from the front The main difference between actiMirror and its main competitor is that actiMirror is a true mirror, while the competitors hold a screen display behaving like a mirror giving them issues within the cosmetics vertical where true mirrors are required. The competition’s price point currently is several times higher (!), making them non-scalable unless they dramatically improve their business model.

Furthermore, actiMirror is entering a commercial partnership with the undisputed number one augmented reality beauty platform with several million downloads worldwide providing the company with a large customer platform for scalability.

Overall, actiMirror is the successful integration of first mover advantage in a large not yet well signalled Offline to Online growing market in the cusp of taking off. actiMirror has a comprehensive pool of selected talent in complementary disciplines, backgrounds and industries.

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Also, actiMirror’s proprietary and disruptive technology for both consumers & enterprises along with the commercial validation, market presence and traction, gives the company a solid and achievable position of market power vs competition. Not to forget, actiMirror’s fair and attractive valuation and promising exit possibilities. Whether you are a business striving to offer the best digital touchpoints to your consumers or an investor, look out for this company. Because it’s going to be big!

For more information, visit actimirror.com

Ben Weber Principal Data Scientist Zynga

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Data Science for Startups: Introduction

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I

recently changed industries and joined a startup company where I'm responsible for building up a data science discipline.

While we already had a solid data pipeline in place when I joined, we didn't have processes in place for reproducible analysis, scaling up models, and performing experiments. The goal of this series of blog posts is to provide an overview of how to build a data science platform from scratch for a startup, providing real examples using Google Cloud Platform (GCP) that readers can try out themselves.

This series is intended for data scientists and analysts that want to move beyond the model training stage, and build data pipelines and data products that can be impactful for an organization. However, it could also be useful for other disciplines that want a better understanding of how to work with data scientists to run experiments and build data products. It is intended for readers with programming experience, and will include code examples primarily in R and Java.

Why Data Science?

One of the first questions to ask when hiring a data scientist for your startup is how will data science improve our product? At

Windfall Data, our product is data, and therefore the goal of data science aligns well with the goal of the company, to build the most accurate model for estimating net worth. At other organizations, such as a mobile gaming company, the answer may not be so direct, and data science may be more useful for understanding how to run the business rather than improve products. However, in these early stages it's usually beneficial to start collecting data about customer behavior, so that you can improve products in the future.

Some of the benefits of using data science at a start up are:

to an organization where data science provides key input for product development.

Series Overview Here are the topics I am planning to cover for this blog series. As I write new sections, I may add or move around sections. Please provide comments at the end of this posts if there are other topics that you feel should be covered.

1.

Introduction (this post): Provides motivation for using data science at a startup and provides an overview of the content covered in this series of posts. Similar posts include functions of data science, scaling data scienceand my FinTech journey.

Ÿ

Identifying key business metrics to track and forecast

Ÿ

Building predictive models of customer behavior

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Running experiments to test product changes

Ÿ

Building data products that enable new product features

2.

Tracking Data: Discusses the motivation for capturing data from applications and web pages, proposes different methods for collecting tracking data, introduces concerns such as privacy and fraud, and presents an example with Google PubSub.

Many organizations get stuck on the first two or three steps, and do not utilize the full potential of data science. The goal of this series of blog posts is to show how managed services can be used for small teams to move beyond data pipelines for just calculating runthe-business metrics, and transition

3.

Data pipelines: Presents different approaches for collecting data for use by an analytics and data science team, discusses approaches with flat files, databases, and data lakes, and presents an implementation using PubSub, DataFlow, and BigQuery.

Similar posts include a scalable

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analytics pipeline and the evolution of game analytics platforms.

4.

Business Intelligence: Identifies common practices for ETLs, automated reports/dashboards and calculating run-the-business metrics and KPIs. Presents an example with R Shiny and Data Studio.

5.

Exploratory Analysis: Covers common analyses used for digging into data such as building histograms and cumulative distribution functions, correlation analysis, and feature importance for linear models. Presents an example analysis with the Natality public data set. Similar posts include clustering the top 1% and 10 years of data science visualizations.

6.

Predictive Modeling: Discusses approaches for supervised and unsupervised learning, and presents churn and cross-promotion predictive models, and methods for evaluating offline model performance.

7.

Model Production: Shows how to scale up offline models to score millions of records, and discusses batch and online approaches for model deployment.

Similar posts include Productizing Data Science at Twitch, and Producizting Models with DataFlow.

8.

Experimentation: Provides an introduction to A/B testing for products, discusses how to set up an experimentation framework for running experiments, and presents an example analysis with R and bootstrapping. Similar posts include A/B testing with staged rollouts.

9.

Recommendation Systems: Introduces the basics of recommendation systems and provides an example of scaling up a recommender for a production system. Similar posts include prototyping a recommender.

10.

Deep Learning: Provides a light introduction to data science problems that are best addressed with deep learning, such as flagging chat messages as offensive.

Provides examples of prototyping models with the R interface to Keras, and productizing with the R interface to CloudML. The series is also available as a book in web and print formats.

Tooling Throughout the series, I'll be presenting code examples built on Google Cloud Platform. I choose this cloud option, because GCP provides a number of managed services that make it possible for small teams to build data pipelines,

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productize predictive models, and utilize deep learning. It's also possible to sign up for a free trial with GCP and get $300 in credits.

This should cover most of the topics presented in this series, but it will quickly expire if your goal is to dive into deep learning on the cloud.

For programming languages, I'll be using R for scripting and Java for production, as well as SQL for working with data in BigQuery. I'll also present other tools such as Shiny. Some experience with R and Java is recommended, since I won't be covering the basics of these languages.

Ben Weber is a data scientist in the gaming industry with experience at Electronic Arts, Microsoft Studios, Daybreak Games, and Twitch. He also worked as the first data scientist at a FinTech startup.

Lucas Roh CEO Bigstep

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Tailor-Made Big Data

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ndustry leaders believe that the big data and analytics are still more difficult than it needs to be. There are still too many levers to adjust to make it all work. On the infrastructure side, while the move towards the cloud is inevitable, companies are also starting to realize that the cost is more expensive than they expected and thus the bill shock. Enters Bigstep which is trying to make it as easy and accessible as possible while at the same time reducing the cost of infrastructure whether or not the clients use Bigstep cloud or other cloud providers. Bigstep is a big data cloud provider with offices in Chicago, London, and Bucharest, and provides a full-stack solutions from the bare-metal infrastructure to the managed services for big data and analytics. The company supports its big data solutions on other tier 1 cloud providers such as AWS and Google Cloud for those wanting its big data solutions without using Bigstep’s cloud.

It all began when company’s Founder and CEO, Lucas Roh saw the need for a high performance cloud focused on the big data that is very easy to use. He had previously founded and sold a leading hosting provider, Hostway.

Therefore, he had what it takes to make an idea, a reality. Nonetheless, he laid the cornerstone of Bigstep with the dual vision of companies increasingly moving their infrastructures to the cloud as well as the rising need for more accessible big data solutions. Over the years, Lucas has increasingly been convinced of the cloud vision as well as the big data vision. He believes the need for companies to leverage their data that not only bring new insights but

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also to guide them is now more than ever. Today, Bigstep is a big data orchestration company that empowers organizations determined to make sense of their data with a full-stack big data ecosystem running in a highperformance bare-metal cloud, or on-premises.

A solid understanding of the cloud and big data fields Bigstep empowers organizations determined to use cloud resources intelligently and to make sense of their data, by providing a full-stack big data ecosystem running in a high-performance bare metal cloud.

Providing a powerful, reliable, and scalable cloud-based platform purpose-built for big data is the number one goal of the Bigstep team. The company has handpicked the most valued big data applications to date and the best hardware components, to build and update Bigstep Metal Cloud. The team started out with a solid understanding of the cloud and big data fields and designed all Bigstep services to meet the expectations and exigencies of professionals like ourselves. Whether clients are aiming to outplay their competition or attempting to improve operational efficiency and customer retention, with Bigstep they are just minutes away from the proper insights.

clients’ underlying infrastructure is ready to adapt for point-in-time computationally intensive experiments.

The right platform for data science and machine learning Bigstep empowers all team members with democratized access to big data translates into rich insights that are uncovered quicker and can drive informed business decisions with greater impact.

Moreover, Bigstep enables clients to do all of that in just a couple of clicks and have access to a platform where they can run their data science workloads at scale in a flexible environment. ·

·

·

Self-service data science: With Bigstep, clients can write code in Python, R, and Scala in Jupyter Notebook accelerated by Spark to explore and visualize data, build models and develop analytics pipelines.

Perform modelling and run predictive analytics: Bigstep provides clients with the most powerful machine learning libraries (Scikit-Learn, Mlib, XGboost, Spark ML) to experiment with algorithms, train model, validate them and apply them on production data in real-time at scale.

Scale with workload: As complexity evolves,

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·

Self-service data visualization: The company brings processing closer to data, slice and dice through clients’ data at every step of the analysis.

Moreover, the clients are able to explore and interact using charts with powerful data visualization tools, run experiments for “what if” scenarios and take decisions in real-time.

·

Enable conversations around the data: Stories help Bigstep better understand changes in the business. Notebooks are used to tell a story, combining code, visualizations and results in a ready-to-share format that is easy to understand by all of clients’ teams.

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Focus on analysis, worry less about the infrastructure: Bigstep offers managed architectures that consist of Hadoop and Spark clusters and modern BI tools, allowing data scientists and engineers run SQL

analytics at scale, tweak machine learning algorithms and strategize more on their results.

Meet the maestro Lucas is also founder and nonexecutive chairman of Easyhost and Affinity.com. He had previously founded and served as the CEO of Hostway Corporation, a global web hosting company, before its sale in 2013.Prior to founding Hostway, Lucas was a computer scientist at Argonne National Laboratory. He has authored and co-authored over a dozen published academic papers, and has received several patents.

He received his undergraduate degree in physics from the University of Chicago and a doctorate in computer science from Colorado State University. Lucas has been in the cloud industry for over 20 years and has been one of

the pioneers of cloud computing.

He is a serial entrepreneur and investor who helped build a number of companies over the years. He has a PhD in computer science and a passion for efficiency.

Award winning bare-metal infrastructure for greater performance In 2016, Bigstep introduced the first Open Data Exploration-as-aService with the launch of Bigstep DataLab that simplifies and streamlines data science and analytics. The key components of Bigstep DataLab include Bigstep Data Lake, an infinite repository system where structured, semistructured, and unstructured data can be stored side by side and Bigstep Real-Time Spark Service, a managed, fully scalable big data computation service capable of machine learning, graph processing, and statistics. Bigstep

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DataLab can easily handle large quantities of real-time and historical data, perform complex machine-learning tasks and be quickly stopped or repurposed, with on-demand scalability and pricing. It enables users to experiment with powerful data technologies, leveraging Bigstep’s award-winning bare-metal infrastructure for greater performance and security than any other cloud offering. Going ahead, Bigstep looks forward to the launch of Bigstep Data Lake Service which will hit the market in Q4.

Lucas feels it is a breakthrough product that makes it a lot easier to do analytics for companies.

What started as an idea and need of an hour for Lucas has turned into an industry leader and continues to achieve new feats with the releases of innovative products.

Ad 11

Ajay Kulkarni Co-founder & CEO Timescale

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Why SQL is beating NoSQL, and what this means for the future of data

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series database that fully embraces SQL. In this post we examine why the pendulum today is swinging back to SQL, and what this means for the future of the data engineering and analysis community.

ince the dawn of computing, we have been collecting exponentially growing amounts of data, constantly asking more from our data storage, processing, and analysis technology. In the past decade, this caused software developers to cast aside SQL as a relic that couldn’t scale with these growing data volumes, leading to the rise of NoSQL: MapReduce and Bigtable, Cassandra, MongoDB, and more.

Part 1: A New Hope To understand why SQL is making a comeback, let’s start with why it was designed in the first place.

Our story starts at IBM Research in the early 1970s, where the relational database was born. At that time, query languages relied on complex mathematical logic and notation. Two newly minted PhDs, Donald Chamberlin and Raymond Boyce, were impressed by the relational data model but saw that the query language would be a major bottleneck to adoption.

They set out to design a new query language that would be (in their own words): “more accessible to users without formal training in mathematics or computer programming.”

Yet today SQL is resurging. All of the major cloud providers now offer popular managed relational database services: e.g., Amazon RDS, Google Cloud SQL, Azure Database for PostgreSQL (Azure launched just this year). In Amazon’s own words, its PostgreSQL- and MySQL-compatible database Aurora database product has been the “fastest growing service in the history of AWS”. SQL interfaces on top of Hadoop and Spark continue to thrive. And just last month, Kafka launched SQL support. Your humble authors themselves are developers of a new time-

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(Sadly, Raymond Boyce never had a chance to witness SQL’s success. He died of a brain aneurysm 1 month after giving one of the earliest SQL presentations, just 26 years of age, leaving behind a wife and young daughter.)

Query languages before SQL ( a, b ) vs SQL ( c ) (source) Think about this. Way before the Internet, before the Personal Computer, when the programming language C was first being introduced to the world, two young computer scientists realized that, “much of the success of the computer industry depends on developing a class of users other than trained computer specialists.”

They wanted a query language that was as easy to read as English, and that would also encompass database administration and manipulation.

For a while, it seemed like SQL had successfully fulfilled its mission. But then the Internet happened.

Part 2: NoSQL Strikes Back While Chamberlin and Boyce were developing SQL, what they didn’t realize is that a second group of engineers in California were working on another budding project that would later widely proliferate and threaten SQL’s existence. That project was ARPANET, and on October 29, 1969, it was born.

But SQL was actually fine until another engineer showed up and invented the World Wide Web, in 1989.

Like a weed, the Internet and Web flourished, massively disrupting our world in countless ways, but for the data community it created one particular

The result was SQL, first introduced to the world in 1974. Over the next few decades, SQL would prove to be immensely popular. As relational databases like System R, Ingres, DB2, Oracle, SQL Server, PostgreSQL, MySQL (and more) took over the software industry, SQL became established as the preeminent language for interacting with a database, and became the lingua franca for an increasingly crowded and competitive ecosystem.

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headache: new sources generating data at much higher volumes and velocities than before.

As the Internet continued to grow and grow, the software community found that the relational databases of that time couldn’t handle this new load. There was a disturbance in the force, as if a million databases cried out and were suddenly overloaded.

Then two new Internet giants made breakthroughs, and developed their own distributed non-relational systems to help with this new onslaught of data: MapReduce (published 2004) and Bigtable (published 2006) by Google, and Dynamo (published 2007) by Amazon. These seminal papers led to even more non-relational databases, including Hadoop (based on the MapReduce paper, 2006), Cassandra (heavily inspired by both the Bigtable and Dynamo papers, 2008) and MongoDB (2009). Because these were new systems largely written from scratch, they also eschewed SQL, leading to the rise of the NoSQL movement.

And boy did the software developer community eat up NoSQL, embracing it arguably much more broadly than the original Google/Amazon authors intended.

It’s easy to understand why: NoSQL was new and shiny; it promised scale and power; it seemed like the fast path to

engineering success. But then the problems started appearing.

Developers soon found that not having SQL was actually quite limiting. Each NoSQL database offered its own unique query language, which meant: more languages to learn (and to teach to your coworkers); increased difficulty in connecting these databases to applications, leading to tons of brittle glue code; a lack of a third party ecosystem, requiring companies to develop their own operational and visualization tools.

These NoSQL languages, being new, were also not fully developed.

For example, there had been years of work in relational databases to add necessary features to SQL (e.g., JOINs); the immaturity of NoSQL languages meant more complexity was needed at the application level.

The lack of JOINs also led to denormalization, which led to data bloat and rigidity.

Some NoSQL databases added their own “SQL-like” query languages, like Cassandra’s CQL. But this often made the problem worse.

Using an interface that is almost identical to something more common actually created more mental friction: engineers didn’t know what was supported and what wasn’t.

Some in the community saw the problems with NoSQL early on

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(e.g., DeWitt and Stonebraker in 2008). Over time, through hardearned scars of personal experience, more and more software developers joined them.

Part 3: Return of the SQL Initially seduced by the dark side, the software community began to see the light and come back to SQL. First came the SQL interfaces on top of Hadoop (and later, Spark), leading the industry to “backcronym” NoSQL to “Not Only SQL” (yeah, nice try).

Then came the rise of NewSQL: new scalable databases that fully embraced SQL. H-Store (published 2008) from MIT and Brown researchers was one of the first scale-out OLTP databases. Google again led the way for a georeplicated SQL-interfaced database with their first Spanner paper (published 2012) (whose authors include the original MapReduce authors), followed by other pioneers like CockroachDB (2014).

At the same time, the PostgreSQL community began to revive, adding critical improvements like a JSON datatype (2012), and a potpourri of new features in PostgreSQL 10: better native support for partitioning and replication, full text search support for JSON, and more (release slated for later this year). Other companies like

CitusDB (2016) and yours truly (TimescaleDB, released this year) found new ways to scale PostgreSQL for specialized data workloads.

In fact, our journey developing TimescaleDB closely mirrors the path the industry has taken. Early internal versions of TimescaleDB featured our own SQL-like query language called “ioQL.” Yes, we too were tempted by the dark side: building our own query language felt powerful. But while it seemed like the easy path, we soon realized that we’d have to do a lot more work: e.g., deciding syntax, building various connectors,

educating users, etc. We also found ourselves constantly looking up the proper syntax to queries that we could already express in SQL, for a query language we had written ourselves!

One day we realized that building our own query language made no sense. That the key was to embrace SQL. And that was one of the best design decisions we have made.

Immediately a whole new world

Google has clearly been on the leading edge of data engineering and infrastructure for over a decade now.

It behooves us to pay close attention to what they are doing.

opened up. Today, even though we are just a 5 month old database, our users can use us in production and get all kinds of wonderful things out of the box: visualization tools (Tableau), connectors to common ORMs, a variety of tooling and backup options, an abundance of tutorials and syntax explanations online, etc.

But don’t take our word for it.

Take Google’s.

Take a look at Google’s second major Spanner paper, released just four months ago (Spanner: Becoming a SQL System, May 2017), and you’ll find that it bolsters our independent findings.

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For example, Google began building on top of Bigtable, but then found that the lack of SQL created problems (emphasis in all quotes below ours):

The success of this approach speaks for itself. Spanner is already the “source of truth” for major Google systems, including AdWords and Google Play, while “Potential Cloud customers are overwhelmingly interested in using SQL.”

“While these systems provided some of the benefits of a database system, they lacked many traditional database features that application developers often rely on. A key example is a robust query language, meaning that developers had to write complex code to process and aggregate the data in their applications.

As a result, we decided to turn Spanner into a full featured SQL system, with query execution tightly integrated with the other architectural features of Spanner (such as strong consistency and global replication).”

Considering that Google helped initiate the NoSQL movement in the first place, it is quite remarkable that it is embracing SQL today.

What this means for the future of data: SQL as the universal interface In computer networking, there is a concept called the “narrow waist,” describing a universal interface.

This idea emerged to solve a key problem: On any given networked device, imagine a stack, with layers of hardware at the bottom and layers of software on top. There can exist a variety of networking hardware; similarly there can exist a variety of software and applications. One needs a way to ensure that no matter the hardware, the software can still connect to the network; and no matter the software, that the networking hardware knows how to handle the network requests.

Later in the paper they further capture the rationale for their transition from NoSQL to SQL: The original API of Spanner provided NoSQL methods for point lookups and range scans of individual and interleaved tables. While NoSQL methods provided a simple path to launching Spanner, and continue to be useful in simple retrieval scenarios, SQL has provided significant additional value in expressing more complex data access patterns and pushing computation to the data.

The paper also describes how the adoption of SQL doesn’t stop at Spanner, but actually extends across the rest of Google, where multiple systems today share a common SQL dialect: Spanner’s SQL engine shares a common SQL dialect, called “Standard SQL”, with several other systems at Google including internal systems such as F1 and Dremel (among others), and external systems such as BigQuery…

For users within Google, this lowers the barrier of working across the systems. A developer or data analyst who writes SQL against a Spanner database can transfer their understanding of the language to Dremel without concern over subtle differences in syntax, NULL handling, etc.

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Like networking we have a complex stack, with infrastructure on the bottom and applications on top.

Typically, we end up writing a lot of glue code to make this stack work. But glue code can be brittle: it needs to be maintained and tended to.

IP as the Networking Universal Interface (source) In networking, the role of the universal interface is played by Internet Protocol (IP), acting as a connecting layer between lower-level networking protocols designed for local-area network, and higherlevel application and transport protocols. And (in a broad oversimplification), this universal interface became the lingua franca for computers, enabling networks to interconnect, devices to communicate, and this “network of networks” to grow into today’s rich and varied Internet.

What we need is an interface that allows pieces of this stack to communicate with one another. Ideally something already standardized in the industry.

Something that would allow us to swap in/out various layers with minimal friction.

That is the power of SQL. Like IP, SQL is a universal interface.

We believe that SQL has become the universal interface for data analysis.

We live in an era where data is becoming “the world’s most valuable resource” (The Economist, May 2017).

As a result, we have seen a Cambrian explosion of specialized databases (OLAP, time-series, document, graph, etc.), data processing tools (Hadoop, Spark, Flink), data buses (Kafka, RabbitMQ), etc. We also have more applications that need to rely on this data infrastructure, whether third-party data visualization tools (Tableau, Grafana, PowerBI, Superset), web frameworks (Rails, Django) or custom-built datadriven applications.

But SQL is in fact much more than IP. Because data also gets analyzed by humans. And true to the purpose that SQL’s creators initially assigned to it, SQL is readable.

Is SQL perfect? No, but it is the language that most of us in the community know. And while there are already engineers out there working on a more natural language oriented interface, what will those systems then connect to? SQL. So there is another layer at the very top of the stack.

And that layer is us.

SQL is Back SQL is back. Not just because writing glue code to kludge together NoSQL tools is annoying. Not just because retraining workforces to learn a myriad of new languages is hard. Not just because standards can be a good thing.

But also because the world is filled with data. It surrounds us, binds us. At first, we relied on our human senses and sensory nervous systems to process it. Now our software and hardware systems are also getting smart enough to help us. And as we collect more and more data to make better sense of our world, the complexity of our systems to store, process, analyze, and visualize that data will only continue to grow as well.

Either we can live in a world of brittle systems and a million interfaces. Or we can continue to embrace SQL. And restore balance to the force.

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Gyorgy Lajtai Co-founder Lynx Analytics

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Deep Learning meets Graphs

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n today’s data driven age, huge measures of data have turned out to be accessible to decision makers. Big Data alludes to datasets that are in high volume, as well as high in assortment, speed and veracity, which makes them hard to handle for utilizing conventional tools and strategies. Because of the fast development of such data, solutions should be contemplated and provided to handle and extract value and knowledge from these datasets. Moreover, decision makers should have the capacity to gain valuable insights from such diered and quickly evolving data, ranging from day-to-day exchanges to client interactions and social network data. Graph theory is an answer to this, wherein we are able to analyze & anticipate while taking decisions easily, comparatively faster and precisely.

What once had started out as a research group of professors and students from INSEAD, who decided to form a product company to solve complex business problems with big data graphs, has today evolved into a full-scale AI and data solution company known as Lynx Analytics. With the mission to solve real, complex problems with the power of Big Data graph analytics, Lynx Analytics has built its own graph analytic technology and solution platform. Lynx Analytics honed its skills and technology with early adopters in the Telecommunications and Financial Services industries, where clients needed a radical solution to solve the constant challenge of understanding, retaining, and upselling to their customers. The company’s superior product, ideas, and brainpower

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helped them achieve profitable, organic growth ever since the start of the company in 2012.

“We started Lynx Analytics in 2010 to test out our ideas of applying graph theory to solve business problems. We saw that businesses had massive amounts of information but were not able to draw actionable insights on how to drive their business forward. This was when we decided to develop a technology that could serve this purpose,” shares Gyorgy Lajtai, Co-founder of Lynx Analytics.

Power and Scalability at its best At its core, Lynx Analytics is a graph analytics and solutions company. By the word graph, they do not refer to data charts like pie or bar charts. Neither, are they developing data visualization software like Tableau or Power BI. Lynx Analytics’ definition of “graph” comes from the mathematical graph theory, which is the study of connected data points and structure of networks.

PageRank, an internet search algorithm made famous by Google, is a perfect example of this.

“What we have done at Lynx Analytics is to take the idea of graph theory and apply modern big data technology and techniques to it. To answer your question about industry standards, our software is built on Apache Spark for its power and scalability. But it doesn’t stop there. We think what makes us

unique is that we commercialize our technology by building an entire solution with real-world use cases around it. We do this by working closely with our B2B clients, such as communication service providers, and pioneering innovative solutions based on big graphs. Our Customer Happiness Index solution is a great example of this,” Gyorgy adds.

Moreover, Lynx’s Customer Happiness Index is helping its clients reduce churn and grow ARPU (average revenue per user).

One of its clients experienced a significant drop in churn rate - from 1.3 to 1.1%, which translated to millions of USD in revenue savings. With CHI, Lynx’s clients are able to narrow down specifically on customers that matter, in areas that matter to them.

Not to forget, Lynx’ CHI is also constantly improving over time with machine learning; so over time as more data is collected, the company can deliver more accuracy in findings and recommendations.

The frontrunner leading the wave The Chief Executive Officer of Lynx Analytics and Member of the Board is Gyorgy, who co-founded Lynx Analytics. Before founding Lynx Analytics, Gyorgy worked on CRM challenges, including marketing automation and systems.

As a launch customer for Oracle’s cloud-based marketing solution, he

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helped INSEAD develop a new analytics-driven marketing and sales methodology to maintain its global number one position in executive education. Prior to INSEAD, Gyorgy co-owned GreenerOne, a Silicon Valley based online crowdsourced eco-rating company. Earlier in his career, he worked on CRM and product management for GE Capital, including Eastern Europe’s first mobile payment linked credit card with T-Mobile. Gyorgy holds an MBA from INSEAD, and a Bachelor of Arts in Business Studies from the Oxford Brookes.

Technology that solves real business problems Gyorgy believes that as an industry, Big Data solutions are commoditizing rapidly. This is true in so many ways. According to him, cloud computing and storage are becoming more affordable and accessible. Hadoop provides a standardized framework to support data analytics at a scale of billions.

Meanwhile, there will be more and more publically available data services, including open source libraries. Companies are increasingly building their own data departments and mandating data collection, protection, and analysis as a business requirement.

“At Lynx Analytics we see ourselves as a solutions company.

We want to be one of the first graph companies to build end-to-end solutions that solve real business

problems, particular in the nature of customer engagement and enlightenment. In some ways we see ourselves as a hybrid between analytic software vendor and management consulting company.

That really is what is unique about our business,” exclaims Gyorgy.

Reliable data security solutions that work Lynx Analytics offers clients a comprehensive portfolio of security solutions to ensure personal data privacy, protection against misuse or threats, and enablement of regulatory IT compliance. Its security controls are based on industry standards and includes full

data encryption, hashing, masking, fine-grained access control, auditing, and strong user authentication. Thus, when working with Lynx Analytics, clients can deploy reliable data security solutions that require no changes to existing applications, saving both time and money in the process.

The company’s clients operate in hyper-competitive markets, like telecommunications. There is a lot more consolidation, price competition, and revenue erosion from digital OTT players. Hence, if you are a communications service provider (CSP) today, you care about differentiating your services

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based on the value and customer experience. You want a speedy solution that you can plug-and-play, without the need of multiple ‘digital transformation’ projects just so you can deploy an innovative analytics solution.

“We want to become the number info communications analytics solution provider, while exploring mobile device-adjacent verticals like IoT, fintech, security, health.

All of these businesses share one common trait: massive amounts of rich customer data and a strong element of connectivity. That is the sweet spot for us,” Gyorgy concludes.

Venkata Narayanan Founder & CEO BigTapp

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In Conversation with a Newsmaker

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s the competition amongst businesses becomes more and more cut-throat across industries, the need for faster and effective decision-making capability is becoming increasingly imperative. Providing an opportunity on the same line, BigTapp provides customers with Big Data & Analytics solutions to help them make insightful decisions. In a conversation with the Venkata Narayanan, the Founder and CEO of BigTapp, we look at the journey and the vision of the company and its roadmap towards success.

What seeded the vision to create a platform that helps enterprises to create a culture of “Data driven Decisions” and “Analytics driven Actions?”

In early 2010s, what we observed was the stark difference in the ability of organizations to leverage analytics as a key competitive differentiator. Two major inhibitors were the prohibitive cost of Big Data Analytics solutions and expensive resources who can make sense out of data. We wanted to bring the barrier the down by creating a platform that provided ‘best-in-class’ analytics for industries out of the box using Cloud as the delivery platform. This enabled, say, any retailer access to ‘Walmart’ standard analytics at affordable TCO. The platform was complemented by a team of consultants who were able to guide our customers to enable the cultural shift to using data for decision making and use

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analytics for business actions that enable business objectives to be met.

Can you give us a little background on ‘BigTapp’ and where it stands today?

Over the past few years since inception, BigTapp has continuously re-invented itself by focusing ruthlessly on customer needs and staying true to our chosen areas of specialization. Over the years, we have helped a number of customers in the region democratize Analytics and move to a culture of Data Driven Decisions.

As we went through this journey, we heard customers increasingly ask us about solutions in the area of discovering customer intent: what do our customers intend to do, what are their sentiments, their interests, their relationships, etc.: all of which are towards helping reach customers with the right messaging.

That led to the birth of our InfoActiv platform: An Artificial Intelligence (AI) platform to identify customer intent by processing transactions & interactions. We enumerate customer profiles by determining personal attributes, relationships, life events, sentiments and buzz.

This helps digital enterprises in sending effective messages at scale and customise customer experience.

Brief us about the team and the building-blocks of BigTapp.

BigTapp is led by experienced leaders with over 3 decades each of

experience in the industry. Apart from me, the leadership group comprises of Co-Founder and COO Veeraraghavan, and CRO Sanjay Venkataraman.

Veera is an inventor and futurist and has been the driving force behind many of the market making technologies in the Information Management space and among his creations are the world’s first ondemand Enterprise DW&BI Impact Analysis technology and Data Integration Code Health Improvement Engine. Has led multiple teams, and built billion dollar businesses in the past. Prior to BigTapp, Veera was with Cognizant as Managing Director and Global head of their Enterprise information Management (EIM) practice.

Through his visionary leadership, Veera built and grew the EIM practice to a billion-dollar practice and achieved a phenomenal growth (CAGR of ~70%) in the past 13 years.

Sanjay is widely known for his dynamic personality, and as someone who has great enthusiasm and a gogetter attitude. An inspiring & visionary leader, he is excellent at building teams and relationships by leveraging diversity and cultivating synergies. His earlier roles include being President at Firstsource, Country Head of Dell in India, Marketing Manager at IBM and Senior Product Management role at Wipro.

BigTapp also has strong technical and management teams spread across Singapore and India. We have a

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team of experienced Data Scientists, strong Project Managers, Big Data Consultants, and multiple technical teams in the area of big Data. The research and development center is in Chennai, India, and comprises associates who work on creating and enhancing our InfoActiv platform.

What are your breakaway services in the big data bracket?

Our AI based intent discovery engine is a game changer in many industries where it is used to predict the intent of a person or organization – for e.g. which channel the customer will use in a bank.

While Data Engineering (e.g. Data Lake) is a common requirement as a hygiene factor, our breakaway services are in the areas of Big Data Analytics consulting where we craft business use cases and deliver them using our platform and/or data scientists.

Tell us about the moment when the team realized that their hard work has finally paid-off.

This occurred last year when we were selected as the 2017 TiE50 Award winner for the prestigious TiE50 Awards Program in TiE Silicon Valley’s annual tech entrepreneurship conference, TiEcon.

TiE50 Awards Program recognizes the world’s most innovative tech startups and winning this against more than 1300 tech startups from all over the world is the validation of our hard work getting paid-off.

What’s the current scenario, breakthroughs and disruptions, in the

industry? What benefits does ‘BigTapp’ provide over its competitors?

Analytics industry is getting disrupted with the wide deployment of Artificial Intelligence in many domains. In terms of delivery models, micro services from cloud vendors like Amazon is reducing the cost of analytics solutions leading to more use cases clearing the RoI hurdle. However, the recent challenges around Facebook & Cambridge Analytica as well as high profile data breeches in Facebook coupled with GDPR compliance are forcing organizations to rethink their analytics strategy. We believe that higher focus will go towards harnessing the insights from the data

within the organization (both transactions and interactions) than rushing to procure external data sources, especially social media data.

With many enterprises becoming digital enterprises, they are discovering the untapped potential within their internal data pools.

BigTapp is creating a coalition of trusted data owners who provide access to curated data that can add value to the internal data of organizations, thereby increasing the quality of insights that can be derived.

What can be expected from the company in upcoming years?

With continuing investments on our platform, we look forward to helping

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an increasing number of clients handle their customer messaging using data driven insights. We also have a strong practice that helps clients solve specific business problems e.g. Optimizing Car Park Occupancy for a real-estate major, Re-engineering Menus for a large restaurant chain, discovering Customer Sentiment for a number of clients, etc.

We also intend expanding into other geographies. While we have a number of clients in the South East Asian region, we intend expanding our footprint into India and the United States shortly.

Dr. Cindy Gordon Founder & CEO SalesChoice

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Helping See More to Win More A

t present, human attention has dropped by 50% over the past ten years ever since the advent of mobile phones.

Additionally, the productivity of Sales Professionals has plummeted due to the emergence of the Age of Distraction.

As a former Senior Xerox Sales Director and General Manager (GM), Cindy became very concerned with the declining attention span of B2B Sales Professionals, as their productivity has plummeted to less than 33% in front of what is really important – real customer conversations. This is further exacerbated as 30% of the sales professionals suffer from attention deficit disorder. Hence, their ability to focus is less than 8 seconds and as a result, the average B2B company has only30-60% of sales professionals making their sales plans. Hence, losing trillions of top-line revenue growth is impacted due to productivity impacts and the decline of cognitive “focus and critical thinking” skills.

Cindy saw a gap in the market that could help guide sales professionals to find the best opportunity to sell to, i.e. leveraging historical data insights using Artificial Intelligence (AI). Her vision of using AI and Cognitive Science methods has prompted her to build a powerful SaaS AI mobile platform that guides customers to the best win destinations, with high value insights to increase sales professionals success odds up to 95% accuracy.

Our vision is to be a unicorn in AI-guided selling – helping our customers See More to Win More, transparently and ethically.

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A one of a kind service provider SalesChoice is an AI/Cognitive Sciences SaaS provider, with a registered Salesforce AppExchange solution and is Canada’s first to employ Artificial Intelligence in B2B sales for guided insights. The company’s AI/ML Insight EngineTM analyzes CRM dataset(s) (Inside and outside the CRM) for sales professionals (including Managers and Sales Reps) to predict sales outcomes with up to 95% accuracy.

The company uses Guided AI Selling methods to help B2B sales teams save 20-30% on cost of sales and increase their win rates by 10-15% by focusing on the right opportunities and taking corrective actions to increase their odds of hitting their number.

SalesChoice is the only AI guided selling SaaS platform which easily explains the rationale behind its conclusions. SalesChoice practices EXPLAINABLE AI or TRANSPARENT AI vs. BLACK BOX AI. The company also has a DSaaS (Data Science as a Services) Professional Services offering and has a strong go-to-market relationship with Salesforce Platinum SI Partners, Accenture, and RelationEdge (recently acquired by RackSpace). SalesChoice has developed Cognitive Sciences & AI products which can analyze complex and large CRM datasets to predict sales outcomes and provide insights on the reasons for the win or loss of a deal, 12 months before the occurrence of any outcome, and has an open API, and has successful integrations at this stage with SalesForce, and NetSuite (Oracle).

A step ahead of competitors SalesChoice is a SaaS platform in the domain of AI and Cognitive sciences, and the company specializes in predictive and prescriptive analytics for sales professionals. The company is a certified ISV partner of Salesforce and is registered on the SalesForce AppExchange. There are very few competitors, including Salesforce, that have developed an AI platform that automatically configures and analyzes customized datasets.

Speaking about the competitive advantages of SalesChoice, Cindy mentions “Our first competitive advantage is that we can achieve up to 95% accuracy in our predictive scores, and can easily scale up to handle mid-market to large enterprise data sets, and have proven this out with our current client base.

We can work on Small Businesses as well, but they need sufficient data volume, at least 2000 historical wins or losses. Our second competitive advantage is that we are the only company that does not treat AIdriven actionable insights as a black box. We explain the reasons for predicted wins/losses, as well as data completeness to allow clients complete visibility into how they can improve their odds of winning a deal.

We are a unique company as we are the only company that provides a Playbook and a Dealbook with enriched guided selling, which is 100% derived from AI. Thirdly, we have filed a comprehensive patent with Norton Rose in Fall 2012, well ahead of this market segment of AI and guided selling. We have further advanced by providing AI-guided selling coaching insights to further

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extend our value to our customers.”

See More to Win More!

The vision for SalesChoice is to become the unicorn of predictive and prescriptive sales analytics cloud company, making data speak, to help B2B Sales Professionals – simply See More to Win More!

“We will continue our commitment to Explainable AI and advancing our product innovation to integrate more data connectors to be able to build out more sales relevant use cases,” Cindy claims. In particular, the company has already done some early ideation with Alexa on voice chat using scenarios related to the functionality in our app, such as “What are my top three coaching points for Susan to make her quarter?” or “Is Susan going to make her quarter?”

SalesChoice views voice to bring in the next killer application for sales professionals in CRM, however, the data is needed to make smarter and more informed predictions with AI. Speaking of which Cindy mentions, “we will continue to extend our data quality and completeness app layer giving management and reps the insights on the health of their Data.

Otherwise, AI is running on a dirt road with many ruts vs gliding on a well-paved road with occasional potholes that quickly get fixed. We will continue to be a leader in Guided Selling, with a continued commitment to no extra keystrokes, easy navigation, and insights that improve the productivity of sales professionals. We will also be integrated with other leading CRMs beyond Salesforce, including

Oracle/Netsuite, and Microsoft Dynamics, as our company matures.”

A leader with experience Cindy Gordon, the Founder and CEO of SalesChoice, is one of the pioneering individuals who has shouldered the company to what it is known as today. She is an expert in SaaS, AI, business innovation, earlystage software commercialization, and sales & marketing practices. She has held senior leadership and partnership roles in global B2B Enterprises, including Accenture, Xerox, Citicorp, and Nortel Networks. In addition, Cindy has also been a founder, VC, an angel investor, and founder of emerging software companies. She is currently involved with: Corent Technology, CoursePeer, Kula, LyfeUp, and TouchTown TV. With over 13 books

in the market, and a new one in labs: The AI Shift, she is globally recognized for her innovation and thought leadership in: Innovation, SaaS, Collaboration, AI, and Technology Emergence. Cindy’s passion revolves around the constant pursuit of sustainable innovation and making differentiated experiences to make our world an incredible place.

The showers of accomplishment Time and again, the team at SalesChoice have experienced several moments of accomplishment, including the most recent one where the company was awarded the AI Disruptor in Canada 2018, award by IT World Canada and presented by the President of Microsoft Canada.

Speaking about these achievements, Cindy mentions “The achievements

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in terms of R&D Innovation, Customer Case Validation in both Canada and the USA are simply due to an incredible team that is passionate about improving B2B sales productivity gaps and helping Canada achieve its AI Supercluster Status.”

To learn more about SalesChoice, please book a demo at www.saleschoice.com. Dr. Cindy Gordon, CEO and Founder, can be reached directly at cindy@saleschoice.com

Guru Moorthi CEO Virtual Tech Gurus

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Navigating the Technology World Made Easy

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oday, there are multiple opportunities to develop dynamic standards that ensure your technology supports rapid business growth via automation, consolidation, and standardization – all the while improving the operational expenses. It becomes important for IT businesses to change the way they think about their business’ IT infrastructure and align their business and IT with scalable solutions delivered by experts.

Enters Virtual Tech Gurus with a team of IT professionals that brings-forward enterprise-level expertise with deep knowledge to give small- to medium-sized businesses access to the best talent and premier technology. Virtual Tech Gurus is perfectly aligned to assist its customers with their cloud services requirements with its expertise, including developing solid solutions around Managed IT Services, Cloud Infrastructure, including private, public and hybrid cloud offerings and Zero Downtime Data migration.

The technical gurus of the company work in unison with the clients to gather crucial requirements, design, implementations, and support solutions based on emerging Virtual and Cloud Technologies. VTG knows that providing the best possible solutions at an affordable cost equals instant success for its customers. The team collaborates with clients to gather crucial requirements and knows that providing the best possible solutions at an affordable cost equals rapid results.

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Bringing quality and automated analytics solution to the industry VTG saw a big vacuum in automation, data analytics in the infrastructure. VTG’s goal was to bring quality and automated analytics solution to meet customer demand and with that goal, VTG was founded back in 2008.

Today, Virtual Tech Gurus (VTG) is an IT Service Provider company headquartered in Dallas, Texas, with offices across the United States and India having core competencies in Cloud Services, Data Center Migration, Cloud Migration, Infrastructure Assessment, and Staffing. VTG is partnered with leading players in cloud and data center solutions like EMC, NetApp, Hitachi, VMware, and other renowned companies.

An Elite Partner of Dell EMC for over nine years, VTG has received the coveted 2017 Best Customer Experience Award for its leadership in Intelligent Data Mobility (IDM) area and expertise in the relationship-building prowess.

VTG is also the winner of the SMU Cox School of Business, Dallas 100 Fastest Growing Company Awards (#10).

One of the most trusted partners to the frontrunners Today, VTG proudly stands as one of the leading providers of cloud, infrastructure, migrations, and data center solutions and has also made a name for itself as one of the most trusted partners to the frontrunners

in the business. The company’s services are briefly explained as follow:

Collection, Processing and Data Generation, Reporting and Automation.

Data Center Migration: Virtual Tech Gurus help clients migrate applications and workloads to new platforms quickly and easily, with minimal downtime. VTG’s skilled project team helps them plan and implement their migration with an end-to-end service. VTG works with their team at every stage from the opportunity identification, initial assessment, and implementation to ongoing management. The service is suitable for storage, server, application, or exchange migration, and migration to the cloud. VTG uses its patented automation tool, ZENfra, to ensure a successful endto-end transition. This helps lower costs by up to 25 percent and reduce migration times by 30 to 40 percent compared to traditional inhouse methods.

Cloud Automation: VTG’s skilled project team helps clients plan and implement their migration with an end-to-end service. VTG works with their team at every stage from the opportunity identification, initial assessment, and implementation phase to ongoing management. The service is suitable for storage, server, application, or exchange migration, and migration to the cloud. VTG uses its patented automation tool ZENfra to ensure a successful endto-end transition.

ZENfra™: VTG’s automation tool ZENfra manages and monitors migration projects to cut lead times by 30 to 40 percent and reduces the cost by 25 percent compared to traditional in-house methods.

ZENfra integrates initial assessment, development of a strategic migration plan, and premigration to ensure a successful end-to-end transition. By automating the collection of data from log files, ZENfra eliminates the complexity of data capture and reduces the risk of human error.

The ZENfra process includes Data

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DevOps: VTG believes in balancing and integrating improvement strategies across people, process, and tools to create meaningful business results such as keeping your team up to date on cutting-edge practices, utilizing a system level approach in process design and creating a prioritized roadmap and tactical project plan.

At VTG, the team sees DevOps as an Enterprise Architecture Framework that allows seamless communication between development and operations team to deliver highly available and secure infrastructure on time.

A look into the future VTG’s proven processes and best practices, coupled with its innovative automation tools such as ZENfra, allows the company to complete the migration process

with less downtime and significant cost savings over its competitors. “We are transforming our company to more solutions and IP based from purely consulting. We are able to add a significant number for fortune 500 customers to use our products, looking to increase our adoption and add more data-analytics towards containers,” says Guru Moorthi, Chief Executive Officer of Virtual Tech Gurus.

Notable client testimonials “Brinder, Thanks for all you do and your incredible sense of pride and tenacity to get this thing to move forward with Capital One while balancing everything else. I do not have the words to say thank you enough, so I hope this will say it all.

Congratulations and please keep up the great work!” – Software Engineer, EMC Corporation.

“There was nothing in the process that we thought could have been improved, but we were proven wrong.

We were extremely happy with the way the project was managed.” – Major Motorcycle Manufacturer.

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