Monthly Archives: October 2017

Lessons from the Sentient Enterprise: To Scale Your Analytics, “Merchandise” the Insights

October 30, 2017


“Lessons from the Sentient Enterprise” is a series of posts timed around the publication of “The Sentient Enterprise”, a new book on big data analytics by Oliver Ratzesberger and Mohan Sawhney. Each post in the series highlights a major theme covered in book and at executive workshops being held in conjunction with its upcoming release by Wiley publishing.

One of the bedrock principles Mohan Sawhney and I put forth in “The Sentient Enterprise” is that more data is only as good as your ability to keep up and leverage it for insight. It’s a sentiment shared by many of the top analytics leaders we interviewed for the book. As Jacek Becla, a former data executive at Stanford University’s prestigious SLAC National Accelerator Laboratory and current Teradata vice president of technology and innovation, told us analytics don’t progress unless there’s a “symbiotic relationship between capacity and skills.”

Capacity, unfortunately, can easily outpace our skills in managing it. In fact, our book focuses on several “pain points” of data drift, duplication and error — side-effects of poorly governed capacity that can leave people swimming in oceans of data, without much insight to be found. These problems get more critical as you try to scale the operation.

A ‘Forcing Function’ for Agility

Dell Vice President for Enterprise Services Jennifer Felch and her colleagues learned this first-hand as they worked to aggregate global manufacturing data into one master environment for reporting and analytics. “Scaling is the forcing function for standardizing and becoming as efficient and accurate as possible with your data,” she told us. As we describe in the book, Dell’s solution involved setting up “virtual data marts” — more than two dozen specialized data labs that access, but do not corrupt, the master environment.

The virtual data mart is a feature of the Agile Data Platform, the first of five stages in the Sentient Enterprise journey. That’s where we “decompose” data into architectures that preserve its most granular form, so data’s more malleable and adaptable to various business needs across the organization. The next couple stages — the Behavioral Data Platform and Collaborative Ideation Platform — are where build capacity and set up a social-media style “LinkedIn for Analytics” environment for business users to share insights from this newly agile data.

But sharing insights is not the same as prioritizing them. And here’s where I’d like to emphasize a concept from our book that’s surprisingly simple, yet still underutilized in most businesses today: The key is to not just socialize data insights among business users, but to “merchandise” them!

‘Merchandising’ the Value of Data

What do I mean by “merchandising” analytic insights? Think of how we shop on Amazon, eBay or any other major e-commerce site. We search, we promote, we recommend, we follow. All that activity is tracked by analytics, such as eBay’s “Customer DNA” database — which we examine in the book — that can follow patterns of browsing, bidding and other indicators of value amid some 800 million concurrent auction listings. Over time, analytics running underneath learn what’s important in order to tailor searches and increase the relevance of product recommendations.

In the Sentient Enterprise, we’re essentially doing the same thing with data and analytics. We’re applying the same form of merchandising to the analytics network within an enterprise — promoting and recommending questions, people, and answers that a data scientist or business user might be interested in based on previous queries and activity.

Particularly at scale, there really is no other way to go about it. That’s because — as the book explains — we’re carrying forward the merchandising process beyond just data insights, and applying it to the valuation of entire prepackaged workflows (in the Stage 4 Analytical Application Platform) and self-decisioning algorithms (in the Stage 5 Autonomous Decisioning Platform). I’m covering a lot of ground here, which is why I invite you learn more about the Sentient Enterprise through online resources and, of course, the book itself!

I’m hoping it’s already clear, however, that scaling requires the absolute commitment to rethink old habits — such as extract, transform, load (ETL) and centralized metadata — and embrace new, scalable ways of listening to data and positioning algorithms for “wisdom-of-the-crowd” insights. That’s because, as we we’re fond of saying, humans don’t scale the way data does — and a hundred or even a thousand analysts will remain outgunned without some way to automatically “merchandise” insights from the huge volumes of lightning-fast data streams coming at them.

Simplicity out of Complexity: Announcing the Teradata Analytics Platform

October 23, 2017

Businesswoman inspecting graph on interactive display

What is the sense in transformative technology if only a select few can use it?

That has been a big conundrum for enterprises working with increasingly powerful analytics capabilities, from data science to machine learning and AI. As these technologies have matured, they have created a complex web of siloed data and analytics, some on premises, some in the cloud, some structured, some not. And that complex web of architecture and tools has also made it difficult for enterprises to scale analytics capabilities across the organization.

Yes, there are great open-source tools out there, like Python, R, Spark or TensorFlow, enabling analytics, machine learning and deep learning. And there are more enterprise-grade data and analytics tools on the market today than ever before. But using the latest technologies at scale, across a hybrid cloud environment, isn’t simple. It’s just not feasible for large enterprises — the businesses most likely to have a complex web of data — to continually knock on the door of  their IT department or data scientist, asking them over and over again to custom-build analytics applications that integrate disparate data across disparate platforms. At Teradata, we realize that these siloed data set — and disparate abilities within the enterprise to manage and interpret data — are at the crux of one of the biggest challenges enterprises have to overcome. That’s lost time no one can afford to waste. And that means performing analytics and machine learning in all aspects of the business is a nonstarter.

This gap must be closed and cutting-edge analytics capability must be simple and accessible across the organization. This is at the center of our sentient enterprise vision, which I codeveloped by Mohan Sawhney, noted academic, author and management consultant. In our comprehensive book we outline how the “The Sentient Enterprise” model gives businesses a guide to surviving the evolution of analytics and AI. With this announcement, we continue to drive toward the vision of the sentient enterprise through a series of Teradata technologies.

Last year, Teradata tackled the architecture problem, releasing Teradata Everywhere, the world’s most powerful analytics database, enabling massively parallel processing on multiple public clouds, managed clouds and on-premises environments. This gave companies a flexible data management layer that allowed them to focus on analytic applications.

Teradata Analytics Platform Delivers Superior Insights

Today, we’re announcing the next step — the Teradata Analytics Platform. With this platform, enterprises can keep their current analytics tools, write in the coding languages they prefer, and apply analytics to all their data quickly, regardless of location.

Our vision is clear. With the Teradata Analytics Platform, businesses can use the latest open-source technologies and their prefered analytics tools to perform both widespread and granular data analysis, from looking at customer purchasing trends down to the purchase path of a single individual. And this solution can be deployed anywhere, from the Teradata Cloud to public clouds, as well as on-premises. Teradata Analytics Platform is comprised of the best analytic functions, the leading analytic engines, the industry’s preferred analytic tools and languages, and support for a wealth of data types. First, Teradata Analytics Platform will integrate Teradata and Aster technology, allowing data scientists and analysts to execute a wide variety of advanced techniques to prepare and analyze data within a single workflow, at speed and at scale. In the near future, the Teradata Analytics Platform will include Spark and TensorFlow engines to provide quick and easy access to a full range of algorithms, including those for artificial intelligence and deep learning.

Teradata Analytics Platform further closes the loop on providing analytics power to everyone in a business through its integration with the Teradata AppCenter, which lets analysts share analytics applications and capabilities with their fellow employees through a web-based interface, enabling self-service access to all users within an enterprise.

As businesses and their data grow more complex, they have struggled to find an enterprise-grade solution that enables analytics with simplicity, agility and scale. This announcement fits into our vision of bringing about a shift in the market, by breaking data analytics free of its location and coupling it with the latest open-source and proprietary technologies that empowers analytics for everything.

For more, read the announcements from our PARTNERS 2017 conference.

The Sentient Enterprise. Why Another Book on Analytics?

October 17, 2017

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There are already so many books about big data on the market. That’s why, when management professor Mohan Sawhney and I decided to write The Sentient Enterprise, it wasn’t to fill more shelf space – it was to fill a void: We wrote our book for the same reason you’d create a road map for a highway that somebody’s already busy racing along at top speed.

Such is the reality for organizations being disrupted today by big data: That highway is the ongoing digital transformation. And the driver is you – the executive – racing into the unknown with little room for error and no time to lose on a data-driven journey that rewards agility and runs mediocrity off the road.

Under these conditions, you can’t afford a road map – or a book – that gets distracted by scenery or tangents. That’s why The Sentient Enterprise goes straight to the heart of the urgent challenge facing most large businesses today: As the digital age forces every organization to undergo a transformation through technologies like artificial intelligence and cloud computing, The Sentient Enterprise is a blueprint for the powerful analytics capabilities needed to pull off that transformation at scale.

My road map analogy, but the way, was inspired by what the CIO of a large global car manufacturer told me recently when I discussed the concept of The Sentient Enterprise with him in a half-day session. “You have just given me my roadmap for the next 7+ years,” he said.

A Path for Action…TODAY

The Sentient Enterprise is indeed a bold vision into the future of analytics, automation and AI in an increasingly data-centric world. Executives must act today if they’re going to survive what the data will demand of them tomorrow. Most urgently, they need to harvest insights lightning fast and at scale; they need seamless collaboration across a data-literate workforce; and they must have decision-making so proactive and automated that the organization approaches an end-state of self-awareness and even “sentience.”

We built this book and the capability framework around The Sentient Enterprise to stay laser focused on that end-state – our North Star destination – of having the company act as a single organism. And like all good road maps, we chart the most efficient and practical steps for getting there. The path to maturation involves a revolutionary method for preserving the granular agility of data, even as increasingly complicated analytic processes get applied to such data.

We take the reader on a layered, five-stage journey through advanced “platforms” of capabilities: the Agile Data Platform, Behavioral Data Platform, Collaborative Ideation Platform, Analytical Application Platform and Autonomous Decisioning Platform. As we move through these platforms, we are building a more data-literate culture and breaking down historic barriers to business agility – including organizational silos, data duplication and errors and broken processes – that plague many legacy technologies and even newer architectures that mismanage analytic capabilities.

A Radical Transformation of People, Processes and Technology

With this layered, five-stage journey through progressively more advanced platforms of capabilities, The Sentient Enterprise creates agile enterprises that intuitively connect people, processes, data and outcomes. Keep in mind that by “platform,” we don’t imply a specific hardware or software platform, but rather a realized organizational capability made up of people, processes and technology working seamlessly together.

It’s been a long and enlightening road since Mohan and I first shared our mutual perspectives to develop The Sentient Enterprise concept in 2013, at a dinner meeting near his offices at Northwestern University’s Kellogg School of Management. We’ve since refined the model and tied it to real-world use cases from companies like Wells Fargo, Verizon, Dell, Siemens and General Motors, just to name a few. So why take things even further by writing a whole book? Why spend these past several years researching, writing and interviewing leading experts on the forefront of enterprise analytics?

The answer is we wanted to put concepts of data agility in the hands not just of top executives, but any business user who interacts with data and wants to improve that interaction. Innovation happens wherever there are the insights, tools and the motivation to make things better. Our book seeks to put all three – insights, tools and motivation – into the hands of as many people as possible. You don’t have to be an organizational leader, in other words, to be an innovator.

Think of The Sentient Enterprise is a lens or framework that helps make sense of both the analytics challenges and remedies amid a big data “Mount Everest.” The Sentient Enterprise is your toolkit and tactical grid overlaid onto that map of the mountain – something you keep returning to for reference as you chart your course and plan your ascent. And as you do that, check in with us at and to keep us posted on your progress #SentientEnterprise! The book is available to purchase in many stores – you can get a copy here.


Oliver Ratzesberger is an accomplished practitioner, thought leader and popular speaker on using data and analytics to create competitive advantage – known as his Sentient Enterprise vision.

Oliver Ratzesberger is executive vice president and chief product officer for Teradata, reporting to Vic Lund. Oliver leads Teradata’s world-class research and development organization and provides strategic direction for all research and development related to Teradata Database, integrated data warehousing, big data analytics, and associated solutions.

Lessons from the Sentient Enterprise: Three Big Predictions from the Pros

October 2, 2017


“Lessons from the Sentient Enterprise” is a series of posts timed around the publication of The Sentient Enterprise, a new book on big data analytics by Oliver Ratzesberger and Mohan Sawhney. Each post in the series highlights a major theme covered in book and at executive workshops being held in conjunction with its upcoming release by Wiley Publishers.

Analytics is progressing so quickly that the future is less about consulting a crystal ball than simply reviewing the latest predictions from Gartner, Forrester and other industry analysts.  I’ve even done my own forecasting on where analytics and artificial intelligence are taking us in the years to come.  In writing The Sentient Enterprise, however, Kellogg School of Management professor Mohan Sawhney and I saw an additional opportunity to get predictions from the more than dozen top analytics executives we interviewed for the book.

Though we talked with each about different parts of the journey toward enterprise-wide “sentience” – including Dell’s Virtual Data Mart for global manufacturing data, Siemens Mobility’s use of a Collaborative Ideation Platform for train maintenance and Wells Fargo’s companywide deployment of an Analytical Application Platform – there’s one question we made sure to ask everyone: Where is analytics headed over the next ten years?  We then compiled their predictions in a special “Looking to the Future” section of the book.  By way of sharing a few highlights, here are three big predictions that stood out in our series of interviews:

Preventing vs. Fixing Problems

More than one executive flagged the CRM trend away from problem solving, and toward problem prevention.  “For me, the future is about getting to a stage where, before customers know there’s a problem or issue, we will be able to proactively offer to meet their needs,” said Grace Hwang, executive director for business intelligence and advanced analytics at Verizon Wireless. “Tomorrow is about being proactive to the point where both the company and the customer stay ahead of the curve… avoiding surprises or problems before they happen.”

“The new world is not going to be a big giant call center waiting for people to call and report problems,” echoed Dell Vice President for Enterprise Services Jennifer Felch. “We’ll be asking ourselves fewer questions like ‘How fast was my response to the call?’ or ‘How quickly did I resolve the issue?’ That’s the legacy world. In the new world, you’re looking at ‘Why didn’t I stop that problem before it started?’…. Instead of measuring problems, we’re measuring success.”

Expanding Collaboration Beyond the Company Walls

Other executives foresee an expansion of seamless data sharing to a broader community that will include customers, vendors, supplier groups and other partners.  Over the next decade, for instance, Siemens Mobility plans to expand its version of a Collaborative Ideation Platform for customers of its locomotives and rail infrastructure. “From our end we can add engineering understanding to clarify insights … From their end, customers provide information on how they run their operations,” Siemens Director of Data Services Gerhard Kress told us. “Together, we’re jointly creating something that’s much bigger than what any of us could do on our own.”

“I think in the not too distant future, we’ll start to see organizations like ours address the extended enterprise, not just the internal enterprise,” added General Motors Director of Big Data Infrastructure and Engineering Brett Vermette. “As we see organizations become more agile and data-driven themselves, a further frontier will offer more seamless coordination more broadly with their larger environment … That’s another order of magnitude of complexity that goes beyond just the enterprise itself.”

More Autonomous Decisioning

Decision support and fully-automated decisioning are capabilities we discuss in The Sentient Enterprise that will continue to mature.  Over the next decade, Dell’s Jennifer Felch predicts that “better capabilities to automate routine tasks and decisions will free up more people and resources (for) opportunities and growth versus problems and troubleshooting.” Meanwhile, Volvo Senior Director of Business Analytics Jan Wassen forecasts tremendous progress in the automation that goes into self-driving cars.

“All the testing and problem solving that’s happening today will get us to the point where autonomous drive will be an ordinary reality, simply a fact of life,” Jan predicted.

“In fact, I believe the day will come when we’re not allowed to drive ourselves any longer. Perhaps not every road, but for some major roads, you’ll see autonomous drive as a requirement.”

Beyond these particular predictions, an overall consensus that emerged among our interviewees is summarized nicely by something told to us by A. Charles Thomas. We interviewed him while he was chief data officer at Wells Fargo, before he left to join General Motors in that same role. “Over time, you’ll see more situations and contexts where access and curiosity around data are making a difference,” he said. “I ultimately consider the chief data officer’s charter to be anywhere we can inform business decisions at scale, and I think the future will show how that footprint expands into more and more lines of business.”