The Sentient Enterprise

The Evolution of Business Decision Making

Oliver Ratzesberger, Mohanbir Sawhney

• Publisher: Wiley
• Year: 2017
• Print ISBN:
  9781119438861
• Edition: 1
• eBook ISBN:
  9781119438793
• Topic: Business
• Subtopic: Information Management
• Index: Business

Mohan and Oliver have been very fortunate to have intimate views into the data challenges that face the largest organizations and institutions across every possible industry—and what they have been hearing about for some time is how the business needs to use data and analytics to their advantage. They continually hear the same issues, such as:

• We're spending valuable meeting time wondering why everyone's data doesn't match up.
• We can't leverage our economies of scale while remaining agile with data.
• We need self-serve apps that let the enterprise experiment with data and accelerate the development process.
• We need to get on a more predictive curve to ensure long-term success.

To really address the data concerns of today's enterprise, they wanted to find a way to help enterprises achieve the success they seek. Not as a prescriptive process—but a methodology to become agile and leverage data and analytics to drive a competitive advantage.

You know, it's amazing what can happen when two people with very different perspectives get together to solve a big problem. This evolutionary guide resulted from the a-ha moment between these two influencers at the top of their fields—one, an academic researcher and consultant, and the other, a longtime analytics practitioner and chief product officer at Teradata. Together, they created a powerful framework every type of business can use to connect analytic power, business practices, and human dynamics in ways that can transform what is currently possible.

CHAPTER 1
Reimagining the Enterprise

The Stained Glass Bistro in Evanston, Illinois, is a bustling wine bar with impressive wine flights and a one-of-a-kind cheese and charcuterie plate. Nonetheless, your coauthors weren’t paying much attention to the ambience during our dinner there together on November 12, 2013. By then, we were immersed in an animated conversation—begun hours before in Mohan’s office, just a short walk away at the Kellogg School of Management—about the startling evolution now underway for analytics in large enterprises.

Introduced by our mutual friend Mary Gros, we had come together as two veteran technology professionals with decades of experience under our collective belts, trying to chart our industry from two very different perspectives: Mohan as an academic researcher, consultant, and technology company board member; Oliver as a longtime analytics practitioner and executive at major companies like eBay, Sears, and now Teradata Corporation.

As the ideas and the wine continued to flow around the table, we came to realize how our different perspectives were like complementary puzzle pieces that when fit together suddenly painted a clear picture of the journey that a data-driven enterprise needs to undertake. We began to see that rapid developments in technology and the explosion of data are now transforming the very nature of large enterprises—and that maturing analytics capabilities are the key to future survival.

Our insights that cold Tuesday in November were as enduring as they were sudden. Even the calendar date, 11/12/13, seemed auspicious as we began our own step-by-step journey in developing a capability maturity model for large data-driven companies, and putting it together in the book you’re now reading. As you will see in the pages that follow, we’re on an evolution toward an end state—a journey every big company should take, but only a few brave ones have started—that we call the “Sentient Enterprise.”

We’ve since filled in the details of the five stages—the Agile Data Platform, the Behavioral Data Platform, the Collaborative Ideation Platform, the Analytical Application Platform, and the Autonomous Decisioning Platform—but everything is based on the insights sparked during that first meeting at the Stained Glass Bistro. Even the initial “Sentient Enterprise” term that we came up with over our dinner has endured: As we talked through the need to make decisions in real time and at the speed of data, with the enterprise ingesting information and using algorithms to make the bulk of decisions on its own, Mohan observed how such an enterprise was almost like an organism, a sentient organism . . . a “Sentient Enterprise.”

The name stuck because it is persuasive not just as the title of a maturity model or a book, but because it summarizes the end state our whole analytics journey is leading us toward: the Sentient Enterprise is the North Star that every large business should aspire to as it struggles to make decisions at the speed of data.

DISRUPTION AND DECISION MAKING

Henry Ford is famous for reportedly (though not definitively) having said, “If I had asked people what they wanted, they would have said faster horses.” By choosing instead to create the Model T automobile in 1908 and introduce the assembly line approach to production, he disrupted and redefined an entire mode of transportation. A century later, Steve Jobs fostered much the same attitude and outcome in developing the Apple iPhone, an instant and total game changer in how we view phones and mobile capabilities overall.

These are prime examples of disruptive innovation, a term coined only in 1995 but already a fixture in modern business theory and practice. Today, disregarding or deconstructing the status quo is embraced in countless business plans. Entrepreneurs are mounting wholesale reworkings of entire industries and product lines. Many investors put their money on the disruptive playbook, favoring the revolutionary over the incremental.

Serial disrupter Elon Musk is legendary for upending e-commerce with the 1998 advent of PayPal. Five years later, he echoed Henry Ford’s makeover of the auto industry by founding Tesla Motors. From design and manufacturing to service and operation, Tesla’s electric, software-intensive vehicles have redefined what the automobile can be today. (Full disclosure: both of us are proud Tesla owners and big fans of the company and its approach.) In a further instance of disruption, Tesla even released all of its patent holdings in 2014 in the belief that open-source innovation can accomplish more than any single company can achieve with its own proprietary ideas.

By definition, disruptive innovation displaces companies and sectors that remain vested in the status quo. There will always be losers. Borders Books and RadioShack, for example, are two retailers that failed to straddle the online/brick-and-mortar divide with seamless, multichannel customer engagement models. They learned the hard way how disruption and bankruptcy share the same linguistic root.

Modern analytics, however, raises the stakes and brings disruption to another scale altogether. As data-driven becomes the norm across all industries, we’re no longer just facing obsolescence of particular products, sectors, or services; we’re now seeing the extinction of fundamental business models that most major companies have been founded on.

Indeed, big data and the new analytic capabilities that go along with it are changing everything from how large enterprises structure and finance operations to how they pursue opportunities and engage their workforce. And analytics can revolutionize an organization’s ability to listen to data sources, understand what the data is saying, and use it to make informed decisions in near real time.

SELF-DISRUPTION AT CISCO: ON PURPOSE AND AT SCALE

Foundational writings on disruption are required reading for anyone in business today. So embedded is the concept, in fact, that the question is no longer how and why disruption happens, but who the winners and losers are. That has given rise to a second wave of insight around the mantra to “disrupt or be disrupted” (a very large wave indeed, judging by the 14 million search results when we recently Googled the term).

Consider the case of Cisco Systems, a hugely successful global networking company that includes more than 70,000 employees and 240,000 industry partners. Some 80 percent of the world’s networking traffic crosses Cisco infrastructure at some point in time; the company consistently ranks number one or two in every market where it competes. Still, Cisco is pursuing an aggressive and company-wide self-disruption effort as if its survival depends on it. That’s because it does!

We recently caught up with Kevin Bandy, senior vice president and chief digital officer for Cisco Systems. As he shared Cisco’s self-imposed transformation from a hardware-intensive model to a software- and consumption-based model focused on recurring revenue, Kevin explained that the company is not racing so much against competitors, but against the future needs of its own customers.

“Business models are changing every 18 to 24 months with Moore’s law,” he told us. “Our trigger to change was the voice of our customers and the forward visibility they expect us to have when it comes to innovation and how they’ll be consuming it in the future.”

Marathon runners know to hydrate before they get thirsty. The same can be said of companies needing to self-disrupt before they get desperate. “Rather than let someone else disrupt us, we chose to disrupt ourselves,” Kevin explained. “That’s especially critical with the operational level we’re at; 80 percent of global networking traffic is too important to let fail.”

SELF-DISRUPT IN SUSTAINABLE WAYS

When you’re a small start-up, disruption is like intellectual Red Bull that powers you through a few market cycles. You’re agile because you’re small. And you can risk a huge crash because, given how 90 percent of start-ups fail within a few years, long-term survival is mostly an abstraction.

Large enterprises, with an ecosystem of customers relying on them, can’t afford to think this way—but neither can they afford to sit still. To thread the needle, big companies like Cisco are fostering disruption and entrepreneurialism within the context of sustainable and scalable models. You’re building a digital operating model of people, processes, behaviors, and competencies in the spirit of what the Wharton School’s Eric Clemons calls an “all-pervasive” approach to disruption across the “structure and strategy of the entire business.”

Unless you’re constantly anticipating tomorrow, even today’s biggest successes will always be on borrowed time. This is especially true in analytics, where clients may be buying not just a product but an entire digital environment that their whole business relies on.

“Think about the logic of Moore’s law, and the reality that corporate timetables for standing up innovation at scale can be 18 to 24 months,” explained Kevin. “If it’s only then that we realize we stood up the wrong solution, we can spend another two years of unraveling and rebuilding. That whole cycle counts for eons on the technology clock—plenty of time to put yourself and your customers out of business if you make the wrong call.”

Cisco’s story shows how, especially for large enterprises that serve in a trusted adviser role, getting ahead of disruption is a make-or-break proposition. Whatever customers think of you now, they’ll abandon you—or go out of business along with you—if you’re not there with the right innovations needed for tomorrow.

“The further along you go on this Sentient Enterprise maturity model, you encounter the challenge of people relying on you,” echoes Brett Vermette, director of big data infrastructure and platform engineering at General Motors. “Delight becomes demand. Experiment becomes expectation.”

We interviewed Brett about GM’s own proactive transformation to consolidate what turned out to be hundreds of disconnected data marts into a more unified and agile environment. “We had an intensive, six-week period in early 2013, launching our enterprise data warehouse program,” he told us. “This was a major transformation program, including installation of 60 crates of infrastructure in our data center, building the foundation for a global data warehouse, and consolidation of more than 200 siloed repositories and data marts over time.

“As part of our IT transformation, GM hired thousands of new college graduates and experienced IT professionals to handle work previously done by third-party suppliers,” Brett said. “We were fortunate to have top leadership support; the challenge was more how to mobilize company-wide in ways that remained agile and innovative. It’s like fighting the agility war on all fronts.” In Chapter 3, we’ll take a closer look at how GM achieved success in this effort.

ANALYTIC PAIN POINTS AND A SELF-SERVICE REVOLUTION

GM is a manufacturing company with roots going back more than a century. The fact that such a legacy institution would embrace analytics shows just how fully data has penetrated all markets and industries. Indeed, financial implications for this global tsunami of digitized information are so important that the World Economic Forum has now designated big data as a new kind of economic asset, just like currency or gold.

A study by the MIT Center for Digital Business, meanwhile, is representative of many in showing how data-driven businesses do indeed have the edge. That survey of 330 leading U.S. businesses showed companies that focused strongly on data-driven decision making had an average of four percentage points higher productivity and six percentage points higher profits overall.

Many businesses nonetheless struggle through a combination of huge data volumes and organizational hurdles that creates analytic pain points familiar to any data scientist who works at scale. For one thing, we spend the majority of our time just sifting through data instead of making decisions. We’re constantly on our heels in reaction mode, putting out fires instead of charting the future. We can’t seem to make decisions...

Table of contents

• Cover
• Title Page
• Copyright
• Foreword to The Sentient Enterprise
• Introduction
• CHAPTER 1 Reimagining the Enterprise
• CHAPTER 2 Leveraging an Expanding Universe of Data
• CHAPTER 3 The Agile Data Platform
• CHAPTER 4 The Behavioral Data Platform
• CHAPTER 5 The Collaborative Ideation Platform
• CHAPTER 6 The Analytical Application Platform
• CHAPTER 7 The Autonomous Decisioning Platform
• CHAPTER 8 Implementing Your Course to Sentience
• Conclusion
• Acknowledgments
• About the Authors
• Index
• EULA