Thomas H. Davenport is a Distinguished Professor at Babson College, a Research Fellow at the MIT Center for Digital Business, Director of Research at the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.
How are things with Big Data? Is the hype just about over?
Well, “big data” as a term is now nearly ubiquitous. Indeed, it seems that every day we hear new reports of how some company is using big data and sophisticated analytics to become increasingly competitive.
According to Google Trends, the words “big data” have been trending downward for the last six months or so. This decline is somewhat deserved, given that the term never had a precise meaning in the first place. But while the term “big data” might be in decline, the concept is still extremely valid.
The importance of data and analysis to human beings will not be fading away anytime soon, if ever. So “big data” now really means “all data”—or just “data.”
To understand customers and predict their behavior, for example, a company needs to employ small data (such as sales records of what a customer has bought in the past) as well as big data (such as information from customer clickstreams and social media). In other applications as well, making a hard-and-fast distinction between big data and everything else really makes no sense. Most executives who refer to big data really mean all data.
What is the single greatest challenge facing business today in terms of data?
In my experience, the resource in shortest supply continues to be management awareness and understanding of the business potential of big data. Hardware, software, and programming or analytics skills are all becoming increasingly available, but many managers still don’t know the true value of big (and small) data. Although there are abundant university programs teaching big data and analytics skills, there are far fewer executive education programs that provide a sophisticated understanding of big data capabilities. Because senior management sponsorship is necessary to proceed aggressively with this resource, educating those individuals is arguably the most important thing that organizations can do.
“Big data” still might need to be defined more precisely, but without a doubt the concept has been gaining considerable traction at many companies. Yet even as significant progress is being made with regard to this important business resource, the growing use of big data and analytics has not been without its challenges. As has often been the case in the past, the technology itself may now be outpacing the ability of organizations to deploy and manage it effectively.
You recently wrote an article called Analytics 3.0. How is that different from 1.0 and 2.0?
Analytics 1.0 was the era of business intelligence; 2.0 was about Big Data; and 3.0 is the era of data-enriched offerings.
With Analytics 1.0, for the first time, data about production processes, sales, customer interactions, and more were recorded, aggregated, and analyzed. This was the era of the enterprise data warehouse, used to capture information, and of business intelligence software, used to query and report it. The great majority of business intelligence activity addressed only what had happened in the past; they offered no explanations or predictions.
Analytics 2.0 is about using Big Data – not generated purely by a firm’s internal transaction systems, but externally sourced as well, coming from the internet, sensors of various types, public data initiatives such as the human genome project, and captures of audio and video recordings. The hallmark of 3.0 is predictive analytics, to predict, as Eric Seigel says, “who will click, buy, lie, or die.”
Companies are competing on analytics not only in the traditional sense – by improving internal business decisions – but also by creating more valuable products and services. This is the essence of Analytics 3.0.
With Analytics 3.0, it’s not just information firms and online companies that can create products and services from analyses of data. It’s every firm in every industry. If your company makes things, moves things, consumes things, or works with customers, you have increasing amounts of data on those activities. Every device, shipment, and consumer leaves a trail. You have the ability to analyze those sets of data for the benefit of customers and markets. You also have the ability to embed analytics and optimization into every business decision made at the front lines of your operations.
There has been a shift in how analytics are used today. Today’s companies are accelerating the speed and scale of their initiatives – many of which are strategic in nature. This requires new organizational capabilities.
Organizations will need to integrate large and small volumes of data from internal and external sources and in structured and unstructured formats to yield new insights in predictive and prescriptive models—ones that tell frontline workers how best to perform their jobs.
There have always been three types of analytics: descriptive, which reports on the past; predictive, which uses models based on past data to predict the future; and prescriptive, which uses models to specify optimal behaviors and actions. Although Analytics 3.0 includes all three types, it emphasizes the last. And it introduces—typically on a small scale—the idea of automated analytics. These come to full fruition in a new era—Analytics 4.0, I suppose.
Analytics 3.0 also provides an opportunity to scale decision-making processes to industrial strength. Creating many more models through machine learning can let an organization become much more granular and precise in its predictions. IBM, for example, formerly used 150 models in its annual “demand generation” process, which assesses which customer accounts are worth greater investments of salesperson time and energy. Now it uses tens of thousands.
What about combining analytics and products? Are there examples of companies that lead the way?
Businesses are using analytics to create innovative service offerings. And we are just beginning this transformation. Many of them are traditional industrial companies.
The Bosch Group, based in Germany, is 127 years old, but it’s hardly last-century in its application of analytics. The company has embarked on a series of initiatives across business units that make use of data and analytics to provide so-called intelligent customer offerings. These include intelligent fleet management, intelligent vehicle-charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. To identify and develop these innovative services, Bosch created a Software Innovations group that focuses heavily on big data, analytics, and the “Internet of Things.”
Among US-based companies, GE’s manufacturing businesses are increasingly becoming providers of asset and operations optimization services. With sensors streaming data from turbines, locomotives, jet engines, and medical-imaging devices, GE can determine the most efficient and effective service intervals for those machines. To assemble and develop the skilled employees needed for this work, the company invested more than $2 billion in a new software and analytics center in the San Francisco Bay area.
Toyota is using telematics to change how it thinks of itself as a company. It views itself as a mobility company, not a car company. Executives there realize that telematics and autonomous vehicles may mean dramatic changes in the entire automotive industry, such as people not owning cars as much. They are trying to prepare for these types of changes.
NCR has put sensors on its cash registers and ATM machines. It can now predict when failures will happen, and it can service those machines before they break. The company also has data on what customers are doing and buying with those machines, but it’s not using that data yet. However, it could eventually be transformative for NCR’s business.
You mentioned that the next stage of analytics involves cognitive computing. What can you tell us about that? Is it more than just another buzzword?
Most organizations that are exploring cognitive technologies—smart machines that automate aspects of decision-making processes—are just putting a toe in the water. They’re doing a pilot or “proof of concept” to explore the technology.
Organizations will want to create architectures for cognitive technologies that support more than a single application. In fact, I believe that it won’t be long before sophisticated organizations set out to build “cognitive architectures” that interface with, but are distinct from, their regular IT architectures.
This is the way that key vendors in the area are moving. IBM, for example, has disaggregated Watson into a series of modular services—a “cognitive platform”—that are available by subscription in the cloud. The original text analysis services of Watson have been augmented by Watson Analytics for analysis of numbers, “Personality Insights” for analysis of human behavior, “Visual Recognition” for image identification, and so forth.
Today some cognitive technologies, like most industrial robots, assume that they are in charge once programmed. Others, like surgical robots, assume that the humans are in charge. In the future I think we will need a variety of control modes. Just as in autonomous vehicles, there needs to be a way for the human to drive at some key points in the road.
Humans and cognitive technologies will be working together for the foreseeable future. Humans will want to know how the cognitive technologies came up with their decision or recommendation. If they can’t get into the black box, they won’t trust it as a colleague.
That said, there is no doubt that the use of artificial intelligence, machine learning, deep learning, or Watson, is going to profoundly change knowledge work.
How does automation tie into this? And are the robots going to eat our jobs?
People in all walks of life are becoming concerned about advancing automation. And they should be: Unless we find as many tasks to give humans as we find to take away from them, all the social and psychological ills of joblessness will grow, from economic recession to youth unemployment to individual crises of identity. That’s especially true now that automation is coming to knowledge work, in the form of artificial intelligence. In the very foreseeable future, as the Gartner analyst Nigel Rayner says, “many of the things executives do today will be automated.”
Now, instead of asking “What tasks currently performed by humans will soon be done more cheaply and rapidly by machines?” I’d like to ask “What new feats might people achieve if they had better thinking machines to assist them?”
Instead of seeing work as a zero-sum game with machines taking an ever greater share, we might see growing possibilities for employment. We could reframe the threat of automation as an opportunity for augmentation.
One other reason that intelligent machines haven’t—and probably won’t—reduce labor dramatically is that we are using them—at least sometimes—with the correct mindset. There is automation, which replaces existing tasks with machines that do the same thing, perhaps faster or cheaper. Then there is augmentation, which combines smart humans and smart machines to achieve an overall better result. We—both individual workers and their organizations—should be thinking of these new cognitive tools as opportunities for augmentation, not automation. They are aids not to job replacement, but rather job expansion.
My belief and hope is that the new intelligent systems will follow the path blazed by spreadsheets. Instead of replacing knowledge workers, spreadsheets freed analysts up to accomplish larger and more important tasks. Some decisions and actions may be taken over by automated systems, but that should make it possible for knowledge workers to take on other tasks and responsibilities. This outcome is certainly not inevitable, but in the early outcomes of cognitive technology implementation it seems to be what is happening
Finally, let’s talk about marketing function in organizations. What does the future look like?
No change, except there won’t be any people (laughs).
All the structured tasks will be largely automated – from testing, to digital advertising, to search engine optimization. Even ad-buying on TV is becoming automated through exchanges. Many of these tasks are already performed with “programmatic” software.
The creative function is also under some pressure. Creative will still create content, but it will be tested with analytics. Increasingly we will see analytics being used to tell marketers where and when to intervene to improve outcomes.
Reporting and communications becomes more critical. I’ve talked, for example, with several “programmatic buying” companies that buy and place digital ads, and they say their customers insist on high-quality reporting, and want to slice and dice their data in many different ways.
Whatever companies do with cognitive technologies, they will need to inform the rest of the organization about it. They will also have to communicate better with customers –using IoT products to connect to and anticipate needs instantly.
Thanks so much, Tom!
INTERVIEW by Christian Sarkar.