Earlier this month I hung out at Dreamforce, the Salesforce.com extravaganza that swamps San Francisco each fall with people (170,000 or so) and hoopla (U2, Tony Robbins, etc.). At first I was impressed that I was asked to give a keynote—I began to think of myself as the “Tony Robbins of analytics and AI,” perhaps a dubious distinction—but then I realized that at any given time there are about ten keynotes being delivered. It was great to be onstage, but mostly I was just glad to find my session among the more than 3000 others. What I really enjoyed, however, was talking with other attendees of Dreamforce who are interested in analytics and AI. I learned much more from them than I ever could from hearing myself talk.
Salesforce has been moving in a more analytical direction for a while now, with its Wave Analytics product that offers do-it-yourself descriptive analytics. But this year, the company took a decided turn in the cognitive direction, with its Einstein suite of cognitive and analytical capabilities that are being baked into the different cloud-based systems for sales, service, and marketing. As Marc Benioff, the company’s CEO, put it in his keynote (which was attended by a few thousand more folks than mine), “We’re on a march to artificial intelligence,” and, “We want to become everybody’s data scientist.” The company has made ten or so acquisitions of AI and analytics startups over the past couple of years, and in the process has “acqui-hired” more than a hundred very smart data scientists.
I thought that the translation of acquired companies into actual Salesforce products was very fast, but John Ball, the company’s General Manager of its Einstein offerings, told me that the intelligent capabilities were planned for several years, and that each acquisition added a piece that had been identified as necessary.
However long it took, I think it’s a great idea to make AI and predictive analytics easier by embedding them into existing transactional systems like CRM. If you offer a salesperson a set of predictive scores on how good their customer leads are (one of the Einstein capabilities), who wouldn’t want to use them to help allocate time and attention? If your analysis of sales data can identify hidden patterns that you didn’t anticipate (which the company’s BeyondCore acquisition can do), who wouldn’t be interested in learning about them? These types of embedded capabilities can, I believe, rapidly accelerate the adoption of AI.
But I was most interested in how customers would respond to these new capabilities, and in the conversations I had with them, I got some insights into that issue. There seemed to be a lot of interest. In fact, several companies (I have promised not to name them) said they were hoping to “leapfrog” over traditional business intelligence and analytics capabilities and go directly into AI-related environments. As one technology executive from a software company put it, “We’ve had enough bar charts. Nobody has time to digest them anyway. We want our analytics to tell people what to do.”
Another manager from a non-profit organization admitted that her organization had used virtually no analytics in the past. But she also wanted to leapfrog to AI. She noted, “Why walk when you can run—or even drive?” She liked both the BeyondCore “smart data discovery” and the Einstein-equipped CRM ideas.
The general feeling of many of these executives seemed to be that the traditional notion of “analytics” can recede into the background, and that what workers really need is to be told at any point the smartest thing to do next. The managers I spoke with at Dreamforce don’t want complete automation—the humans still need to be able to override the recommendations—but at least some don’t seem to feel a need for visible analytics.
This development, were it to actually happen, would have implications for far more than Salesforce.com (though it might imply diminishing demand for the company’s traditional descriptive analytics offerings, which create lots of bar charts). It would change the entire analytics and BI industry. We’ve traditionally felt that companies should walk with descriptive analytics, run with predictive and prescriptive analytics, and drive with autonomous analytics and cognitive technologies. But what if companies want to go straight to driving? What if the traditional distinction between transaction-oriented applications and analytics applications collapses? Is this the end of bar charts and pie charts?
I am not entirely sure that this would be a good thing for the world. Descriptive analytics help people get familiar with their data and understand how far it can be taken into decisions and actions. A bar chart for a single variable may not be the most sophisticated form of analytics, and it does require some human attention. But it can tell you, for example, that you have some outlier values that you didn’t anticipate, or that a different categorization structure for your data might be in order. More advanced analytics and AI based on them are great, but you don’t want to automate a decision before knowing that your data are of sufficient quality.
We’ll see if this new view of analytics catches on. It’s both an exciting and scary prospect for those of us who have spent much of our work lives discussing the traditional categories of analytical activity. Let’s hope that the new tools are used in addition to the old ones, and that we can still find ways to occasionally immerse ourselves in data.
Tom Davenport, the author of several best-selling management books on analytics and big data, is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, co-founder of the International Institute for Analytics, and an independent senior adviser to Deloitte Analytics. He also is a member of the Data Informed Board of Advisers.
Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.