When Business Intelligence software vendors that are to blame for keeping the information age advancing at a snail’s pace have the chutzpah to give advice about data science (the in-vogue term today for data sensemaking), I find it difficult to remain silent. The latest entry in the “Advice from the Clueless” category is an interview with Timo Elliott of SAP Business Objects, titled “What Is a Data Scientist? SAP’s Timo Elliott Says Leadership,” which appeared in Forbes on February 22, 2012. I’ll warn you now that my comments in this blog post are dripping with disdain. A less acerbic response would lack honesty.
Rather than writing a thorough review of Elliott’s comments, which isn’t warranted, I’ll just feature a few quotes from the interview, followed in each case by a short rejoinder.
Timo Elliott, Senior Director, Strategic Marketing, SAP Business Objects
To begin, let’s put Elliott’s comments into context by looking at his experience:
Elliott performed analytics for Royal Dutch Shell for about a year ending in 1988, when he joined BusinessObjects in Paris as the eighth employee. He has been with the company, now part of SAP, for more than 20 years.
“About a year” of analytics experience 24 years ago? Well in that case, let’s hang on his every word.
We have now entered an era in which technology is no longer the primary bottleneck to extracting meaningful business value from data. The primary bottleneck is actually human leadership, according to Timo Elliott, Senior Director, Strategic Marketing at SAP BusinessObjects. In other words, to expand the impact of data on your business, it is time to balance the focus on the technology of analyzing data with development of leadership in order to make sure that technology is put to good use.
SAP would love for us to believe that technology is no longer a bottleneck. Human leadership is indeed part of the problem, but savvy leaders would realize that technologies are still in fact a big part of the problem that we face. A good leader would advise the organization to stop relying on vendors like SAP when attempting to make sense of data.
Historically, what we now call “data science” has been somewhat limited to Web companies, which have had wide access to their activity logs and have been able to devise excellent products from them. But now, through the release of the latest crop of visualization and business intelligence (BI) products, that kind of capability now exists for all kinds of companies.
This is indeed beginning to happen, no thanks to SAP. Those who are using the term “data science” meaningfully and with integrity are doing so, in part, to distance themselves from the likes of SAP Business Objects, which has so far provided nothing useful but production reporting systems. When marketers like Elliott use terms like “data science,” they’re trying to give the impression that they’re on to something new and better without any real substance to support the claim. This is the same organization that introduced the embarrassingly impoverished Business Objects Explorer as “revolutionary.”
The reason many BI projects ultimately fail is too much focus on technology.
I couldn’t agree more. This is also the reason why so many BI products fail. Focusing exclusively on technology without understanding the needs and abilities of those who will use it—a common pitfall in software development—produces products that only a software engineer could love. This is especially true of products that support thinking, which must interface seamlessly with human perception and cognition. SAP Business Objects knows how to build production reporting systems, but not how to build tools for interacting meaningfully and efficiently with data.
“You have Scotty down in the engine room,” Elliott says. “He’s the guy who understands the technology perfectly, but he’s not the one leading the ship. You need the whole crew. You’ve got Spock, who’s an analytics person; you’ve got Bones, who’s the human relations person who does the emotional side; you’ve got Uhuru [sic] on communications. But the key person is Captain Kirk. Captain Kirk doesn’t know how the fusion generator works. He is a decider. His job is to lead people into whatever the situation is and make those tough decisions.
Much like former President George W. Bush, the “deciders” in many organizations are not in touch with the data. Relying on deciders in leadership (the Captain Kirks of the organization) will only work if they actually do have some idea of how that fusion generator propels the ship.
In some ways, a data scientist is equal parts Captain Kirk and Mary Leakey, the best-known member of the team that discovered and interpreted the early human skeleton “Lucy” in Egypt. The data scientist is part ship’s captain, part anthropologist. The data scientist is aware of the complexity of the systems at hand, but is less a deep technology expert than a comprehensive evaluator of the modalities of data used in an organization.
Okay, I’ll start by admitting that I don’t know what “comprehensive evaluator of the modalities of data” means. Probably nothing, giving the fact that marketers like Elliott don’t have to make sense as long as their words sound impressive. Unfortunately, data scientists—those who understand what’s going on based on evidence derived from data—are rarely given leadership positions. Knowing what’s really going on is seldom given priority in organizations.
“What’s not data science is a business person with a business question going to an IT organization saying, ‘Give me this report,’ and the IT person coming back and saying, ‘Here’s your report,'” Elliott says. “That is not data science. Why? Because it’s not about the interface. That’s the business person basically trying to do their current job in the current way, using a little more data. That can be worthwhile, and I’m not saying IT organizations don’t provide value when they do that, but it’s not data science.
I agree that this scenario doesn’t reflect data science. But this scenario accurately represents the very model that SAP has fostered for years and continues to foster in its products today.
“Ultimately, the reason why a lot of the companies that have people called ‘data scientists’ are successful is not only because of the data scientists and their skills, but also because the people that run those companies are keenly aware how much of a difference data can make to their businesses.”
The suggestion that organizations with people called data scientists, rather than data analysts, business intelligence professionals, or decision support specialists, are more successful than others is pure nonsense. The term data scientist in most organizations is just the latest term that’s being used by people who do precisely the same work as those who use the other titles. Changing what you call these folks doesn’t magically improve their work.
Even as data-science technology is on the upswing—IT spending per head…may actually jump 60 percent in the coming years, according to Gartner—there is a growing realization among the most data-savvy companies that the culture is just as important as the technology.
And if Gartner says it, we know what that means, don’t we? After all, Gartner’s magic quadrant claims that SAP Business Objects is the second most visionary vendor in business intelligence, second only to IBM, neither of which have demonstrated any real vision in their products for years.
“Most of us are just really bad at analyzing information,” Elliot says.
Yep, this is absolutely true. Why? Because most people haven’t learned to think critically, haven’t learned basic analytical skills, and have grown less capable to the degree that they rely on dumb technologies such as SAP Business Objects to do their thinking for them. If SAP wants to provide leadership in data science or whatever you choose to call the work of data sensemaking, they themselves have some learnin’ to do. Until then, perhaps they should remain silent and concentrate on listening.