In an interview in the Spring 2011 edition of MIT Sloan, Ali Riaz and Sid Probstein argue that organizations can get differentiating value from analytics by forgoing data precision for decision-making speed. Precision is bad. Fast is good. That caught my attention. The gist of their argument follows.
The interview starts with Michael S. Hopkins asking Riaz and Probstein about how information capture and reporting has changed in recent years. After providing a short history on report reconciliation, data consolidation, and more recently, data federation via master data management, Probstein gets to the heart of the matter:
"None of us expected two things: the amount of scale that we need, and the speed that we need. Companies like Comcast and Verizon have millions of clients, and every day, hundreds of clients move from them to competitors. There's no point in finding out tomorrow why my customers left me yesterday, but it would be great to know who is about to leave me a week or two from now."
And that's the thing. While we can in fact learn from history, it's much more important to be proactive about our future, therefore creating a better history.
But, how do you make better decisions on worsening data? You rethink your premises:
Probstein: "One of the most important questions is whether we should even worry about whether this report is exactly right or not.
There’s a term called “eventually consistent” that grew up around a whole fleet of open-source-type technologies for crunching the huge amounts of data generated by website click-throughs. If you’re an e-commerce site, you want to understand the convergence of what the user is looking at and why he is clicking on it. Amazon.com, of course, is really good at this, asking, “For this customer at this very moment, what’s the best thing to show them?” They have high, high rates of success on recommendations, on product bundles, on follow-on advertising.
Amazon is good at this because they don’t worry about everybody. They develop a model where they’re eventually going to get a consistent model of the world, but at the moment they need to do it, they don’t care that they can’t roll it out for everyone. They’ve got hundreds of millions of clicks a day, and they figure, why don’t we just look at 20% of them? The key thing is to do it quickly and to make sure that whatever we conclude, there are many observations for it."
Of course, accelerating information flow isn't enough. You need to grow the organizational capability to confidently consume and act on the information, before the related opportunity expires.
"But for us to live with the realities of information growing more and more and speed getting faster and faster, we need a new way of thinking not about having precision but about having a good understanding. And being able to live with that."
And this organizational capability starts with leadership:
"Generally speaking, most corporations are inefficient in a lot of ways, given the human factor, the data factor, the change factor. There are a lot of factors involved. But in order to abandon that, managers have to come to believe that having a range of information is better than having one piece of information.
...That is exactly the role of leadership, which is to deal with uncertainty in this information age."
Summing the key points, Riaz ends:
"You’ve got to get the data right, and not just data, but the range of data, and then you have to have context for what the data mean. Then you have to have leadership and business processes that allow for a dialogue. Do all that and you’ll actually be making intelligent decisions and not political CYA, all those things that happen every day in organizations and governments. Having a wider set of data and content, structured and unstructured, will allow you to learn to paint with colors that are new and old."
Does your organization have the aptitude (and fortitude) to apply good enough practices to analytics? Or, does slow and steady still win the race?
