A senior banker – let’s call him Jack — was on a conference call attempting to close out an acquisition. The stakes were high. It was a multibillion-dollar deal and the negotiation of the final price hinged on the measurement of the target’s EBITDA, the Earnings Before Interest, Taxes, Depreciation, and Amortization. Jack argued that the EBITDA was lower; the opposite party asserted it was higher.
In the middle of the lengthy, convoluted discussion of the numbers, a junior associate realized that, in fact, the other side was right. She passed Jack a note letting him know this. Jack stared at the associate with contempt and proceeded to argue even more vehemently for the lower price. He literally just spoke louder than the other party, cutting them off at every opportunity. And he won. The other side just gave up. In the associate’s words, “I knew Jack was wrong. Jack knew Jack was wrong. The other side knew Jack was wrong, and Jack still won!”
How can we build teams and organizations that don’t succumb to the jerk who just yells more, argues louder? We all want to be data-driven instead of being driven by supposition, ego, and ideology
Over the last two years, I’ve had the opportunity to meet with analysts and leaders inside data-driven organizations as well as many that were not so data driven. Surprisingly, I’ve learned that being data driven has little correlation to size or geography and only a marginal correlation to industry. Data-driven companies range from small health care firms to large banks and even include mid-sized non-profits. And while the traditional categorizations of businesses have little to offer, I’ve observed a few common characteristics:
1. Size doesn’t matter, but variety does. You would think that a data-driven organization has a lot of data, petabytes of data, exabytes of data. In some cases, this is true. But in general, size matters only to a point. For example, I encountered a large technology firm with petabytes of data but only three business analysts. What really matters is the variety of the data. Are people asking questions in different business functions? Are they measuring cost and quality of service, instrumenting marketing campaigns, or observing employee retention by team? Just getting a report at month end on profits? You’re probably not data driven.
2. Everyone has access to some data. Almost no one has access to all of it. There are very few cultures where everyone can see nearly everything. Data breach threats and privacy requirements are top of mind for most data teams. And while these regulations certainly stunt the ability of the company to make data available, most data-driven companies reach a stage where they have developed clear business processes to address these issues.
3. Data is all over the place. One would think that the data is well organized and well maintained — as in a library, where every book is stored in one place. In fact, most data-driven cultures are exactly the opposite. Data is everywhere — on laptops, desktops, servers.
4. Companies prize insights over technology standards. Generally, the principal concern of people in data-driven businesses is the ability to get the insight quickly. This is a corollary of point #3. Generally, the need to answer a question trumps the discussion of how to best answer it. Expediency wins, and the person answering the question gets to use the tool of their choice. One top 10 bank reported using more than 100 business intelligence technologies.
5. Data flows up, down, and even side to side. In data-driven companies, data isn’t just a tool to inform decision makers. Data empowers more junior employees to make decisions, and leaders often use data to communicate the rationale behind their decisions and to motivate action. In one data-driven company, I observed a CEO present a 50-slide deck to his full team, and almost all of those slides were filled with charts and numbers. Most fundamentally, data empowers people to make decisions without having to consult managers three levels up — whether it’s showing churn rates to explain additional spend on customer services vs. marketing or showing revenues relative to competitors to explain increased spend on sales.
So, what do you do if you’re leading an organization that doesn’t meet these criteria? How do you start?
First, just start collecting the data. You can’t answer any questions if you don’t have the fundamental building blocks. Second, empower your people to access the data and challenge them to use it at every opportunity. Third and most importantly, don’t just hire people who are naturally skilled in using data (e.g. a data scientist), but look for people who are characteristically generous in sharing their knowledge.
People are naturally curious — if you give them the raw materials, they’ll do what they need to start learning more. Like anything else, using data just seems like habit, one that’s pretty addicting once you get used to it.
Satyen Sangani is CEO and cofounder of data startup Alation and was previously vice president of financial services analytical applications at Oracle and a financial analyst at Morgan Stanley. Throughout his career, Sangani has been fascinated with the possibilities of data and has spoken with hundreds of Chief Data Officers, data scientists, analysts, and data infrastructure professionals. You can follow him on Twitter @satyx.