Calling All Data Scientists: Be the Hero and Become the Chief Revenue Officer
Cross-posted on LinkedIn on 1/20/16
Recently I read an article from Randy Bean, Contributor, Wall Street Journal, entitled, “Current Data Scientist Craze Can’t Last.” It got me thinking. My key takeaway from the article was this: “The data scientist has not always been perceived as an essential member of the business team.”
Are they really scientists at all? When most people think of scientists they think of people who lock themselves in a room trying to solve the toughest problems we know. Cures for cancers, the next biggest things in polymers, spacecraft bus avionics, alchemy, stuff like those super-intelligent high school kids from the movie “Weird Science” would whip up.
In reality, data scientists exist to solve the universal, mission-critical business problems that companies contend with every day. Two of the most fundamental are these: How are we going to make money? And… What can we do to minimize the risks and costs associated with doing business? This is what the CEO wants and this is what Chief Data Scientists and their teams need to figure out to be part of the senior leadership.
With the rise of big data and data analytics, business people formed high expectations of what could be done and what should be accomplished. In 2013, Brian Cardon, the former CMO of Lattice Engines, on CMO.com, wrote an article entitled, “Predictive Analytics: The Power Behind Next-gen Marketing.” Here’s an excerpt…
Right now, virtually all of our marketing data is backward-looking. Clicks, Web visits, open rates, downloads, and tweets all happened in the past. What if we could take this data and use it to predict what customers were going to do next? Could we possibly answer questions, such as:
- Which of my customers are most likely to terminate service? What steps could I take now to remediate this?
- Of the thousands of prospects my sales team are speaking to, which ones are most/least likely to buy? How can I align my sales team’s time with those most likely to buy?
- Which of my current customers are most likely to buy some of my other products? Which products should I try to cross-market to which customers?
It is not science-fiction. Right now, the marketing organizations at companies such as ADP, Dell, SunTrust, and Microsoft are doing just that. They are using a variety of statistical techniques to analyze current and historical data to make predictions about the future. It’s called predictive analytics.
This summarizes the hopes that business leaders had for the prospects of predictive analytics and the access to big data. Data scientists are making some progress. But unfortunately they often are not showing the right attitude when approached with the idea of using technology to automate the process of creating or building the predictive models. They see technology as a threat. Not as an asset. But that’s not the way to look at it.
John C. Welch, Blogger with Ars Technica, from his article, “It’s the little things, Pt. 2: The importance of automation,” put it this way…
When I talk to internal analytics teams about automating predictive analytics I am regularly receiving push back. They’re defensive. They pick everything apart. Blocking the acquisition of new technology. In reality this is an opportunity to eliminate the drudgery, the time-consuming, costly process of building predictive models manually. Automation is the way to be the hero, the way that the Chief Data Scientist can save the day.
One of the biggest frustrations in your current role is repetitive – gathering the data, time and time again, building and repeating one-off models, old-style point and click technology (even though it is billed as fully automated) – all of this keeps you occupied with the mundane and gives you no time to think big about how data can be used to drive the business. You should mandate that all approved predictive models be fully automated and put into production on a regular basis, fingerprint free and updated from scratch anytime there is new data available.
Data Scientists can be the Chief Revenue Officers because they can use creativity and automation, technology that builds a semi-exhaustive series of predictive models automatically. Automation enables the data experts to develop a comprehensive plan to take advantage of every single revenue opportunity for existing and prospective customers. This way, when the head of sales asks the CEO for twenty new salespeople in order to hit the number, you can counter with, “What if I could make your existing team so effective that you’ll exceed your revenue target without adding a single salesperson?” With autonomous data modeling quarter over quarter success can become a reality. It’s the competitive advantage that keeps on giving. Don’t settle for being put into a supporting role within a “cost center.” You need to be seen as a critical member of the company, part of a profit center. Then you’re shaping the strategy, you’re laying out a vision, not just dealing with the drudgery, the part of “data science” no one really likes.