Many senior managers fail to manage their data scientists properly. They put data scientists in the wrong spots in the organization; view the data science as a technical, not business, initiative; or underestimate how resistant their organizations are to change. These missteps lower the chances that data efforts will succeed and, in extreme cases, doom the efforts from the very start. Senior managers must actively manage their data scientists in order to set them up for success. First, they must think through how they want data scientists to contribute and put them in spots where they can do so. They must immerse data scientists in the business and help them connect with others in the organization. They must obsess on quality, professionalism, and business results. And they must encourage data scientists to spot things that look out of place in the data and to follow up on them.
Getting as much as they can from analytics is critical for companies seeking to monetize their data, become data-driven, and put their data to work. Yet most find this difficult. Indeed, the failure rate of analytics projects remains distressingly high.
A key reason for this is that senior managers fail to manage their data scientists properly. Many fail to focus the data science program, or they put data scientists in the wrong spots in the organization; others view the data science as a technical, not business, initiative; and still others underestimate how resistant their organizations are to change, and do not fully equip data scientists to change hearts and minds.
These missteps lower your chances of success and, in extreme cases, doom the effort from the very start. But by following the road map below, you can ensure that your data scientists are more productive, and increase both your probability of success and the rewards you reap.
First, think through how you want data scientists to contribute, and put them in spots where they can do so. The worst mistake a company can make is to hire a cadre of smart data scientists, provide them with access to the data, and turn them loose, expecting them to come up with something brilliant. Lacking focus and support, most fail. Instead, clearly define the opportunities you want to address using data science, and put your data scientists in places in the organization where they can best pursue those opportunities.
Consider my experience when I started at Bell Labs as a freshly minted PhD. At that time, Bell Labs employed dozens of world-class statisticians — some in network performance (like me), but others in research, quality assurance, and so forth. A few days after my arrival, I was ushered into Steve Katz’s office, my director, who was three levels above me in the organization. Steve welcomed me, then explained exactly what AT&T was trying to achieve, where his lab fit, and why he was adding so much statistical talent. He made clear that the overarching goal was to improve the telephone system and that I’d work on teams filled with people of diverse skills to do so.
Every senior manager should follow Steve’s lead. If you want to improve marketing efficiency, put data scientists in marketing; if you aim to drill oil wells more effectively, put data scientists close to the action; if you seek game-changers and new discoveries, put them in a laboratory. Even if you’re just getting started in your data efforts, pick one specific objective and position your data scientists to pursue it.
Second, immerse data scientists in your business.
