Without buy-in from your company’s rank and file, even the cleverest AI-derived model will sit idle and “data-driven decision-making” will just go around in circles. Companies need to start seeing regular people as part of their data strategy. Data teams must work with regular people every day, develop a feel for their problems and opportunities, and embrace their hopes and fears surrounding data, then focus on equipping people with the tools they need to formulate and solve their own problems. They should also ask two questions with each data project: 1) Who will this effect? And 2) How can we get them involved as soon as possible?
Regular people, those without “data” in their title, are central to all data-related work. Without buy-in and contributions from your company’s rank and file, even the cleverest AI-derived model will sit idle and “data-driven decision-making” will just go around in circles. Conversely, costs go down and products get better when people help improve data quality, use small amounts of data to improve their team’s processes, make better decisions, and contribute to larger data science and data monetization initiatives. Yet, recent research confirms that these people are missing from too many data programs, limiting the scale and impact of these efforts.
To drive the importance of regular people home, consider the process of completing a data science (big data, analytics, artificial intelligence) project. In general, this requires five steps: understanding the problem, collecting and preparing the data, analyzing that data, formulating the findings, and finally, putting those findings to work. At each step, regular people have a critical role to play — as collaborators, as customers, and as creators of the data used — and there are serious consequences for not including them. Doing each step well depends on regular people.
Dig into anything you wish to accomplish in the data space — architecture, data-driven decision-making, digital transformation, exploiting proprietary data, monetization, quality — and you get the same result: you need regular people. In fact, you cannot do good data science without them.
To take fuller advantage of their data, companies must put regular people front and center in their data programs, get everyone involved, and assign them specific tasks. Doing so will accelerate those programs while simultaneously reducing fear and stress. Here’s how to start.
In my consulting work, I find that many managers, unconsciously perhaps, have debilitating pre-conceptions about people. They view them as part of the problem — out-of-date, ill-suited to the rigors of data, and resistant to the new ideas. Such preconceptions simply will not do. When I talk to their teams, I find just the opposite. Large numbers know that data is increasingly important, have great ideas for making improvements, and want to create opportunities for themselves. Engaging them is simply not that difficult.
Leaders and companies need to reboot their outlook and see people as part of the solution. I advise managers to “start small,” asking people where they see opportunity. The vast majority have plenty of ideas — one person wondered if they wasted too much time in meetings, another whether most of the reports the team produced were ever read, a third why it so difficult to reschedule patient appointments. Encourage people to gather some data to test their ideas and propose better ways for their teams to do their work. Then help them implement those better ways.
I’ve seen so many people with no formal data background contribute to better team and company performance in exactly this way. Almost all derive enormous satisfaction from the experience. One woman told me, “I’ve worked for this company for 20 years. And I never felt like I had any control over anything. But this was different. I was in control, I did what I thought was best. And let me tell you what we achieved.
