Last month we talked about the types of data scientists companies should hire, based on the type of data they would be modeling: data for people or data for machines. As a quick refresh, data for people focuses on data collected to improve the customer experience—how to improve it, change it, or make it more profitable. Data for machines is the type of data that helps computers learn and process. Unless you’re a tech development company, you’re most likely working with the former—data for people—and trying to find the best way to optimize the information you learn about them. The thing is, the way we as humans are used to “optimizing”—by hiring specialists to divvy up work and accomplish it efficiently—may be hurting your company. In fact, it may be time to hire a data generalist.
First, I recognize there are a lot of business trends in digital transformation. It can be overwhelming as a business owner to know which trends to follow and which trends to ignore. This one, however, I find worth mentioning—and considering for implementation. Harvard Business Review recently shared an interesting take on this topic, so I’ll share some of my own thoughts here. From where I stand, the solution isn’t right for everyone—especially large companies that have far too much data and far too many projects for generalist to manage single-handedly. However, for many companies today, it might be a sound choice. Here are four reasons a data generalist may help improve the data you’re gathering from your customers.
- It could lead to better insights. When people focus on their own small piece of the puzzle, rather than looking at it from a bird’s-eye-view, they tend to care less about how the rest of the pieces fit together. You hear things like, “Well, I did my part correctly,” or “At least my project was done on time.” Employees focus more on the process than they do on the purpose—getting the deepest and most meaningful insights about the customer. When a data generalist is able to manage the “full stack” of data gathering, modeling, and implementation, they’re more committed to finding the right and best answer, rather than finishing their piece of the process.
- It could save you money. I’m not telling you to go out and fire your machine learning, AI specialist, and data engineer. What I am telling you is that if you are employing different specialists for data acquisition, modeling, management, and insights—you might be diminishing your ROI in a number of ways. First, hiring that many people, obviously, is expensive. Second, the insights you do get will be less valuable, as noted above.
- It could save you time. We all know that “design by committee” creates trouble bottlenecks in terms of project implementation. I have seen major projects sidelined for years simply because the right executives couldn’t get in the right room—or on the phone!—at the same time. By eliminating the “design by committee” mentality, your data generalist will be able to push projects through far more quickly—improving customer satisfaction overall.
- It can improve your company culture. Everyone enjoys having a purpose. Hiring data generalists—and empowering them to develop their own insights and action plans—will allow each person to feel like they have their skin in the game—like they are empowered to find solutions that truly benefit the company. This isn’t always the case for data specialists who spend time modeling other people’s solutions or projecting other people’s insights. Never underestimate the power of committed employees.
We’ve all heard the joke about the kid who majors in “general studies” in college. We think he’s going nowhere. But, if you look at the broader tech sector, you’ll see that hiring generalists is becoming a trend in all areas of digital transformation. Technology is changing so quickly that it is no longer a good investment to focus solely on one skill—for the company or worker. Businesses need quick thinkers who can adapt, see the whole picture, drill down into it, and create solid plans to use the information they learn to meet a business goal. It’s no longer enough to be “good at coding” or “good at algorithms.” A data generalist needs to be generally good at business, as well.