Connecting the Dots Between Data Science and Business Challenges

Problem

In data science, one of our major challenges is finding the right balance between being proactive and reactive. Data science excites people. They love the idea of having a team that can answer questions for them on a daily basis, and they can get carried away at times.

Often, those questions will involve securing a data point to convince one of our stakeholders of something. It ends up happening in a series of data requests, somebody asking for a number to dig up. We scramble to do the analysis and to make it happen. The line of communication should always be open in both directions, however.

Actions taken

I was the only data scientist on the team at first. I was working with three product managers, thirty engineers, and a couple of designers. The ratio in terms of my support had me swamped almost all of the time. I wanted to find some way of elevating my level of function within the company.

I started to do more deep-dive analysis in order to figure out how to give the rest of the company what they needed. The first thing that needed to happen was that I needed to unblock myself and my way of approaching the problem. The people that we work with are relatively technically-minded; many of them know how to code, and they know the data that they’re working with well.

I invested some time in building a self-help guide for these types of colleagues. If they felt confident enough to dig in and to answer some of their questions for themselves, I wanted to enable that curiosity and exploration. This is the schema that we’re in. Here are the tables that we use. Here are some example queries that you can reference. In addition to these resources, here are my office hours. I made a promise to dedicate time to those who needed to be unblocked. That allocation of time made them more self-sufficient, giving me more time to focus on the big picture.

Where I see data science as being most useful is when crafting the story for the whole team. We want to use the data to make the best decisions possible. I was able to position myself as a thought leader within the squad, which helped me up-level the data science department’s role within the company. When they start thinking laterally with data in mind, the rest will fall into place.

Lessons learned

• Not everybody knows what data science is, so part of my mentality is that I need to educate other people constantly. I want to show them what data science really is and what it is capable of.
• Instead of constantly trying to put out the fire in front of me, I try to think ahead to where I aspire to be in the future. This is the true spirit of data science. I try to leave this impression on those bringing me data points to find. They may actually need something other than what they’re asking for entirely. We can work together to find the real insight to uncover.
• I think a lot about the difference between being a service leader and a thought leader. I am constantly reassessing the value that my department brings to the organization and how I can continue to deliver. It is more than the number that you put down on paper.