The best data teams are the ones that tell the most insightful stories. But what characterizes a good versus poor data team? I’ve found — from my 15 years of experience working with data— that it always boils down to the relationship between qualitative and quantitative team members and how they complement each other.
First, what do I mean by qualitative and quantitative?
Generally, the qualitative thinker is visually and verbally gifted and has the business chops to understand and explain the shifting groundwork of any given industry. They are likely managers or leaders, deal with bigger picture challenges, and are client facing.
On the other hand are quantitative individuals. These are the analytical thinkers — the facts and figures miners — and they can explain why certain phenomena occur with models, regressions, and evidence supported by data big and small. While they can make sense of the numbers, they oftentimes lack the business savvy and experience to relate it back to the bigger picture, or to clearly explain how their findings impact business trends.
Together, they make the modern data team. Part analytical wizard and part storyteller, equipped to make sense of complex systems with data-powered narrative. Their effectiveness depends on how the team is structured, leading to reports that are either mind-blowingly insightful, have a few interesting takeaways here and there, or are total data mush.
I am a mathematician and qualitative thinker by trade and have worked on successful, lackluster, and subpar data teams across various organizations. While I was at Gartner, an IT research and advisory firm, I learned something really interesting about what defines a good vs. mediocre vs. poor data team, and how to work with a qualitative teammate to create insightful reports quickly.
At Gartner, our immediate data team consisted of only two of us; myself on the quant-side, and my boss as the qualitative leader. We were tasked with advising CTO’s and IT managers on how to attribute IT spending and personnel back to the bottom line impact of the overall business. Working on-site, my qual-minded boss would piece together a story of what was happening at a high level and where the bottom line could likely be improved with his strong business chops. He would then articulate this message to me and I would work to gather the data to support or negate his assumptions. With our story complete, we would present to CTO’s and tech managers on ways to optimize their IT businesses. We soon became so adept at the job that we were providing the services of other 10 person teams.
Initially, we figured our high performance was the product of our individual quant and qual strengths but this was not the case. What actually allowed us to outcompete most every team was my understanding of storytelling and business themes, partnered with his comprehension of analytics and data tools. For example, when we were compiling a client report, he knew just enough about analytics, and what was possible, to structure our story with missing quantitative arguments that I could quickly generate. He trusted that I had the business wherewithal to gather the relevant data and display it in a way that would support the story he was crafting and appeal to our client’s interests. In effect, we were simultaneously creating the same storyline — he laid out the structure, and I filled in the missing pieces with the right data, days and weeks before our competitors could.
This mutual understanding also allowed us to hold each other accountable without wasting time. He would constantly challenge my approach and ask why I did things a certain way, where the data came from, how it was presented, if there were any missing variables, how it supported or negated the story we were telling, etc. On the other hand, I would challenge his assumptions with data evidence and course correct his pitch to the client if my analysis conveyed a different narrative.
Essentially, we found the ‘Goldilocks’ principal of overlapping skillsets: “not too much, not too little, but just right”. We were not too different-minded to make it difficult to communicate, nor did we overlap to the extent where we were stepping on each other’s toes.
After realizing the effectiveness of this team dynamic firsthand, I started to incorporate it into my company, Psocratic. We started by hiring two quant-minded employees with business chops and partnered them with our qualitative storyteller, who understood analytics, to work on a handful of projects. In one week, we identified three major initiatives to benefit the company. Three months after that, we had created two reports that became core pillars of Psocratic, and built a tool that we used to validate our entire model; all of this 4 months ahead of schedule!
From there we took this approach one step further and incorporated it into our actual well-being and leadership product. We do this by providing storylines for individuals, teams, and organizations that are fueled by quantitative reasoning. For example, each user and client of our platform is provided with a compelling storyline that is supported with data to quantify their mental health or human capital statistics.
In the future, we plan to play around with this model a bit. Hiring multiple quantitative teammates and storytelling types, that care about our overall mission of improving mental health, and putting them into two person teams on a rotating basis. Constant circulation will allow for more access and exposure with other teammates to help spread skills and increase empathetic thinking. We will obviously be crafting a data-supported narrative along the way.
Psocratic is a proactive behavioral health platform on a mission to advance workplace culture and wellness. Schedule a demo or say hello: firstname.lastname@example.org 🙌