Bring data analyst to the table
With data analytics becoming essential in today’s business environment, this is why we need to change our ways of working with data analysts.
It was 2014, and I was sitting in a company office lobby, waiting for a meeting to begin. Little did I know how transformative the following hours would be for my career. I was interviewing for an engineering leadership role at a small company called GlobalWebIndex.
I got the job, and over the next five years, I had a chance to build a fantastic team of 60 technologists. We created a data processing & analytics platform for market research and advertising data, used by some of the largest companies in the world. I had a blast on the journey, growing from Lead to Manager to Executive.
Something else changed too.
Before GWI, I spent my career in Software Engineering. At GWI, I had to spend a ton of time with data—building data pipelines, training ML models, designing data projection algorithms, and running data analysis. Five years later, I left the company as an Engineer with a changed perspective—I realised how inseparable Data & Software are, and I became more versatile in working with both.
When I joined Pleo in Jan 2020, we started building our data stack. Since the team was small, I ended up doing a lot of hands-on analysis. When I discussed the latest company strategy with the exec team, I was still running analyses on various projects—feature adoption, new market segments, or deep-dives into the product onboarding funnel. Besides building software and teams, I loved to open the SQL console and dive into the data.
Benn’s story of the Executive Analyst
Being an exec team member with occasional deep dives into data, I accidentally brought analytics skills to the board room. This is what Benn Stancil wrote about in his recent post, The missing analytics executive, where he describes the importance of analytics skills at the exec team with the role of Chief Analytics Officer:
“Board meetings rarely have analytical observers, and there is no official designation for ‘“senior data advisors’” to the executive team. Nobody is directly responsible for helping a company make its most important decisions, or for exploring its uncharted strategic opportunities. There is no role committed to this work and no title that acknowledges its value.”
Without a hands-on analyst present, the exec team’s only option is to pass analysis required for essential decisions through layers of managers to the actual analyst, right?
To explain the Chief Analytics Officer, Benn has used the analogy with the CTO, a hands-on technologist, and an exec team member. First, I was sceptical. Some CTOs stay focused on the code but often step away from the exec team to focus on R&D projects.
I am not sure that analogy works because data and engineering are different:
While building a solid piece of software takes teams months of work, a well-thought-through analysis done by a person with great context and solid data could yield impressive results fast—in hours or days. This could accelerate the exec team forward real quick.
There is something to Benn’s Chief Analytics Officer idea indeed. Maybe the title needs some work—a Chief Analyst or Principal Analyst1? Nevertheless, they deserve the seat at the exec team.
Analytics Executive Team
Tristan brought this narrative to the next level with his article Data expertise everywhere. I was particularly impressed by the stat he shared about his exec team at dbt labs:
“At dbt Labs, of our eight-member executive team, six of us are world-class analytics engineers.”
— Tristan Handy
He argues that such a world will become a commonplace:
“Every knowledge worker will make decisions informed by large-scale and diverse observations of reality. Partially, this will be the result of advances in tooling that make complicated things simple. But partially this will be because new generations of professionals will see this skillset as foundational to their professional success.”
— Tristan Handy
This gets me excited. This is the future I would love to see—and I am curious about how it feels to be on an exec team with 75% of analytics engineers🤩.
Back to the reality of today’s data teams
While there is no doubt that the above perspective is an exciting promise of the future, it is a distant reality for many companies. It's not because of a lack of tech or inability to create scientific ways of working. It's something far simpler.
It’s because we often misunderstand the role of the data team. This means we typically don’t know how to work effectively with them. It's one of the reasons why many analysts frequently get a message like this:
With this engagement model, the data analyst is merely there to provide access to the data.
We shouldn’t hire data analysts to write SQL queries and churn out dashboards and spreadsheets. We should employ analysts to analyse and help us understand business problems using data.
Finding the correct data and putting it together is necessary, but there’s much more to do than that, and analysts are uniquely equipped to follow through.
Besides excellent data skills, the best analysts I know have a far more critical trait— a relentless curiosity that drives them forward to keep exploring.
How do they practice curiosity if they’re asked to “pull data” from a table? There is not much left to be curious about.
The “can you pull data” model has another big flaw: who does the analysis?
If data gets handed over in a spreadsheet or a dashboard, brought behind closed doors, where a group of decision-makers debate the numbers and make decisions, is that the best form of analysis? I am not sure.
When I do data analysis, I use the following workflow:
I have a problem—an unanswered question. I seek an understanding of the problem on a deeper level, using data.
I get an idea—a clue which I explore. Perhaps a dataset, which can contain some of the answers.
I look through the data—looking for evidence that would confirm or deny my idea. I don’t necessarily do so with the rigour of statisticians, but even a basic analytics approach often shows the way forward.
I iterate—this is very, very important. As soon as I look at the data, I have one answer and two new questions. And so I go on, repeating the previous steps to gain a better and better understanding of the problem iteratively.
If I accept that this is how data analysis should work, it shows how deeply dysfunctional the “pulling data” model is—It assumes that I know what exact data is needed to answer all my questions upfront. But that is simply unrealistic. The typical business problems are too complex for that. And what about the questions I have as soon as I answer the first one?
A different way to work with data analysts
I believe in a world where almost anyone can run their analysis using a new generation of self-serve tools and excellent data literacy.
In this world, the Chief Analytics Officer sits in the board meeting, getting inspiration for the analysis that could change the entire direction of the business.
I believe in all that.
But before we redefine analytics career ladders, there is one simple thing we can do today:
Next time you encounter an important decision that should be based on data, resist the habit of requesting the next data pull. Instead, go to the analyst with a question and an invitation to the decision-making table. Not just in the boardroom: in every room where important decisions get made.
You’ll be surprised at what a difference this can make.