What Makes a World-Class Data Professional?

I was recently invited to speak at the launch event of the University of London M.Sc. in Data Science at the Royal Institute of Colombo.

Instead of a generic speech on data science and it being the need of the hour at most companies, the Academic Coordinator encouraged me to touch on the more human-side of working in data.

The following is a raw transcript of my speech at the launch of the University of London’s M.Sc. in Data Science at the Royal Institute of Colombo.

Over the past four years, I’ve been in a very unique position.

I had surrounded myself with some of the brightest minds in problem-solving and Stax, the management consultancy I was at, was transitioning from primarily supporting our foreign consultants to becoming a center of excellence in technology and data analytics. This coincided really well with the growth in interest in data in Sri Lanka and among our American clients.

As we got more data projects, and more complex projects at that, we had to evolve from a rag-tag bunch of coders to an established data science and business intelligence unit. Naturally with this came a lot of pressure to up-skill ourselves and we soon found out that there was a particular set of attributes required to be considered a world-class data professional.

I say data professional and not data scientist because this is relevant for data analysts and data engineers as well. Most companies still haven’t drawn very clear boundaries between these three positions.

The Often Forgotten Ingredient

Let’s get the obvious out of the way.

Every data professional needs to have good statistical intuition. There’s no way around it. Statistics is the foundation of the entire field and at the very least you need to know your descriptive and inferential statistics.

Next, every data professional needs to know how to use their tools and how to use it well. I’m talking about programming languages, development environments, visualization and dashboarding tools, and so on. This is a technical job so it’s a given that you need to be able to tap into the power of your computer and quite possibly a distributed network of computers.

Based on my experience of having worked with data professionals in Sri Lanka and amongst my clients offices, too many data professionals think that this is it. “This is all I need to succeed in business. I know my stats, I know my Python and now I’m ready for the big bucks.”

The issue with thinking that data science is the end and not a means to an end is that it can lead to scenarios where companies thump their chests and boast about the cutting edge models or technologies that they’ve implemented in the workplace. But when you look beneath the hood you realize that they’ve had little to no impact on their customers or operations, because they haven’t solved a business problem with financial implications.

So, what’s the missing ingredient here? What makes a world-class data professional?

The answer is extremely simple but also very powerful: empathy.

If we’re asking our sales people to have empathy, if we’re asking our customer service representatives to have empathy, if we’re asking our leaders to have empathy, then we should demand the same from our data professionals. They may not be on the front-lines unlike the other three, but aren’t these the people tasked with understanding, and making predictions about, our customers, markets, operations, and employees?

Empathy is a powerful trait that influences every other desirable characteristic of a data professional.

Empathy and Problem Solving

Data professionals need to be great problem solvers because they’re in the business of problem solving wherever they may work. What better way to solve a problem than by being empathetic? What better way to solve a problem than by putting yourself in the shoes of those you’re solving it for?

When I was a junior data analyst and somebody asked me to make a report or dig into the data to understand why something was happening, I thought to myself here’s the chance to show off everything I know. I’d make a big report with as many charts, graphs, statistics, and cuts of the data as I could generate and I’d present it to them proudly.

What I learned the hard way was that this was the worst possible thing you could do to a time-crunched executive. Give them way too many things to look at and make it very difficult for them to make a decision. What I realized was that I was lacking empathy by not respecting their time and their intentions. By wanting to show off what I knew, I wasn’t helping them achieve their objective which was to make a data-driven decision.

Empathy and Technology

Empathy even influences the technology (or tech stack) chosen to solve a data problem.

Machine learning unlocks a lot of power and it has a wide number of applications. But with that being said, it’s not always the most optimal solution all the time. Sometimes it’s a very simple algorithm that can be easily explained, or sometimes it’s an Excel model that the client can play around with and validate as well.

At the end of the day, a solution should be based on a client’s level of comfort and preferences because this is the person (or the group of people) who will buy into the solution. The worst thing you can do is build something fancy and expensive that nobody uses.

World-class data professionals know this. They know that the utility of a solution is far more important than its composition.

During our early days as a data science unit, we’d often deal with skeptical clients. People who hadn’t bought into the data-driven decision making movement yet. We discovered that the easiest way for us to convince them was to start with a simple and explainable solution that immediately delivered value and won their trust. We were then able to leverage that trust and familiarity to propose more complex and more sustainable solutions.

Empathy and Communication

You can’t be a data professional if you can’t effectively communicate and empathy is what makes world-class data professionals, world-class storytellers.

They don’t parrot a list of facts, figures, findings, and trends because they know that logic and reason alone don’t influence decisions and actions. Emotions influence decisions and actions.

What I really like to do with any big data finding is look for the supporting small data stories or anecdotes to back up an overarching trend. I like to look for stories about people and their experiences, stories that an audience can relate to, so that they personally resonate with the analysis and that makes it much easier to drive my points home.

World-class data professionals also speak in the language of their audience. They don’t use terms like “gradient descent” or “resilient distributed datasets” if their audience is largely non-technical. Instead, they paint words with pictures and pictures with their words.

Empathy and Resilience

Any data job is a tough job.

Sometimes our clients don’t know exactly what problem they want to solve. Sometimes you have a dataset that’s extremely messy and you need to have conversations with multiple stakeholders to make sense of it. Sometimes you don’t know how to get something done because you’ve never solved this kind of problem before.

Whatever it is, working in data requires a lot of grit and resilience. You’re frequently going to be lost and confused and you’re frequently going to be making mistakes. A world-class data professional understands that being uncomfortable is the way of the game.

But when you have empathy, it makes things easier. When you have empathy, you know why you’re solving a problem. You know who you’re solving it for and you know how their lives will be made easier by your solution. When you understand your purpose and the impact you can have, this big picture perspective will make it far easier for you to be resilient.

The management theorist, Simon Sinek, says that if you ask a bricklayer without perspective what he’s doing he’ll tell you he’s building a wall, but if you ask a bricklayer with perspective what he’s doing, he’ll tell you he’s building a home.

Closing Statements

Speaking of homes, this university was once my home for 3 years.

It’s really great that the University of London now has a Masters in Data Science because if it’s anything like my experience of studying Economics, it’ll require students to not just be able to learn complex concepts but to challenge themselves to apply it to a variety of different problems. It’ll require them to learn as much from their colleagues and classmates as they will from their lecturers. It’ll force them to think from multiple perspectives, including the examiner’s perspective, if they want to excel. And in doing so, it’ll build empathy so they can be better and more resilient problem solvers, technologists, and storytellers.

I look forward to a new generation of world-class data scientists from RIC.