No bullshit data: Tim Wiegels' uncomplicated approach to business-driven data science

In the fast-paced world of data science, Tim Wiegels brings a refreshingly blunt and business-focused approach. Eschewing the typical data-centric mindset, he zeroes in on what really matters: aligning data with the core needs of a business. As the former VP of Data at Free Now, Tim mastered the art of scaling up data infrastructure and assembling powerhouse teams, all while cutting through the technical jargon to keep things real. His philosophy? Forget getting bogged down in every tiny data detail – it's all about those few critical KPIs (key performance indicators) that truly drive a business forward. Tim's straight shooting and pragmatic style, combined with his knack for lifelong learning and adapting to new challenges, makes him a standout voice in the world of data.

What is your secret sauce as a data expert?

My approach is somewhat unconventional, I tend to listen less to the data teams and more to the business side. The skill of working with data becomes increasingly easier to learn; the real challenge is in making it work for the business. It's about understanding what management, marketing, and other departments need, and then addressing those needs with data. The issue I often see is that data teams get caught up in doing their work in a technically proficient way, which is fine, but it doesn't necessarily move the business forward. You've got to make that connection between data and business needs. That's where the real progress happens.

And what goes into your approach?

My approach is all about merging business insights with data and really understanding what the business is doing and what it needs. And then I usually try to boil this down to three to five numbers. I find that getting lost in the details of every single part of the funnel or obsessing over the KPIs of this KPI… is all bullshit in my opinion. With about five critical KPIs, everyone in the company can understand the whole business, its problems, and its needs – those few key indicators tell the whole story.

As former VP of Data at Free Now, what key strategies did you implement for handling large data products?

Scaling up data infrastructure at Free Now was a massive undertaking. In the early stages, setting up for a small company is straightforward. But as you grow, the data grows exponentially. We're talking about an increase by tenfold within the five years from when I started to when I left. It's one thing to set up; it's another to scale up effectively.

Then there's the challenge of building the right team. It's tougher than just dealing with data or tech. The talent pool is limited, and not everyone's cut out for this kind of work. At Free Now, we grew the team from 15 to over 100, hiring around 200 people over five years. But finding the right people is just half the battle. You also have to educate the higher-ups. You'd be surprised how many times I've had to explain the difference between a data engineer, a data scientist, and a data analyst to CEOs who think they're data-driven. They often want to hire a bunch of data scientists without understanding what they actually need. It's about more than just filling positions; it's about finding the right fit for the job at hand.

When do you think is the best time to implement healthy data strategies in a company?

When it comes to implementing data strategies, it's all about the company's growth and activities. You've got to start early and set it up in a way that's scalable, keeping in mind what's down the road. The initial setup should be pretty much in line with what you'll need later on. It's not just about getting things up and running; it's about planning for the future from day one.

But here's the thing: I've seen companies with top-notch awesome technical setups, and guess what? They barely use the data. They've got these great tech teams working in their own bubble, creating impressive systems that are completely disconnected from the business's actual needs. Sure, with all the cloud and automation tools available now, it's easier to build a solid system. But the real trick is making sure that system aligns with what the business actually needs, not just what looks good on paper.

You're known for explaining technical subjects in simple terms. Is this an innate skill or something you've developed?

It comes pretty naturally to me. Early on while doing my PhD, I realised I had to be the guy who bridges the gap between hardcore techies and everyone else. I've seen too many smart people, real nerds, doing amazing work but failing to make it understandable. Early in my career, I saw some terrible presentations and thought, "No way, that's not going to be me."

It's partly a personality thing too. I like being on stage, making sure people get what I'm saying. You've got to speak their language. That's what I push for in my teams. But it's tough, right? The data world is full of introverts who are great at what they do but not at explaining it their stakeholders. My approach is to listen first and then explain things as simply as possible, like the 'Explain Like I'm Five' concept. It's about making complex data understandable and trustworthy, avoiding any misconceptions or biases.

What's your strategy for selecting the right candidates for your data team, especially in terms of team dynamics and communication skills?

My approach to hiring for the data team is pretty straightforward but effective. I tell the HR manager to consider if they'd like to hang out with the candidate outside of work, like grabbing a beer or just chilling together. It's not all about their data skills; it's equally about how they fit into the team culturally. In the final interview, I focus on three questions:

1. Their favorite project and why
2. Their interest in the industry of the job (i.e. at FREENOW that was mobility)
3. Their plan for the first 100 days

This helps me understand not just their technical abilities but also their communication skills and team fit.

These questions are quite revealing. Often people start blabbering about some very technical subject, but fail to convey the real impact of their work. This method helps me identify those who can not only do great data work but also effectively communicate and integrate with the team. This approach is efficient too – within 15 minutes, you can gauge a candidate's fit. By the time they reach me, their technical skills are already vetted; I'm looking for their ability to be a team player and articulate their work clearly.

How do you keep on top of your game in a constantly evolving field?

This was tough when I was the VP of Data at Free Now, mainly because there just wasn't enough time. I tried following blogs, but honestly, I'm not great at that. Conferences? I attend them mostly to speak, and maybe I'll catch a few talks, but they're not my main source of new information.

What really works for me is doing podcasts and networking, but not in the usual way. I run this weekly thing on LinkedIn where I listen more than I talk, picking up insights from others. Networking for me isn't about schmoozing at conferences; it's about having real conversations with people in my network, hearing what they're up to. As a consultant now, I'm not boxed into any specific tech or method. I'm more focused on the problem at hand. If it means learning new tech stacks, so be it. In fact, I've picked up about five new ones in the past year alone. This job is a journey of lifelong learning.

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Tim’s tips

• Book: The Effective Manager" by Mark Horstman has been influential in understanding team leadership.
• A piece of advice that has guided your career: A colleague, once told me that I’m good at giving fact-based feedback in a kind way, yet I'm not afraid to point out painful truths. He advised that when I no longer feel able to do this due to company politics or other frustrations, it's time to move on.
• Any content you follow for inspiration? I rely heavily on tools like ChatGPT to stay informed and find solutions. It's efficient, providing about 70% of the information I need, which is often enough to delve deeper into any topic.
• Podcast: I actually don't listen to podcasts, which is funny because I like to speak in podcasts a lot, but never got around to do that. Click here to listen to one of Tim’s podcast episodes.

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Join Tim on February 8 in Paris for his Pollen session, titled ‘Predictive Analytics for customer behavior.’ His session is tailored for Data leaders and intermediates, C-level executives, and professionals in performance and marketing. Don’t miss this opportunity to learn how to generate useful insights, use data science to predict customer behaviours and empower your business to take better decisions. For more information and to register, here.