Building your data team like a startup

Almost 14 years ago, in early 2011, I started my job as Head of Business Analytics at Rocket Internet. At the time, Rocket Internet was the world's largest venture builder and produced global and regional powerhouses such as:

  • Zalando (leading European fashion retailer)
  • HelloFresh (the world's largest meal-kit provider)
  • Lazada (the Amazon.com of South East Asia)
  • Delivery Hero (leading food delivery company)

Three years into my job at Rocket Internet, I faced a huge problem that many data leaders encounter: business users weren't using what we built.

Where did we go wrong?

We had done what many data teams do: drafted massive technology diagrams, built incredible data infrastructure, and assembled a team of elite engineers and analysts. Yet we still didn't have the impact on the business that Rocket's shareholders expected from us.

It took me a few years to realize the problem: our data team didn't have product-market fit!

Product-market fit usually refers to the stage in a startup's lifecycle where its products satisfy strong market demand, demonstrated by high customer adoption, retention, and enthusiasm.

We had none of those:

  • No adoption
  • No retention
  • No enthusiasm

The reason for our users' lack of adoption and our team's failure to create business impact was the same reason why many startups and small businesses fail to achieve product-market fit: We built a great solution but lacked understanding of many other critical components needed for success:

  • Who are our users?
  • What are their problems and unique challenges?
  • How do we distribute our (data) products to our users so they'll use them?

Building your data team like a startup

For many years, startup founders have relied on a framework called the "Lean Canvas" to validate business ideas and build their companies. Invented by entrepreneur and author Ash Maurya, it's essentially an actionable, one-page business plan.

In this episode, I want to introduce a data-team-adjusted Lean Canvas that you can use to craft your data strategy and build out your data initiative.

The original lean canvas exists in quite a few variations but it essentially looks like this:

You can see that the canvas can be sub-divided into:

  • Market Risk
  • Product Risk

Market Risk = Do people want what I am planning to build?

Product Risk = Can I deliver and defend what I am planning to build in a way that makes economic sense?

Product Risk for data teams is typically low.

Market Risk is typically high.

The big mistake most data teams make is that they spend 99% of their time on Product Risk and ignore market risk.

I want to introduce a data-team-adjusted lean canvas that will help you focus on market risk first and then tackle product risk.

The data-team-adjusted lean canvas

During my years working with the Lean Canvas to build data teams and strategies, I noticed that building a data team or initiative is similar to building a startup, but not quite identical. 

I therefore adjusted the Lean Canvas to better suit data teams.

This is what it looks like:

Notice how the sections on the Market Risk side haven't changed!

On the Product Risk Side, I did the following replacements:

  • Channels -> Distribution Strategy (same principle, only different wording)
  • Unfair Advantage -> People Strategy + Management & Systems Strategy
  • Key Metrics + Revenue -> Outcomes

It is very important to tackle the lean canvas in the right sequence:

Market Risk first, then Product Risk!

A key goal is to eliminate risk from your strategy, and your Market Risk is much higher than your Product Risk!

The exact sequence is:

1 Problem

Understand and validate your user's unique challenges, problems, and existing ways of doing things.

2 User / Organizational Strategy

Define roles and responsibilities between business users, the data team, and data producers by understanding the personalities, skill levels, and preferences of users

3 Unique Value Proposition (UVP)

A short statement that describes how your data team achieves to solve your users' problem(s) better than existing alternatives (existing alternatives = gut feel, doing things manually, not using data for decision making, etc)

4 Solution

Many data teams exclusively focus on this part of the lean canvas. This is where you define your dashboards, analyses, tables, pipelines, etc. 

5 Distribution Strategy

Just like any other product, data products need to be brought to market. This part describes how you plan to achieve user adoption for your (data) products and how you will make users aware that they exist.

6 Management & Systems Strategy

Your management system is a set of automations and standard operating procedures (SOPs) that helps you to produce consistent outcomes reliably.

7 Outcomes

This section quantifies how you will create business impact with your data team and documents successes.

8 Cost Structure

This section highlights the costs of running your data team.

9 People Strategy

Includes your principles regarding:

  • recruiting, hiring, and onboarding people
  • managing people

Bottom Line

Building a successful data team requires so much more than building a great data infrastructure, fancy machine-learning models, and well-designed dashboards!

If you are facing problems with stakeholder adoption or your Executive Management team complaining that you're not creating enough business value, take out the data-team-adjusted lean canvas and identify your weak spots.

Remember: They are usually on the Market Risk side!

P.S.: Our Data Action Mentor masterclass - Create massive business impact with your Data Team is launching on January 16! The masterclass will dive deep into every section of the data-team adjusted lean canvas! The first 100 buyers will get 50% off ($99 instead of $200). Sign up for the ​​waitlist​​ now!

Whenever you need me, here's how I can help you:

Data Action Mentor Masterclass: Create massive business impact with your data team. 

This class is built for ambitious data professionals and data leaders. I spent 17 years building data teams for high-growth companies such as Rocket Internet, Zalando, Takeaway.com, Lazada (acquired by Alibaba), and many more. In this class, I am sharing all my lessons, failures, and successes so that you can make your stakeholders and CEO happy and accelerate your data career.  

Impactful Data Teams for Scale-ups

I build data infrastructure and data teams with immediate business impact for global b2c scale-ups and grown-ups in e-commerce, insurance, fintech, and consumer subscription. My proven approach has helped dozens of scale-ups. I build the infrastructure at a fixed price and then empower the business to move rapidly from data to action. If you know a consumer internet scaleup that needs an impactful data team, hit me up!

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