Goal First Thinking in
Data Driven Solutions

In this day and age, people expect answers at their fingertips. Banking applications provide us with our bank balance and financial transactions in real time. Navigation applications provide real time traffic information everywhere around the world. The key ingredient in all these applications? Data.

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Tim Pijl – Meet Our Team

Principal Data Analytics Engineer / Team Lead


Over the past decade, people have discovered ways in which they can use data to improve their work and lives. As expected, people began demanding more and more data. Fast-forward to today, people are bombarded by data from all angles. The challenge has shifted: People no longer want data; they want to get value out of it.

The challenge for companies is building solutions –often software applications– that help users get value out of all the data. It’s all about making the data work for users. Data should help them make decisions to take actions to reach their goals.

This leads to goal-first thinking: What are the users’ goals and how can data be leveraged such that it helps them achieve these goals?

Understanding the Goals
Goal-first thinking needs a user-first approach. As written in the Customer Development Manifesto by Steve Blank and Rob Dorf: “There are no facts inside your building, so get outside.”. It is important to put the users and their goals first and determine how data can be leveraged to achieve those goals. To achieve this, it is important to ask the right questions.


Asking the right questions can be challenging. Getting inspiration from Simon Sinek’s book Start with why, we should also start by asking “Why?”. Because how people want to get data, is not why people want data. How alone doesn’t give enough information to deliver value to the users. Consider the following example: A user requests for a raw data feed or API. Simply providing this additional resource might seem like a quick win.

However, the user has only provided information about how she would like to receive the data, not why. It either might be a signal that the user experience needs some more work. Perhaps the user built her own centralised system for handling external data feeds. Simply knowing the how is not enough and can leave room for failure.

Leveraging Data to Reach Your Goals
Firstly, it’s critical to understand the goals of users and which data is available. To deliver data effectively to users –that help them reach their goal–, data should have at least the following four attributes:

  • Intuitive (easy to understand)
  • Convenient (accessible in the right context)
  • Customisable (viewable in unique ways to each user)
  • Actionable (easy to apply insights to produce the intended outcomes)

These four attributes are a necessity for users to obtain their goals easier. For example, consider a case where the data is not easily understood. Users will be unlikely to reach their goal. This can be due to the fact that they abandon the provided solution because they cannot make sense of it. Or, they draw invalid conclusions based on the data due to a lack of understanding.

Secondly, once you have intuitive, convenient, customisable and actionable data, it is important to answer the following question: How to create an output that helps motivate and guide the users to make decisions and reach their goals? For that, a deep understanding of the users is needed, because the way to add value differs from user to user. In general, there are three ways to present the data:

  • No visualisation (e.g. an API)
  • Nonrepresentational (e.g. product recommendations)
  • Canned reports (e.g. dashboards)

To determine which approach is best for a specific user or a group of users, one can create a visual prototype that the users can interact with directly. For example, by using this prototype, a user who requested an “it just works” experience, actually wants to manipulate data directly. The prototype is often used to answer the how and sometimes even the why.

Keeping the goals of the users, the data, and the output aligned, is critical for success.

And lastly, it is important to determine what data must be captured to create the desired output. Do not assume that the immediately available data is the data that the users will find most valuable. When adopting a user- and goal-first approach, be open to discovering new and unexpected solutions that might require a completely different dataset.