| March 12, 2026
The critical part of all analytics and data pipeline development happens in the last metre. It is where the decision is made and the impact and benefits can start.
Take a moment to think about how you interact with data each week:
- What are the skills and tools you are using?
- How much do you need to interpret?
- Do you know the limitations of the data sets?
One of my first questions is often “What decision are you trying to support?”. The reason for this is to understand the full context for the decision and the data that will be needed to support it. It is not uncommon for us to need to start recording something new or make changes to how telemetry is captured in fulfilling analytics requests.
The next pattern I see is a desire to browse data. For some clients, this extends to wanting
downloadable crosstab extracts. The use case for this is to wrangle data locally in a spreadsheet.
This hides a skills and tooling gap; self-service analytics is great but can lead to an explosion of
metric definitions and inconsistencies month-to-month. Automate where appropriate and reach out to
your analyst and data science teams for help.
Finally, for this article, let us assume the other data quality and governance gaps are solved. Imagine you are looking at the following visualisation, which do you prefer?
Context is important. In this example, adding the target helps you to understand whether the result is good. Consider the context that will help your daily decisions with data. This goes far beyond simple annotations on a visualisation. At their core, analytics and data science are fundamentally about decision support.