Data stories: the glue of the Analytics Cycle.

Anyone working in analytics will be familiar with the Analytics Cycle, in some shape or form. It’s an Analyst’s version of the Scientific Method (perhaps even, justification of the term “data science”).

The Analytics Cycle outlines the high-level process followed by Analysts to uncover data insights and ultimately add business value.

A traditional Analytics Cycle

Ask: Consider the business problem the project is attempting to solve.

Prepare: Decide what data sources are suitable and prepare the tables for analysis.


And yes, this bit takes ages 🤯


Explore: Understand the data and the subject it represents (customers, products etc.).

Model: Calculate further insight with advanced statistics and computing power.

Act: Apply the insight to a business process.

Evaluate: Understand how your insight-driven action performed.

Simple right... but is this real life?

For a business looking to harness analytics, the steps in this cycle have almost become a check list. But modelling is the exception, not the rule. Many actions can be taken from insight uncovered during exploratory analysis.

A more realistic Analytics Cycle

A business is more likely to go through the below Analytics Cycle (even in an ideal world).

Modelling should never be undertaken if there is not a clear use case for the output.

Modelling is a nice to have (if required) but in many cases not necessary to answer the initial business question.

Sharing insight makes the Analytics Cycle go round.

There are at least two moments in the Analytics Cycle when insight should be shared with the wider business.

  • After exploring, and before acting.

Regardless of if any future modelling will occur, exploratory insight should be shared with the business. This allows those with more knowledge on the subject (as opposed to data on the subject) to confirm or question its representation. It is better to pick up discrepancies at this point, than incorporate these into a model.

Insight uncovered through exploratory analysis could also be actioned by the business, without the need for a model.

  • After evaluating, and before asking.

Incorporating insight into existing business process is the usual application of it, but if we don’t measure and learn from this we are doomed to repeat the same mistakes. Understanding how effective (or not) a change has been, helps generate new questions for the next analytics cycle.

Kick it old school.

Distributing data to business users without real-world (or better yet, “their-world”) context, will not inspire them to act. Your pile of sundry facts is not motivation for someone to invest time on a data project, where the value is not clearly obvious. This barrier for engagement creates a huge gap in your analytics cycle.

But humans are evolutionary hard-wired for narrative. Data stories, when used as tools for communication, help bridge the business-analytics “engagement gap”. They paint a bigger picture and evoke an empathy for what the numbers actually represent.

Data stories are an effective way to engage internal business users with analytics.

Avoid the modelling silo.

One of the biggest traps an analytics team can fall into is the modelling silo. This occurs when analytics is practised with no input from the rest of the business. This is an expensive use of resources and can be dangerous if any output is ever actioned (although most of the time it isn’t).

The output of analytics is information that may enhance a business.

But analytics should not operate in silo.

So share the insight you find… and tell a data story.