When distilling data into meaningful points of view, it is essential to be able to join the dots between the prioritised data observation(s) and why they matter more to the story than any other data point. Without a compelling ‘so what?’, it is easy to dismiss the data and question the robustness of the ‘now what?’. This can limit the potential of your data story to convince or persuade the audience to take the recommended course of action.

As Sanica Menezes, Head of Customer Analytics at Aviva, explains, determining the ‘so what?’ is a key capability for both data experts and data users alike.

Ask yourself, ‘do we really understand the customer and their needs?’ and ‘do we understand enough about our data landscape to be able to identify those needs at the right moment in time?’. Data literacy is part of it, but so is asking the ‘why?’, understanding the ‘so what?’, and being able to join the dots. Those are some very basic skills that we all need to get better at.

An example 'so what?'

Identifying a business opportunity from the insight than can be quantified can generate a powerful ‘so what?’, and prioritising these calculations can have a significant impact on the robustness of your data story.

For example, in the following point of view the combination of the ‘what?’, ‘so what?’ and ‘now what?’ create a powerful point of view to communicate to the category marketing team to inform their conversations with retailers and to determine the pricing strategy for their product portfolio:

We need to target the £20-£22 price point with Products A, B and C in the portfolio (THE NOW WHAT?), as this was the price point that lapsed mainstream shoppers stretched to when buying the category last Christmas (THE WHAT?). By leveraging our position as the best performing trading company last Christmas to secure a fair share of Category X range distribution in Retailers Y and Z (THE ‘NOW WHAT?’), the additional revenue gains at this price point would be worth an additional £800,000 over the Christmas period in 2025/6. (THE ‘SO WHAT?’)

However, it is not always possible to identify an opportunity from the data or to quantify a potential opportunity from the data available. So, without a clear commercial ‘so what?’, what else can you do to add weight to your argument?

Seek out one (or more) of the 4 key ‘so what?’ reasons as to why your insight is essential to the data story:

4 ways to determine the ‘so what?’

1 – Size matters

Size matters because the impact of an insight is often tied to its scale. A small fluctuation in customer retention might be insignificant, but a 30% drop signals a major issue. Contextualising your data with absolute and relative comparisons helps the audience understand the risk of inaction or determine whether the recommendation requires urgent action. By highlighting magnitude, you ensure that stakeholders recognise the importance of your insight and respond appropriately to its implications.

Look for:

• Absolute vs. relative change – A 2% increase in revenue may seem small, but if it represents millions of £$£$, it’s significant!

• Business impact – Understanding how the size of a change or the predicted result affects commercial KPIs helps assess whether action is necessary. For example, while a 5% increase in customer complaints might not be ideal, if it occurs alongside a major operational change, this might be considered a fair and tolerable rise in the short term.

2 – It’s all relative

Insights gain meaning when compared against targets, benchmarks, past trends, or industry standards. A 5% revenue increase sounds positive, but if competitors experienced 11% growth, it signals underperformance. Relative comparisons provide perspective. Without a frame of reference, your data story can be misleading. Understanding insights in relation to other metrics allows businesses to gauge performance effectively, prioritise actions, and refine strategies based on a more complete picture.

Look for:

• Industry standards, market averages and competitor data – Comparing results or indexing to competitors or industry performance highlights where a brand/ product/company is excelling or falling behind.

• Internal benchmarks – Evaluating data against specific targets, previous performance, or different markets provides clarity on whether the data point is insightful. For example, like for like sales may have increased for a product, but it still failed to meet the headroom growth expected based on marketing investment.

• Customer expectations – Understanding customer expectations ensures insights are evaluated in a relevant context, highlighting patterns or causal relationships within the data to show the wider impact of performance. For example, while overall satisfaction with the inbound call experience might have plummeted, when splitting the data by the customers channel preference (phone versus digital channels), the data shows that the decline in satisfaction was amongst those who felt ‘forced’ to call to achieve their objective because their attempt to manage via the preferred digital channel failed.

3 - Make or break?

Some insights reveal turning points, where action (or inaction) can have a significant impact. These insights highlight risks or opportunities that could define success or failure. For example, identifying a critical flaw in an element of the digital customer experience that is a key driver of revenue, might necessitate a red flag for immediate intervention. These insights carry high stakes and demand decisive responses that need to be clearly called out in the ‘so what?’

Look for:

• Potential risks & opportunities – Determine whether the insight exposes vulnerabilities, such as weakness in the funnel or a link to high churn rate, or strategic advantages, such as untapped needs for a high value customer segment.

• Short v long term consequences – Assess whether the insight demands immediate action, for example because it relates to a regulatory compliance issue that could incur a huge financial penalty, or signals slower burn opportunities that require adjustments to the strategy or current business plan.

4 – A link in the chain

Not all insights stand-alone—many act as connections within a broader system. These insights may not be dramatic individually, but their role in a larger process makes them important. For example, a small dip in website traffic may not seem alarming until it’s linked to declining conversions and reduced sales. Understanding how an insight fits into a sequence of events helps in diagnosing root causes and predicting downstream effects. By positioning insights as part of a larger chain, stakeholders can make informed decisions that optimise overall performance.

Look for:

• Upstream & downstream effects – Determine how one metric influences others. Insights that directly impact high-priority areas, such as revenue, operational efficiency, or customer satisfaction, are often crucial in decision-making. Showing the alignment between one measure and another can amplify the perceived importance.

• Root causes – Look for dependencies between metrics to identify underlying causes rather than surface-level symptom. For example, the connection between decreasing employee productivity to an increase in customer complaints.

• Process optimisation – Evaluate whether the insight reveals inefficiencies in workflows, supply chains, or customer journeys, allowing for improvements in overall performance.

How to action with your own data story

Now, it’s your turn.

Take a moment to reflect on your current data story and the key insight you want to land.

Ask yourself:

• Why is this happening.

• Why is this important now?

• What are all the directly relevant and connected factors?

• What factors or conditions contributed to these results?

• What are the most likely or probable reasons for this to occur?

• Have similar patterns/ trends arisen in the past?

• Why does this matter more than other findings we have uncovered?

• How does it relate to what are we trying to achieve?

Ready to transform your data storytelling?

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