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Before we dive into today’s topic I wanted to welcome all of you to my 100th edition of this newsletter: Strategies For Effective Data Leadership.

It feels amazing to have been able to publish this week in week out and to arrive at my 100th edition. It’s not always been easy and there have been plenty of weeks where I’d have easily just given up. But to be here today and to have you as my reader feels incredible. THANK YOU!

(If you’ve been enjoying this newsletter and think it might be valuable to others, please share this link with them so they can subscribe as well!)

👉🏼 Now onto today’s topic: The Data Value Chain

We often hear that for our work in data to be impactful, it has to deliver value to the business. But these statements can be as confusing as they are often vague.

In what sense do we mean value?

What do we mean by impactful?

How does that differ from what we’ve been doing?

I get these sorts of questions all the time. So I thought to myself, what’s the best way I can visualise the end-to-end lifecycle of data value.

To hopefully accomplish that, I’ve adapted the data value chain into the below diagram:

Via the Data Value Chain, I wanted to show that data itself is not the starting point in the value conversation, and that insights and dashboards are not the finishing line.

That these are in fact crucial middle points in the overall chain. By observing the value chain, you’ll see that the start and end-points are not at technical pieces of the puzzle, but rather business activities.

Let’s take a closer look, shall we?

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When we talk about the data value chain, we are not simply talking about pipelines or dashboards. We are talking about the sequence of events that turns information into commercial impact.

The chain begins with a decision that needs to be made. That decision generates a question. The question determines what data is required. Only at that point do engineering and modelling come into play.

From there we generate insight, communicate that insight, and ideally a decision is taken. If that decision results in changed behaviour, a business outcome follows, and if that outcome is measured properly, we can demonstrate impact.

That is the full chain. The end-to-end lifecycle of data value.

Notice where engineering and modelling sit. Notice where insight sits. They are in the middle.

Dashboards etc. are not the end of the chain. They are a (possible) midpoint on the way to value. (I say possible because they are not always needed)

Impact sits at the end and in terms of value, is the most crucial piece.

Most data teams however, build their identity around the middle of this chain. They pride themselves on clean pipelines, elegant models and beautifully designed dashboards. All of that matters of course, but none of it constitutes value on its own. Value only materialises when behaviour changes and business outcomes improve.

Insight is not the destination. It is merely a transition point.

If nothing changes after your insight is delivered, you have just created an output, but no actual value.

Unfortunately, this is where many teams stop. They generate insight, present it, and consider the job done. From a delivery perspective that feels complete. From a commercial value add perspective, it‘s barely halfway.

When teams stop at insight, several predictable consequences follow.

1️⃣ First, they end up optimising delivery rather than decisions. Success becomes defined by how many dashboards were shipped or how quickly tickets were closed, rather than by whether any meaningful decision improved as a result. The team feels productive, but the organisation doesn’t necessarily feel progress.

2️⃣ Second, the business begins to categorise the data team as a reporting function. If stakeholders only ever see outputs, they will naturally treat the team as a producer of outputs and therefore a cost centre. Reporting functions are invited in late, asked to tidy up numbers, and rarely brought into early strategic conversations.

3️⃣ Third, urgency starts to dominate without any corresponding increase in influence. Work framed as requests rather than decisions leads to reactive behaviour. Everything is urgent because everything is a task. When decisions are not explicitly named and owned, the data team absorbs pressure without gaining authority over what truly matters.

4️⃣ Finally, it becomes extremely difficult to defend budget, headcount or strategic investment. It is hard to make a compelling case for growth based on activity metrics such as dashboards delivered. It is far easier to argue for investment when you can point to improved decisions, reduced churn, optimised campaigns or measurable revenue impact. The end of the chain is where the persuasive narrative lives.

The good news is that you do not need to control the entire chain to influence it. You simply need to stop pretending that insight is the finish line.

If you want your data team to be perceived as strategic rather than operational, you must extend your focus beyond insight and deliberately design for decision, behaviour and impact.

We start by defining the decision that needs to be made before any technical work begins. Instead of jumping straight to data exploration or SQL, force the conversation to answer a simple but key question: what will we do differently once we know this? If that cannot be articulated clearly, the risk of building something that goes nowhere increases dramatically.

Next, build follow-up into your operating rhythm. After insight has been delivered, return to the stakeholder and ask what changed. What action was taken? What behaviour shifted? What result did that produce? This habit alone transforms how the team is perceived. You are no longer a producer of information; you become a partner invested in the outcomes.

The following step is to change the way you report on your own performance. Move beyond activity metrics or tickets completed and start tracking decisions influenced, initiatives supported and outcomes improved. Language shapes perception, and perception shapes positioning. When you speak in terms of impact, you are treated as impactful.

Finally, resist vague briefs. When someone asks for a dashboard, respond with curiosity rather than blind compliance. Ask what decision the dashboard is intended to inform and what action will follow from it. This is not about being obstructive; it is about maintaining the value chain at its earliest stage.

Understanding the full data value chain changes how you prioritise, how you frame conversations and how your team defines success. It forces you to work backwards from measurable impact rather than forwards from available data.

If you take away anything from this post, I want it to be this:

Curiosity and conversation sit at the start of the chain

Dashboards and insight sit in the middle of the chain.

Impact and value sits at the end.

Until you deliver impact and measure it’s value, you work as a data leaders is incomplete.

Tristan Burns

Find me on LinkedIn or Book a 1:1 call

Last week I asked you, How to you rate your own literacy around strategy?

Here’s how you responded. Fairly mixed results across the board!