You’re measuring more than ever – but is it driving better decisions?

About a year ago, I wrote a series of blog posts exploring what it really means to work in a data-driven way. The point was simple: having more data doesn’t automatically make an organisation more data-driven.

It takes much more than collecting data and setting KPIs. We need to build a culture of continuous improvement and experimentation. We need to understand what is actually worth measuring, then make the right data available, and understand how different metrics relate to one another. We also looked at how to analyse deviations and how metric trees can help us make those relationships visible.

But there was one question we never really addressed: how do you turn all of this into a continuous way of working that actually leads to better decisions?

That’s where we pick things up now. And this time, there’s a new factor that’s changing the game significantly: AI.

The gap: ambition vs. decisions

When we build data and analytics solutions with our customers, we keep seeing the same challenge: it’s still difficult to get KPI-driven decision-making to trickle down to every part of the organisation.

All too often, “we’re going to become data-driven” remains an ambition rather than becoming part of how the business actually operates. There may be a handful of strategic metrics being tracked by the management team, but further out in the organisation, the link between data, analysis and the decisions people make is much weaker.

And it’s not just our experience. According to analytics company BARC, 58% of companies base at least half of their day-to-day business decisions on gut feeling and experience, rather than data. Among the lowest-performing companies, that figure rises to 70%, while the highest-performing companies rely much more heavily on information.

So, we’re collecting more data than ever. Yet when it comes to making decisions, many organisations still fall back on gut feeling.

The problem, in other words, is rarely a lack of data. What’s often missing is a shared foundation and a clear way of working that puts data at the heart of how the business is run.

SO, WHAT DOES IT TAKE?

We believe organisations need a clearer operating model – one that runs through the entire decision-making process, involving far more people than just the management team. That model needs to be built on two foundations: relevant accessible data, and clear ownership.

One way of bringing these elements together is what we call Metric-Driven Decision Making (MDDM).

SAME PRINCIPLES, NEW POSSIBILITIES WITH AI

The hard truth is that very little of what I’ve described so far is particularly new.

Metric trees, structured business reviews, a shared data foundation and clear ownership are all principles that well-run organisations have been using, in one form or another, for many years.

And the fundamentals still matter. Relevant data needs to be available. Someone needs to own it. Someone needs to develop and improve it. And the organisation needs to actually use it.

What has changed is what’s now possible.

AI is beginning to transform the entire chain: how we build and maintain our data models; how we analyse our metrics and identify the reasons behind deviations; how we prepare for and run our business reviews; and, ultimately, how quickly we can move from “something has happened” to “we understand why – and we know what to do about it.”

AI doesn’t replace the way of working. But it can make a good way of working considerably more powerful – and enable us to scale it across more parts of the organisation.

That is what makes Metric-Driven Decision Making particularly relevant right now.

WHAT’S NEXT?

Over the coming weeks, we’ll explore this in a three-part blog series, taking us from the MDDM way of working all the way to a functioning Enterprise solution.

• First, we’ll look at the core of MDDM: how to choose the right metrics, build the metric tree that makes relationships visible, and review them through a Weekly Business Review. This is what gives us the prioritisation map – a clear view of which data is actually worth building and maintaining.
• Then, we’ll look at how to prepare and build the data foundation, ensuring that our metrics are based on a shared and reliable source of truth.
• Finally, we’ll establish the roles and ownership needed to make MDDM a way of working that lasts – rather than yet another project that eventually falls by the wayside.

Throughout the series, we’ll follow the same thread: how AI is changing what’s possible at every stage, and how it can help more organisations make this approach work in practice.

Until next time, it’s worth asking yourself one simple question:
You’re probably measuring more than ever. But is it actually leading to better decisions – across the organisation?

In the next part, we start building the foundation.

/Jonas