What does it really mean to be a data-driven leader?

Published 8/24/2023

What does it really mean to be a data-driven leader?

The first step is to stop calling it data-driven and start calling it goal-driven. It’s the movement towards your goals, where data and analytics play a key part. It’s like saying you’re petrol- or diesel- or leg-driven, when actually what matters is where you are and where you want to go…

Chances are, if you really drill down into it, you probably don’t have the data you need to measure your goals. This is because in the worst case, you haven’t spent the time as a leadership group defining and agreeing what your goals are. Or, best case, you’ve not considered where or how you're collecting the data you will need to measure progress toward your goals. 

So what does it look like to be goal-driven?

Let go of politics

To be goal driven means unprecedented understanding, transparency and consensus about where you are and how far away you are from your goals, and being clear about the collective roles you and your team play in working towards them. Politics and opinion have no place in a truly goal-driven organisation. You have an opinion and it’s probably right, given your experience, but are you brave enough to turn that opinion into a hypothesis and put it to the test? Maybe there’s another opinion that is more impactful. Wouldn’t you be better off pointing your precious resources at the highest impact initiatives versus a pet project? What will improve your reputation more: demonstrating responsible leadership by setting good goals and achieving them? Or spending millions on a project that no one uses? 

One source of truth

“Single source of truth” or “golden source of truth” are phrases that are bandied around willingly by data and analytics practitioners and business leaders alike. But a source of truth that is a data set, is not the truth, because there is still an enormous amount of manipulation and transformation that can happen from a data set. 

A single source of truth is a single or set of metrics that represent a goal. They have been defined, agreed, validated and tested and only exist in one place. If you and your team have created your own different versions of the truth, then you will spend more time arguing over whose truth is correct versus what you will do to reach your goals. We see this phenomenon playing out time and time again.

Analytics literacy

A basic level of analytics literacy is critical across the team so that the right questions, interpretations and confidence can be had from the metrics you are interacting with. 

  • Knowing or demanding the assurance of the lineage and quality of the data behind the metric is key. Otherwise, you could be led down the wrong path with hastily prepared metrics from poor quality data, collected for a different purpose. You could, for example, use a tiering system to denote metrics or outputs that are “Gold” (are checked, verified and signed off) “Silver” (are checked but not verified or signed off) “Bronze” (are not checked, verified or signed off).

  • Having a comparison for context; “are we better than last period?”, “are we closer to our target?”, “is our share getting larger?”. These questions enable you to have a far more effective conversation and allow you to target the most critical areas as a priority. 

  • Using ratios or rates; too often we see reports displaying absolute numbers. You might know if 1000 widgets more this quarter is good, but does everyone else? What is the rate of change? What proportion is it? These questions also enable you to focus on the areas that matter and turn the dials to match your strategic objectives.

Collective cadence of review

More often than not we see single stakeholders asking single questions of single analysts, with no macro view of duplication or set of standards being followed by the analyst. The result? Multiple sources of truth, duplication, siloed interpretation and ultimately, divergence. Metrics that truly matter and represent your goals must be reviewed collectively and regularly. We call them “Goal-Oriented Ceremonies”. A regular forum or meeting of leaders with roles to play in the goals you’ve set, regularly reviewing metrics that matter and discussing progress, gaps and actions. This drives transparency and accountability into everyone’s role in helping the organisation achieve its goals.

Metrics as product

Do you find yourself waiting till the eleventh hour to get the final numbers for the board pack with last month’s figures which seem to change multiple times right up to the minute you front up to the Board or an Exec meeting? You’re not alone, it’s common. 

Static reports and board packs do not support the dynamic nature of decision making. Good decision making requires discussion, interrogation, comparison and interaction with a metric. We have a saying: Reports for regulators and dashboards for decisions. Compliance is a point in time; it’s either compliant at a point in time or it isn’t. A static report achieves this easily and lends itself to automation readily at minimal cost. Decisions are dynamic and require a far higher fidelity experience. If considerable time is not being spent deeply understanding the decision making process, and the visual experience you need to have with a metric that supports that process, you are not goal oriented, you are compliance oriented, check-box oriented.

Foundations first

Far too often we see organisations investing heavily in “Advanced Analytics” without having first achieved a solid foundation of measurement that represents their goals. 

Usually this is driven by passion projects to achieve status, or FOMO that others are doing these things and you’ll be left behind. We’ll let you in on a little secret: Almost no one is truly delivering significant value with AI or Machine Learning, and if they were, they couldn’t prove it. 

This is because the basic, foundational measurement doesn’t exist to measure the impact these capabilities are having. Or even worse, you’re not even collecting the volume and quality of the data you need to make these sophisticated tools and methodologies work. Data Science, to most, is a dark art. Are you qualified to truly challenge the outputs of these technologies? Does the appropriate explainability and documentation exist to give you confidence that what these tools are telling you is actually correct? Establish the foundations, test your hypotheses, then deploy these capabilities if they truly are the best tool you need to achieve your goals. You may find that it's not that complicated… 

These are just some of the characteristics of what we see as being a “goal-oriented” organisation. Stop focusing on the data and start focusing on measuring what matters and bring those measures to the forefront of your daily, weekly, fortnightly, monthly cadence of review, agree, act.

If you want to hear more about how we support organisations to build this kind of goal-driven culture, we’re happy to have a (no obligation) chat. Book in a call via the link below.

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