Data-driven leadership. 7 ways to make better decisions.

How to make better decisions with data (without turning the company into a digital panopticon)

A few weeks ago, I received an email from one of the consultancies. They had read my earlier publication on Internet of Behaviour and thought I would be a good speaker at their upcoming conference. Delighted by the offer, I agreed to talk about the details.

However, the longer we talked, the more and more clearly I understood that there was something hidden in the lecture that caused me concern. At some point, it became clear that what my speakers needed was a presentation on how to use the Internet of Behaviour to extract even more data about employees from the corporate environment, so that it could then be used to make decisions.

It wasn’t about better understanding how teams work, or improving people’s wellbeing. It was just about how to track movement, interruptions, every second of inactivity. To count more accurately how much a minute’s coffee break, a walk to the toilet, a hanging gaze in a window costs.

I declined without hesitation.

My attitude to the IoB has remained unchanged over the years and is largely sceptical, not to say critical. Not because the tool or the data itself is bad. It is because all too often this technology, instead of serving good purposes, starts to serve the worst ones. Just look at how it is used by authoritarian states, where algorithms give citizens points for “right behaviour” and take them away for “wrong behaviour”.

I understand the need for leadership and data-driven decision-making. I write about them myself and practice them myself. But not in this way. Never at the expense of the dignity of the people I manage.

On the one hand, experts talk about using facts wisely. On the other, they create systems to squeeze every drop of productivity out of people, preferably without asking them.

This is a dangerous combination. Especially as the gap between companies that are able to use data to make meaningful decisions and those that continue to wallow in the chaos of scattered spreadsheets and managers’ hunches grows faster than anyone expected.

Gartner showed , up to 87% of organisations globally are stuck at the lowest levels of data maturity, falling into the “basic” or “opportunistic” categories.

The Boston Consulting Group showed in its report that the percentage of highly mature organisations dropped from 13% in 2021 to just 8% in 2024.

What this means in practice is that the narrow group of companies that can work with data are outrunning the rest by an ever increasing margin, growth and competitive advantage

Since the gap between companies is widening like this, there is more and more pressure on the bottom line. In turn, the greater the pressure, the easier it becomes for someone to think that instead of using data more wisely, they should simply start monitoring people more.

That’s why I sat down one evening as the November sun was fading outside the window and started thinking about how to help leaders start thinking with data, but in a way that doesn’t take away people’s dignity, but protects it.

After all, a good leader is able to combine facts, context and people’s emotions into one coherent decision, and knows how to show what he or she has based it on.

Data-driven leadership ≠ digital surveillance

To begin with, I want to separate two things that sound similar to many leaders but carry a completely different management philosophy.

On the one hand, there is data-driven leadership – which is a way of thinking in which a leader relies on facts rather than last week’s sentiment. He is aware of his biases and cognitive distortions. He combines dry numbers with conversations with people, with observation of corridors and meeting rooms, with a sense of the team atmosphere. He is able to name why he made the decision he did and not be afraid to revise that decision later.

On the other hand, management by control – and this does not necessarily always mean cameras over the desk or tracking mouse movements. It often looks much more subtle. The manager introduces so many indicators and requires reporting on so many details that the employee spends more time proving that he or she is working than on the work itself. Every step needs to be justified and every decision needs to be approved beforehand. In this way, people stop thinking for themselves because it is faster and safer to just do exactly what is expected of them. Data, which was supposed to help with decision-making, turns into a leash on which the team is held.

Let me make it clear that the latter is not leadership, and certainly not data-driven leadership. It is simply the old, well-known philosophy of “I don’t trust you, so I need to have everything under full control”.

Research shows that such a climate does real damage to an organisation. The American Psychological Association points out that among employees subject to electronic monitoring , 56% report feeling tense or stressed at work.

And a study by the Chartered Management Institute in the UK showed that around a third of companies have started using so-called “bossware” to track employee activity, with some managers openly admitting that such practices undermine trust and prompt people to leave. In other words – the more you scrutinise every move, the greater the risk that you will lose not only productivity, but also people.

The problem is that, at the level of ideas, the two approaches I mentioned look very similar. In both cases, some manager might say – “we want to be more data-driven”. It’s just that underneath that one sentence, there can be completely different intentions. to some, it’s about smarter decisions that take the human into account, while to others it’s about cleverer tools to squeeze even more work out of people for the same salary.

Therefore, the first fundamental question every leader should ask themselves is relatively simple.

Do I use data to understand people better and make smarter decisions, or do I use data to have more power over them?

If you honestly answer this question for yourself, the rest is just a matter of tools, processes and habits. But without this honesty with yourself, the best data and charts of this world will not help you become a better leader. At most, they will help you become a more efficient supervisor.

Data is there to help you make better decisions

Personally, I like data for one rather simple reason – they organise my view of the world. They allow me to see reality as it is, with all the messiness, tangled patterns and unexpected dependencies. They can pull a signal out of the noise and reveal the direction of change where the naked eye can only see everyday movement and confusion.

But pure data don’t tell me who to hire, who to give another chance, what to do with a team that is tired, scared or just plain burnt out. They don’t tell you how to tell someone that their role in the company is ending, or how to welcome someone new so that they really feel part of the team. That is still up to me – as a human being with experience, intuition, values and responsibility for other people.

You can think of the data as a map of the terrain, and of yourself as someone holding a compass. The map helps you see where you are and what your surroundings are like, where there are hills, obstacles, shortcuts, and the compass helps you get an idea of the directions. But it’s up to you to decide which way you set off and at what pace, taking into account the condition of the team, the weather, the risks, the resources available and the destination you’re aiming for.

However, if, often completely in passing, you start using data more to judge people than to understand what’s happening, any deviation from what you planned starts to look like someone else’s personal loss. When you’re just looking at the numbers on the screen, it’s very easy for the thought to pop into your head – “they’re not trying!”. When you add in automated reports, algorithms and AI, it’s not hard to push the boundary step by step, where you’ll try to bump up every score by a few per cent, every bar on the graph pulled a little higher. At some point, the human being in your Excel stops being a specific person with a history, fatigue, talents and limitations, and becomes just another cell in a table.

From my personal observations, the companies that are most bowled over by the vision of oversight through data very often have a terrible order in them. They do not track the true cost of bad decisions, but instead are very keen to invest in solutions that give a sense of control, rather than seeking to acquire those that give real value and a better understanding of the environment.

Intuition versus thinking with data

If you read or listen to my content on a regular basis, you probably know that I have a fair dose of analytics in me. I like numbers, I like to organise reality into clear categories, I like to see facts laid out clearly next to each other so that you can see what follows from what. At the same time, the humanistic part of my personality makes me know that there are decisions that cannot be plotted in a spreadsheet. There are times when you have to trust something that is hard to name and sits somewhere between your stomach and your heart.

Intuition is often a condensed experience that your brain tries to suggest to you in the form of a hunch. It is the sum of thousands of similar situations you have experienced, patterns you have recognised, mistakes you have made. Intuition is the wisdom of the body and the subconscious, which is sometimes worth listening to.

The problem arises when you fetishise intuition and say to yourself – “I just feel the market”. Or when you blindly trust the data, figuring that since it came out that way in the report, there is nothing to talk about. Both of these extreme approaches are traps. The first leads to arrogance and blindness to the facts. The second to paralysis and cutting yourself off from the human dimension of decisions.

Interestingly, most companies are still living precisely in these two extremes. In a BARC survey, a staggering 58% of organisations admitted that at least half of current business decisions are based more on gut feeling and experience than on hard data. In contrast, a survey described by Greenbook/Talend showed that 36% of senior managers explicitly say that they mainly trust their intuition for most decisions anyway.

It is worth mentioning at this point Gary Klein – a pioneer of Naturalistic Decision Making – who studied firefighters, pilots and doctors making decisions under pressure.

His groundbreaking 1985 study involved 26 experienced firefighters (with an average of 23 years’ experience) and 156 decision points. He found that in more than 80 per cent of cases, firefighters did not compare the available options, but recognised the situation as typical and knew immediately what to do.

Only in less than 12% of the decision points was evidence found of simultaneous comparison of two or more alternatives. This means that a firefighter in a burning building is not analysing options, but reacting based on patterns built up over years of practice.

In contrast, in 2009, fellow researchers Kahneman and Klein, despite representing opposite schools of thought, jointly published an article in the American Psychologist in which they identified conditions when intuition can be trusted.

In an interview with McKinsey, Klein emphasised that for this to be possible:

• “the environment must have a certain structure and predictability that allows a basis for intuition to develop. If the situation is very unstable, there is no basis for accurate hunches,
• the decision-maker must be able to learn through feedback, because only regular feedback on the results of his or her decisions allows expertise to be strengthened and developed.

Kahneman added an important caveat in the same interview. He would only trust the intuition of experts when they are dealing with something they have dealt with many times in the past. Surgeons, for example, perform many operations of a particular type and learn what problems they will encounter. But when the problems are unique, he would be less trusting of intuition.

From my own experience, I can say that there is something that helps me navigate between intuition and reliance on data.

It’s critical thinking, or the ability to separate fact from opinion, accuracy from inaccuracy, signal from noise. It’s looking beyond the obvious sources of information and questioning the assumptions we take for granted. It’s also breaking down complex problems into smaller pieces and looking for connections that are not apparent at first glance.

Just as importantly, critical thinking is also being aware of what we don’t use when making decisions – that is, emotions, guesses, preconceptions or the belief “because we’ve always done it that way”. This doesn’t mean that emotions are bad. It just means that it is useful to know when they are guiding our conclusions and when we are actually basing them on sound reasons.

So before you make any important decision, ask yourself – “what specifically am I basing my belief on?”.

If the answer is mainly recent situations that have stuck firmly in your memory, one or two anecdotes, a single conversation that made an impression on you – this is not data. It is just a handful of memories, snippets of someone else’s story and your own emotions disguised as hard facts. The brain loves to play tricks like this. It can build a whole story out of one dramatic event about how the world “always” works, and then it persists in looking for corroboration of that story and misses everything that contradicts it.

On the other hand, if you have a beautiful report on the table with colourful graphs but can’t answer what data went in there, what period it came from, who collected it and (probably most importantly) with what intention they did so, that’s not data either. Especially since many reports are produced to a very specific order, for a thesis, strategy or decision that someone already wanted to make anyway. Then what you see is not a neutral picture of reality, but a nicely packaged interpretation that can be just as misleading as a hunch.

Today I want to invite you to build some concrete data thinking habits that help you understand reality better, don’t turn people into numbers, and allow you to stand calmly in front of your team and say “This is the decision I made (or we made). Here are the facts we relied on. Here’s what we expect. And here’s how we’ll check in a month’s time if we were right.”.

Five mistakes that make leaders make worse decisions (despite the data)

It’s easy to say “rely on data”. It’s harder to spot the moments when we do it wrong. We have charts, reports and numbers in the presentation. And yet the decision we make has little to do with what that data really shows.

Consultants from Bain & Company analysed hundreds of companies across a range of industries and showed that the effectiveness of decision-making and enforcement is approximately 95 per cent correlated with an organisation’s financial performance – whether we are looking at revenue growth, return on capital or total shareholder return.

So if your decision-making is chaotic, sooner or later it will reflect on the whole organisation.

Mistake one – you know the decision before you see the data

This happens more often than we would like and occurs when a decision is simply born in our heads. It could be a vision that as an organisation we are releasing a new product, or running away from a particular market, or changing the overall team structure. Then we feel it somewhere in our bones, we see it clearly in front of our eyes and only then does the thought come that it would be useful to support it with something. So that people do not say that we are acting on our feelings alone.

And then the “hunt” begins. Someone in the team gets tasked with “do research”, someone else has to pull reports from last quarter. From a whole heap of possible data, we select those that support our narrative and anything that doesn’t fit is “not representative”, “out of date” or “random”.

Except that this isn’t working with data – it’s looking to justify a decision we’ve already made anyway. The real work with data starts somewhere else entirely. It’s when we’re ready to see the numbers that tell us “you were wrong”. And we don’t kill the messenger, we don’t look for a hole in the methodology, we don’t say “but the context was different”. We just sit with that discomfort and give it a chance to change our thinking.

Mistake two – you treat stories like facts

The second mistake is more insidious, because we often don’t even notice that we are making it. It involves treating single situations that have stuck firmly in our minds, one or two anecdotes or single conversations that have made an impression on us – as if they were hard data. It might be a team member who wrote an emotional burnout message, or perhaps a project that spectacularly floundered just before the release date for production.

Of course, all these stories are important because they happen to be the first warning sign that we should not ignore. However, the problem only arises when we treat them as the only complete explanation of reality. Our brains then like to add the rest. Suddenly “all customers are complaining”, “all employees are frustrated”, “all projects are late”. One event swells in our minds so much that other priorities cease to matter.

This is where data comes in handy. They allow us to see how much this one event reflects what is really going on in the organisation. Maybe that disgruntled customer is speaking for others, or maybe he or she was just having a particularly bad day. Without such verification, you will be putting out fires over and over again where they shout the loudest, instead of where they really occur.

Mistake three – you chase “pretty” indicators

You’re sitting politely in a meeting and you hear that “we’re growing on LinkedIn”, “we have great reach” and “the number of leads is exploding”. People nod their heads with satisfaction, until suddenly someone, with that sceptical look of theirs, asks a very uncomfortable question – “And how many of those leads have turned into paying customers, because the financial figures aren’t so optimistic after all?”.

Vanity metrics are precisely all those numbers that grow easily, look nice on charts and give a pleasant feeling of progress. They can be compared to a mirror in which you look at yourself pleasantly – it shows you how handsome you are or how pretty you are, but it doesn’t actually change anything about you. It doesn’t make you feel healthier, smarter or have more money. It just makes you feel good about yourself.

The problem with vanity metrics is that they start to replace a true measure of progress for us. Instead of asking: “are customers staying with us longer?”, “are they coming back?”, “are they recommending us further?”, we focus on what is easy to check in the here and now: “how many people clicked a post?”, “how many people opened an email?”, “how many visits were made to the website?”.

These numbers are interesting, sometimes important, but on their own they say little about whether a company is really moving forward. Therefore, more important than the question: “what else can we count?”, is “which indicators are really linked to the goal we are aiming for?”

Mistake four – you have been analysing for so long that others have already acted

Another equally common mistake leaders make is that they will not move a finger until they feel they have the “full picture”. Every risk must first be carefully described and catalogued. Every decision is preceded by another iteration of the report, and each step must be backed up by yet another analysis, an additional meeting and another round of consultation. Over time, more energy goes into preparing materials for decisions than into the action itself.

I know this type and sometimes (or perhaps more often than I want to admit) I am one myself, because my analytical mind likes to have everything laid out before I take action. Except that “complete data” doesn’t exist. It’s simply an illusion. You will always be missing something. Sometimes a fact, other times a story, sometimes a bit of context, and other times a bit of data from the future that no one can predict anyway. By waiting for the perfect picture, you are handing over the initiative to people who will make a move faster, often on the basis of good enough data rather than perfect data.

That’s why it’s important to realise that data is there to help you live with uncertainty, not to eliminate it. Because if you try to bring everything to zero risk, you become a prisoner of your own reports, while the world runs on and doesn’t wait for you to finish analysing.

Mistake five – the data in the presentation does not change the day-to-day work

The fifth mistake looks innocuous. It lies in the fact that the data lives in one world and the daily work lives in another. Like two neighbours who pass each other on the staircase, politely nod at each other, but never really talk to each other.

Perhaps you have business intelligence systems, dashboards and advanced reporting in your company. Maybe you also have meetings where someone shows charts for half an hour while waving a pointer across the screen. Everyone then nods and then goes back to their tasks and… everything looks exactly the same as it did before the presentation. As if this whole “data layer” is a separate world that doesn’t touch their real work.

If this is the case, it usually means that the data is not woven into the organisation’s processes, just tacked on the side and that there is no clear mechanism that says “if indicator X does this, then we do Y”. Maybe there are no simple rituals or short reviews where the team looks at the numbers together and asks the question “what does this mean for our work this week or month”. Nor are there decisions closed with the question “how do we check in a month’s time if this worked”.

With this approach, data becomes just a backdrop and decoration that we show at quarterly briefings to look professional. But then, sooner or later, someone braver will ask the uncomfortable but very pertinent question – “why are we measuring all this at all if nothing comes out of it anyway?”.

Mistake six – you treat every decision the same way

Before you start applying the same scheme to everything, it is worth realising that different decisions need different data and different levels of accuracy. A different approach is required for decisions about people, another for operational, strategic or crisis decisions.

• Decisions about people – promotions, redundancies and team changes are based primarily on data about behaviour, performance and context. Numbers alone will show you what’s going on, but it’s only through conversations and observations that you reveal why things are the way they are. You won’t make a good decision about a person by just looking at a turnover chart or a KPI table.
• Operational decisions – when you want to change a process, buy a new tool or reorganise a workflow are mainly based on data about efficiency, time, costs and errors. Here, the numbers speak volumes, and the human context is still important, but more as a supplement: what people are bothered by, where work is realistically jammed, rather than how they feel about it “in general”.
• Strategic decisions – entering a new market, changing a business model or making a big investment will usually involve more uncertainty. You need both historical data and forecasts, but you will never have the “full picture” because some of the information is about a future that no one knows. Here, data helps to narrow the field of risk, but it won’t take you off the hook for choosing a direction.
• Crisis decisions – when something just falls apart and you need to act quickly require a minimum of data, but the most important data. There is no time for quarterly analysis. You need to know what’s happening now, the scale of the problem and what your viable options are for the coming hours or days. You stop the bleeding first, only then do a full study.

Recognising what type of decision you are dealing with allows you to better select your data set and the amount of time you can spend collecting it right away. After all, a decision to promote a director should not be treated in the same way as a decision to change office supplies.

7 habits of a leader who makes better decisions with data

Now that you know where data can get us into traps, it’s time to move on to what you can do differently. And importantly – without overhauling your entire IT architecture, without gigantic budgets for new systems and without hiring an army of data scientists.

I don’t have any list of “magic techniques”, but I would like to show you some daily habits or perhaps more routines that will give you a different way of looking at the reality around you. They may seem inconspicuous to you at first, because not much changes in the first week. However, after working in this way for a while, chances are you will change how you make decisions.

Habit one – ask the question first, then look at the report

Before you ask anyone for reports or the latest data, stop for a moment and specify exactly what problem you want to solve.

The phrase “our sales are falling” is just a general concern, but is not suitable as a starting point for further action. Much more is contributed by the statement – “in the last three months we have seen the number of new enquiries from customers in industry X drop by about a third, particularly in region Y, and I want to understand what has happened there”.

A question posed in this way automatically narrows the search field. Instead of drowning in dozens of spreadsheets, you start looking for a few specific pieces of information – the number of customer enquiries, the average basket value, the length of the sales process or turnover in the sales team. The rest ceases to be important at this stage and you can quietly put it aside.

It seems simple in theory, but in practice it is definitely more difficult. Because before you can say “show me the figures”, you need to clarify in your head (or, better still, in a document) one, at most two questions that you really want to find the answer to. Anything else will unnecessarily distract you.

Habit two – change what you can influence

Most of the stress of leaders, in the data area, comes from staring at end indicators over which they no longer have any influence at any given time. Revenue, profit, cash flow – these are just the consequences of hundreds of decisions that were made much earlier.

So when you see a drop in revenue on a graph, you are actually looking at the past. At the effects, not the causes.

A leader who thinks with data wonders “what signals appear much earlier and foreshadow a problem or an opportunity?”. If revenue has started to decline, there were usually already early warning signs in the past. For example, there were fewer new enquiries from month to month, contracts were renewed less frequently and clients were increasingly putting off further projects “for later”. At the same time, sales close times were lengthening, the proportion of returning clients was declining and turnover was quietly starting to increase in one team.

It is these signals that are the inputs. You can influence them here and now. When you change them, over time the results start to change too. However, this requires you to shift your focus from the very end of the process to the beginning, i.e. from effects to causes. But you will get something very valuable in return – a much greater sense that you actually have an impact on something.

Habit three – link numbers to conversations.

When you only analyse the numbers, these will show you what is happening, but they won’t tell you why. Conversely, when you focus only on conversations, stories and people’s stories they will give you some context, but won’t tell you whether it’s an individual case or something wider.

You could say that the figures themselves are like a picture without the sound, and the stories themselves, like sound without the picture. They seemingly show something, they tell something, but the other half is still missing. So, to work properly, you need both at the same time.

If turnover has increased in one department of your organisation, the chart will tell you “by how much” and “when”. However, it will not answer the question “why”. Because the “why” is hidden in conversations, in interviews with departing employees, in one-on-one meetings, or in innocuous sentences thrown between emails, or over coffee or in the corridor. That’s where the context is that no dashboard can show you.

On the other hand, the mere story of a “dire atmosphere” is also sometimes misleading. You need to check whether it’s the perspective of one very loud person, or something that’s repeated in many stories and actually reflected in the numbers. It’s only when you put these two layers together – the numbers and the conversations – that you start to see a fuller picture and stop looking at the company as a set of graphs or individual stories and start seeing it as a living organism.

Habit four – recognise your cognitive distortions

It seems that our brains were not designed to analyse data impartially. Looking at the historical background, most of the time their main task was to help us survive. Therefore, in the main, they react quickly, sometimes even too quickly. To speed things up, they use mental shortcuts or invoke our instincts. These mechanisms undoubtedly used to help us recognise danger, but today they can give us a distorted picture of the situation.

If you tend to see mainly threats, you will conclude from any graph that things are bad and will get worse. And if you are a born optimist, you will probably call the downward trend a temporary fluctuation that will “turn around soon”.

Daniel Kahneman described this as the operation of two systems in our mind. System One is quick, intuitive and automatic. It is responsible for most everyday decisions and works without much effort. System Two is slow, analytical and requires concentration. It should control System One, but is “lazy” and often remains passive. Strategic business decisions require System Two to be consciously engaged, while we as leaders often unconsciously rely on System One’s intuition. This is where all those moments come from when, after the fact, we think to ourselves “how could I have missed this”. The answer is simple. Your fast system made a decision before the slow one had time to kick in.

You can’t completely escape from these filters. You can, however, learn to spot them and take the corrections for how they affect your conclusions.

A leader who thinks with data does not pretend to be free of his default styles of thinking or acting. He or she knows his or her patterns and whether he or she is more likely to turn to “black scenarios” or more likely to turn to “somehow it will happen”, and consciously builds some simple safeguards around them. This could be, for example, inviting someone with a different mindset into the conversation to look at the same figures he is currently analysing. He compares two independent sources of information. He looks at a longer time frame instead of drawing conclusions from one week. This approach is a sign of responsibility for how he makes decisions.

Habit five – test on a small scale rather than immediately making a revolution

The easiest way to get used to data is through small-scale experiments, i.e. specific activities where you know in advance what you want to test.

Instead of doing a company-wide reorganisation, you start with one department. Instead of changing the price list for all customers overnight, you test the new model on a select few. And instead of imposing new working rules on an entire department, you try them out on one team first and watch what happens for a few weeks.

After such an experiment you can honestly say “we had a hypothesis, we tested it, let’s now see what came out of it”. Without data, any change is a bit of a guessing game – you don’t know if it worked or if you just got lucky. When accompanied by numbers, even a small trial becomes a lesson for the future.

Habit six – document decisions, not just dry facts

In most organisations, reports are meticulously archived. You’ll find gigabytes of presentations, sheets, analyses, KPIs and God knows what else. Far less often are the decisions themselves archived – and it is in these that the most interesting knowledge is hidden.

Because if you write them down and, in doing so, identify why and how they were made, then months or years later you can look at the record and know:

• what the context was at the time,
• what numbers you had on the table,
• how you interpreted them, what you considered important and what you deliberately ignored,
• who was for and who was against,
• what arguments were made.

It does not have to be a complicated system. A short note after an important decision – even five sentences – is enough. After a few months, you already have material you can go back to and see where your thinking was accurate and where it completely missed the mark. After all, leadership is not about being the smartest, infallible and always being right. However, it is important that there is at least one tangible lesson left from each of your mistakes for the future you.

Seventh habit – revisit decisions after time and check that they made sense

Once you have created a decision diary it is a good idea to return to it regularly. Once in a while (maybe once a quarter, twice a year) you pick out some of the most important decisions from the last period. You open the old material and look at what data you had at the time, what assumptions you made and what you considered important. And then you calmly juxtapose that with what actually happened.

Most leaders are very quick to skip over this stage. Because after all, once the project is over and the client has paid, you can run on. Because after all, once the project is over and the client has paid, you can run on. Few people have the courage (and sometimes the time) to open up old notes three, six or twelve months later and honestly confront the decisions at the time with what really happened. It is also uncomfortable to some extent, as it is sometimes painful to find out that we were simply wrong. Therefore, it is easier to unravel even more than to stand up and admit it to ourselves or the team.

However, once you sit down for such a review, ask yourself a few questions:

• If I had to make the same decision today, with the same data, would I do it the same way?
• What data did I miss then, what did I have to add up in my head?
• Was the data I relied on reliable – was it isolated anecdotes or a trend over time?

If you feel up to it, do this kind of review not alone, but with one trusted person from the team. Someone who isn’t afraid to say “here I think you were looking for a bit of data for a thesis” or “this decision was mainly based on the fact that you really wanted it to work out”. It can be uncomfortable, but nothing teaches like a calm, honest conversation about how we think as leaders.

It’s in moments like this that you can see what the data is for in the first place. Not just as decision support, but as a tool to learn from decisions previously made.

Additionally, if you teach the team that this is not a blame hunt, but a collaborative examination of how we think and what assumptions have gone wrong – a real change in organisational culture will begin. People will stop being afraid to admit mistakes, and without this approach, no company can grow properly and healthily.

Where do you start if you want to make better decisions based on facts?

You can read this text, nod your head and go back to your business. Put it back on the mental shelf with “interesting, I’ll get around to it one day”. I have such a shelf myself and there are more thoughts, unfinished business, books and articles on it than I’d like to admit.

But you can also start with something simple.

Take a piece of paper and ask yourself one question – what three decisions in my role today do I base most on gut feeling and least on data? These could be situations where you promote someone because you “feel they are ready”, or you consider entering a new market because you “think” there is potential there, or you consider which clients to let go because you “feel” that some are more valuable than others.

With each of these decisions, briefly add what you actually base it on today. Are they individual stories from the past? The opinions of a few team members? Your own observations, a few figures taken out of context? Or simply “because we’ve always done it that way”?

And then ask a second question – “what two, maximum three pieces of information would make you make each of these decisions more calmly and with greater awareness?”. I don’t mean measuring everything. I mean the minimum that would make you go from “I think” to “I know where I stand”.

What will change if you take it seriously?

You won’t suddenly become a perfectly data-driven leader. That’s not how it works. But if you go through these habits honestly, without cheating yourself, a few things will change.

1. You’ll see your blind spots and you’ll understand in which types of decisions you drift most towards “it seems to me”, where your intuition most often prevails over facts.
2. You will also see which data in your business are really useful and which are just decoration. Suddenly, you’ll find that a few indicators nobody needs and that the monthly reports contain colourful graphs that nobody cares about. For that, you may discover that two simple numbers are missing that can change the way you talk and plan activities with your team.
3. Additionally, you will start to build a culture where data is a tool for conversation, not a “baton to beat”. Because if you as a leader are willing to show your thinking, admit uncertainty and mistakes, and review your own decisions after time – then the people around you will also be more willing to talk about facts, not just opinions.

Data protects people and helps leaders make more honest decisions

As we slowly come to the end, I would like to say that an additional benefit of data is that you can use it to better protect people from burnout. For example, by catching the early signs of overload and noticing when someone is swamped with tasks beyond their capacity.

You can then reorganise work (before someone gets hurt), divide workloads more fairly (because you will see the facts and not just loud complaints), and make decisions based less on biases, likes and dislikes (and more on what people are actually doing and the results of their work).

You can also use the same data to squeeze more out of people on the same salary and penalise every deviation from the norm, and every minute spent not where it should be spent. You can also stick labels such as “lazy”, “problematic”, “unengaged”, based on numbers taken out of context and build a system where everyone is measured every second and no one is listened to.

The technology in both cases will be almost the same. Dashboards look similar. Reports have the same charts. The difference, however, sits in your intention and what you are reaching for in these tools.

That’s why data-driven leadership doesn’t start with technology, but with asking yourself – “why do I really want this data?”.

If the smouldering answer in your head is – “to have more control over people” – then know that this is not leadership. This is an archaic model of management that is long overdue, but in the meantime is disguised in modern tools and camouflages itself brilliantly.

If the answer is – “to make fairer, more informed decisions, including to the people who rely on me” – then you are on the right track.

Data is one of the best tools for making smarter decisions about where you want to lead yourself, your teams and your business as a whole.

Data helps you see which things are really pulling the business forward and which things are only living on the strength of old belief. They allow you to count whether a new sales model makes sense or just looks good in your head. They also allow you to see if your investment in people development is paying off not only in motivational stories, but also in turnover, customer retention and greater quality of work. And finally, data helps you to separate temporary jerks from sustainable trends so that you don’t make nerve-wracking turnarounds every time you have a worse month.

There is something else I see after years of working with data in different contexts – it is one of the most effective human defence tools.

When something goes wrong in a company, such as sales falling, the natural reflex, in some people, is to look for someone to blame, but if you have data, you can show that the problem started much earlier – perhaps with a price list change that scared away customers or a marketing budget cut that halved enquiries. Then you have it in black and white that it wasn’t “Smith” who screwed up, but simply got half as many opportunities as a year ago.

Or, when a client leaves, someone may blame the account manager for a “bad relationship”. Meanwhile, the data may show that this customer has been reporting the same problems for eight months, that he has escalated the issue three times and that no one but the customer service man has responded.

Without data, all you are left with is a story. And the story of the weaker loses out to the story of the stronger.

Used well, data can reverse this logic. They bring to light what is important to people. They give concrete arguments to those who would otherwise only have hunches and frustrations. They turn “it seems wrong to me” into “here’s the evidence that it’s wrong – and here’s what we can do about it”.

Finally, I want to leave you with one more thought. In a world where everything can be measured, the easiest thing to start measuring is the easiest thing to measure – and I feel that exactly this need was behind the proposal for the Internet of Behaviour lecture I wrote about at the very beginning.

However, if you make it clear as a leader that you will not judge people purely on activity, but on the value of what they contribute, you are sending a clear message that it is the person who comes first and that data is there to serve them and to help everyone make decisions that you will not have to be ashamed of, either to yourself or to others.