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

It is with great pleasure that I welcome you to another episode of my podcast, Leadership in the Age of AI. Today I will highlight five common mistakes that even seasoned leaders make when interpreting data. I will also discuss seven habits that will help you make smarter decisions and avoid treating people like cells in a spreadsheet. Thank you for tuning in and I cordially invite you to listen. A few weeks ago, I received a message from a consulting firm. They had read my previous publication on the Internet of Behaviour and thought that I would make a good speaker at their upcoming conference. I was delighted and agreed to discuss the details. However, the more we talked, the more uneasy I became about this lecture. Eventually I realized that my interlocutors wanted me to give a presentation on using the Internet of Behaviour to extract more data about employees in the workplace, which they could then use to make decisions. It wasn't about improving teamwork or people's well-being. It was only about tracking movement breaks and every second of inactivity in order to calculate the cost of a one-minute coffee break, a toilet trip, or staring out of the window more accurately. After learning the details I refused without hesitation. My attitude towards the Internet of Behaviour has remained largely skeptical, if not critical for years. This is not because the tool or the data itself is bad, it's because too often this technology begins to serve the worst purposes instead of good ones. Just look at how authoritarian states use it. Algorithms, awards, citizens, points for proper behaviour and take them away for improper behaviour. I understand the need for leadership and data-driven decision-making. I write and practice it 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 the wise use of facts. On the other hand, however, they create systems that squeeze every drop of productivity out of people without consulting them. This is a dangerous combination. The gap between companies that can use data to make meaningful decisions and those that are still stuck in the chaos of scattered spreadsheets and managers' hunches is growing faster than anyone expected. Gartner has shown that as many as 87% of organisations globally are stuck at the lowest levels of data maturity. The Boston Consulting Group has revealed that the proportion of organisations with highly mature data capabilities has fallen from 13% in 2021 to just 8% in 2024. In practice, this means that a small group of companies that can work with data are pulling further and further ahead of the rest. They are reaping an ever-increasing share of margin, growth and competitive advantage. As the gap between companies widens, the pressure to deliver results increases. The greater the pressure, the easier it is for someone to suggest monitoring people more closely instead of using data more wisely. That's why I sat down one evening and started thinking about how to encourage leaders to make data-driven decisions. The aim was to do so in a way that protects people's dignity. After all, a good leader can combine facts, context and people's emotions to make coherent decisions. They can also explain the basis of their decisions. Firstly, I want to distinguish between two concepts that many leaders confuse, despite them representing completely different management philosophies. On the one hand, there is data-driven leadership. This approach involves a leader relying on facts rather than the mood of the previous week. They are aware of their biases and cognitive distortions. They combine hard data with conversations, observations in corridors and conference rooms and an awareness of the team's atmosphere. They can justify their decisions. They are also not afraid to review that decision later. In contrast, management by control does not necessarily involve cameras above the desk or tracking mouse movements. Often it is much more subtle. The manager introduces so many performance indicators and requires so much detail to be reported that employees spend more time proving they are working than actually working. Every step requires justification and every decision must be approved in advance. This way, people stop thinking for themselves because it is faster and safer to simply do exactly what is expected of them. The data that was supposed to a decision-making becomes a leash that keeps the team in check. I want to make this clear. This is not leadership. It is simply the old well-known philosophy of I don't trust you, so I have to have everything under complete control. Research shows that this kind of atmosphere is harmful to an organization. The American Psychological Association points out that, of employees who are electronically monitored, 56% say they feel tense or stressed at work. A study by the UK's Chartered Management Institute showed that around a third of companies have started using bossware to track employee activity. Some managers openly admit that such practices undermine trust and cause people to leave. In other words, the more you try to control everything, the greater the risk of losing not only productivity, but also people. The problem is that these two approaches look very similar at the level of ideas. In both cases, a manager may say, "We want to be more data-driven." However, this one sentence can conceal completely different intentions. For some, it's about making smarter decisions that take people into account. For others, it's about using smarter tools to squeeze more work out of people for the same salary. This is why the first fundamental question that every leader should ask themselves is relatively simple. Am I using data to better understand people and make smarter decisions? Or am I using data to exert more power over them? If you can answer this question honestly, the rest is just a matter of tools, processes, and habits. Without this level of self-honesty, however, even the best data and charts in the world won't help you become a better leader. At best, they will help you become a more efficient supervisor. Personally, I like data for one simple reason. It organizes my view of the world. It enables me to see reality as it is with all its messiness, tangled patterns, and unexpected dependencies. It enables me to extract the signal from the noise and reveal the direction of change where the naked eye sees only chaos. However, raw data does not tell me who to hire, whom to give another chance to, or what to do with a tired, scared, or burnt out team. It does not tell me how to inform someone that their role in the company is coming to an end. Nor does it tell me how to welcome someone new so that they really feel part of the team. That's still up to me. I am a person with a wealth of experience, intuition, values, and responsibility for other people. You can think of 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 the surroundings look like. It shows you where the hills, obstacles, and shortcuts are. The compass allows you to orientate yourself in different directions. However, it is you who decides which way to go and at what pace. You must take into account the condition of the team, the weather, the risks, the available resources, and the goal you are aiming for. However, if you start using data to judge people rather than to understand what is happening, any deviation from the plan begins to look like someone's personal failure. When you only look at the numbers on the screen, it is very easy to think they're not trying hard enough. When you add automatic reports, algorithms, and artificial intelligence to the mix, it's easy to gradually push the boundaries. You try to increase every result by a few percent and raise every bar on the chart a little higher. At some point, the person in your Excel file ceases to be a specific individual with a history, fatigue, talents, and limitations. They simply become another cell in the table. From what I've seen, companies that are enthusiastic about using data for surveillance often have poor data organization. They don't consider the real cost of poor decision making, yet they are willing to invest in solutions that give them a false sense of control. They would rather have those solutions than ones that provide real value and a better understanding of their environment. If you regularly read my content, you will probably know that I have a strong analytical side. I like numbers, organizing reality into clear categories and seeing facts laid out clearly side by side, showing what follows from what. However, the humanistic side of my personality makes me aware that some decisions cannot be made in a spreadsheet. Sometimes you have to trust your instincts which sit somewhere between your stomach and your heart. Intuition is often condensed experience that your brain tries to communicate to you in the form of a hunch. It is the sum of the many similar situations you have experienced, the patterns you have recognized, and the mistakes you have made. Intuition is the wisdom of the body and the subconscious, and it is sometimes worth listening to. The problem arises when you fetishize intuition and tell yourself, "I just feel the market," or when you blindly trust the data, assuming that if the report says so, there's nothing more to discuss. Both of these extreme approaches are traps. The first leads to arrogance and a refusal to acknowledge the facts. The second leads to paralysis and cutting yourself off from the human dimension of decision making. Interestingly, most companies still operate within these two extremes. A bark study found that 58% of organizations admitted that at least half of their current business decisions were based more on gut feeling and experience than hard data. It is worth mentioning Gary Klein, a pioneer of naturalistic decision making who has studied how firefighters, pilots, and doctors make decisions under pressure. His groundbreaking 1985 study involved 26 experienced firefighters and 156 decision points. He found that in over 80% of cases the firefighters did not compare the available options. Rather, they recognized the situation as typical and knew immediately what to do. There was evidence of simultaneous comparison of two or more alternatives in less than 12% of decision points. This means that firefighters in burning buildings do not analyze options, but react based on patterns built up over years of practice. In 2009, Kahneman and Klein, representing opposing schools of thought, jointly published an article in American psychologist in which they identified the conditions under which intuition can be trusted. Firstly, the environment must have a certain structure and predictability to allow intuition to develop. If the situation is very unstable, there is no basis for accurate hunches. Secondly, decision makers must be able to learn through feedback. because regular one on the outcomes of their decisions enables them to develop and refine their expertise. In the same interview, Kahneman added an important caveat. He would only trust the intuition of experts when dealing with situations they had encountered many times before. Surgeons, for example, perform many operations of a given type and learn what problems they will encounter. However, when the problems are unique, he is less trusting of intuition. From my own experience, I have found that something helps me navigate between intuition and reliance on data. It's critical thinking, which is the ability to distinguish facts from opinions, accuracy from inaccuracy, and signal from noise. It involves looking beyond the obvious sources of information and questioning the assumptions we take for granted. It also involves breaking down complex problems into smaller parts and looking for connections that are not immediately apparent. Equally important is the awareness of what we do not use when making decisions. 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 making any important decision, ask yourself a question. What exactly am I basing my belief on? If the answer is based mainly on recent situations that have stuck in your memory, one or two anecdotes or a single conversation that made an impression on you, then these are not facts. They are just memories, fragments of other people's stories and your own emotions, all of which are disguised as hard facts. The brain loves to play such tricks. From one dramatic event, it can construct a whole narrative about how the world always works. Then it stubbornly looks for confirmation of this story, ignoring everything that contradicts it. Conversely, if you have a beautiful report with colorful charts on your desk, but you cannot answer questions about the data it contains, the period it covers, who collected it, and most importantly, the intention behind its collection, then it is not data either. Many reports are created for a very specific purpose to support a thesis, strategy or decision that someone wanted to make anyway. In that case, what you see is not a neutral picture of reality, but a nicely packaged interpretation that can be as misleading as a hunch. Today, I want to show you a few specific ways of thinking about data that will help you to better understand reality without turning people into numbers. They will also allow you to stand calmly in front of your team and say, "This is the decision I have made. Here are the facts we based it on. Here's what we expect. Here's how we'll check in a month to see if we were right." It's easy to say, "Rely on data. It's harder to recognize when we get it wrong." Our presentation contains charts, reports, and numbers. Yet the decisions we make have little to do with what the data really shows. Bain and company consultants analyzed hundreds of companies from various industries and showed that the effectiveness of decision-making and enforcement is approximately 95% correlated with an organization's financial performance, whether we look at revenue growth, return on capital, or total shareholder return. Therefore, if your decision-making process is chaotic, it will sooner or later affect the entire organization. Therefore, before we move on, I want to present six mistakes that even the most experienced leaders make. Mistake number one. Looking for data to confirm a decision you have already made. This scenario is quite common. A decision simply pops into our heads. For example, we might have a vision that our organization is launching a new product, exiting a particular market, or changing the structure of the team. After making the decision, we realized that it would be useful to back it up with something. This is so that people don't accuse us of acting on our feelings alone. Then the hunt begins. Someone on the team is tasked with doing research, while another is asked to pull up reports from the last quarter. Then, from all the data available, we select the information that confirms our narrative. Everything that doesn't suit us is labeled "unrepresentative", "outdated" or "random", but this is not how you work with data. It's about looking for justification for a decision that has already been made. Real data analysis starts somewhere else entirely. It begins when we are prepared to accept that we were wrong. We don't shoot the messenger. Look for holes in the methodology or say, but the context was different. We just sit with that discomfort and allow it to change our thinking. Mistake number two. Confusing data with stories. This mistake is more insidious because we often don't even notice we're making it. It involves treating individual situations that have stuck in our memory, one or two anecdotes, a single conversation that made an impression on us, as if they were hard data. Examples include one customer who caused a scene at a meeting, one person on the team who wrote an emotional message about burnout, or a single project that failed spectacularly just before the deadline. Of course, all these stories are important. Sometimes they are the first warning sign that we should not ignore. The problem arises when we treat them as the only explanation of reality. Our brains then like to fill in the rest. Suddenly, all customers are complaining, all employees are frustrated, and all projects are late. One event takes on such significance in our minds that other priorities cease to matter. This is where data can be useful. It enables us to assess how accurately this one event reflects what is really going on in the organization. Perhaps this dissatisfied customer is representative of others, or maybe they just had a particularly bad day. Without verification of this kind, you will keep putting out fires where they are most vocal instead of where they really occur. Mistake number three. Falling in love with vanity metrics. You're sitting in a meeting and you hear we're growing on LinkedIn. We have great reach. The number of leads is exploding. People nod their heads in satisfaction. Suddenly someone with a skeptical look on their face asks an uncomfortable question. And how many of those leads have turned into paying customers? Because the financial data is not so optimistic. Vanity metrics are all those numbers that grow easily. Look good on charts and create the pleasant illusion of progress. They can be compared to a mirror in which you enjoy looking at your reflection. They show you how handsome or pretty you are, but they don't really change anything about you. It doesn't make you healthier, smarter, or richer. It just makes you feel good. The problem with vanity metrics is that they begin to replace true measures of progress. Instead of asking do customers stay with us longer? Do they come back? And do they recommend us to others? We focus on what is easy to check right now. How many people clicked on the post? How many people opened the email? How many visits were there to the website? These numbers are interesting and sometimes important. But on their own they don't tell us much about whether the company is really moving forward. That's why asking which metrics are really related to our goal? Is more important than asking what else can we count? Mistake number four, analytical paralysis or waiting for ever for complete data. Another common mistake leaders make is waiting until they feel they have a complete picture of the situation before taking action. This means that every risk must be thoroughly described and cataloged first. Every decision must be preceded by another iteration of the report, and every step must be secured with yet another analysis, meeting or consultation. Over time, more energy goes into preparing materials for decisions than into the action itself. I recognize this tendency in myself as I am sometimes, perhaps more often than I would like to admit, guilty of it. My analytical mind likes to have everything sorted out before I take action, except that complete data does not exist. It's simply an illusion. There will always be something missing. Sometimes it's a fact, at other times it's a story or a bit of context, and at other times it's data from the future, which no one can predict anyway. By waiting for a full picture of the situation, you give the initiative to people who will act faster. They will often base their decisions on data that is good enough, not perfect. This is why it is important to realize that data is meant to help you live with uncertainty, not eliminated. If you try to eliminate all risks, you become a prisoner of your own reports while the world keeps moving and does not wait for you to finish your analysis. Mistake #5 - Separating data from everyday work This mistake is the most insidious because it appears innocent. It involves data living in one world and daily work in another. It's like two neighbors who pass each other in the stairwell nodding politely but never really talking to each other. Perhaps your company has business intelligence systems, dashboards and advanced reporting. Maybe you also have meetings where someone spends half an hour showing charts and pointing at the screen. Everyone nods in agreement, then they return to their tasks and everything looks exactly as it did before the presentation. It's as if this whole data layer were a separate world that does not affect their real work. If this is the case, it usually means that the data is not integrated into the organization's processes but merely added on top. There is no clear mechanism that says, "If indicator x does this, then we do that." Perhaps there are no simple rituals or short reviews where the team looks at the numbers together and asks, "What does this mean for our work this week or month?" Nor are there any decisions made based on the question, "How will we check in a month's time if it worked?" With this approach, data merely becomes a backdrop and decoration that we show at quarterly meetings to appear professional. Sooner or later, however, someone will ask an uncomfortable but pertinent question, "Why are we measuring all this if nothing comes of it anyway?" Mistake 6. You treat every decision the same. Before applying the same pattern to everything, realize that different decisions require different data and levels of accuracy. Decisions about people require a different approach to operational, strategic or crisis decisions. Decisions about people, such as promotions, dismissals, and changes to the team, are primarily based on data about behavior, results, and context. Numbers will show you what is happening. But only conversations and observations will reveal why. You will not make an informed decision about a person by looking only at the turnover chart or KPI table. Operational decisions such as changing a process, buying a new tool or reorganizing your workflow are mainly based on data about efficiency, time, costs, and errors. Here the numbers speak volumes and the human context is important too, but more as a supplement showing what bothers people and where work really gets stuck rather than how they feel about it in general. Strategic decisions such as entering a new market, changing a business model or making a large investment, usually involve greater uncertainty. You need both historical data and forecasts, but you will never have the full picture because some of the information relates to the future, which is unknown. Data helps to narrow down the field of risk here, but does not relieve you of responsibility for choosing a direction. Crisis decisions, i.e. when something is falling apart and you need to act quickly, require the most important data, but a minimum of it. There is no time for quarterly analysis. You need to know what is happening now. The scale of the problem and the realistic options available to you in the coming hours or days. First, you stop the bleeding, and then you conduct a thorough examination. Recognizing the type of decision you are dealing with enables you to select the appropriate data and allocate the necessary time to collect it. After all, the decision to promote a director should not be treated in the same way as the decision to change office supply suppliers. Now that you know how data can mislead us, it's time to consider what you can do differently. Importantly, this can be achieved without rebuilding your entire IT architecture, without huge budgets for new systems and without hiring an army of data scientists. I don't have a list of magic techniques. However, I want to show you a few everyday habits. These are routines that will give you a different way of looking at the reality around you. At first, they may seem insignificant because not much will change in the first week. However, if you work this way for a longer period of time, there is a chance that you will change the way you make decisions. Habit 1. Start with a question, not a graph. Before asking anyone for reports or the latest data, take a moment to clarify exactly what problem you want to solve. A statement such as "Our sales are falling is just a general concern and is not a suitable starting point for further action." A much more useful statement would be "Over the last three months, the number of new inquiries from customers in Industry X has fallen by about a third, especially in the IGREC region, and I want to understand what happened there." This question automatically narrows down the search field. Rather than drowning in dozens of spreadsheets, you can focus on finding a few specific pieces of information. These could be the number of customer inquiries, the average basket value, the length of the sales process or the turnover in the sales team. The rest is no longer important at this stage, so you can safely put it aside. In theory it seems simple, but in practice it is definitely more difficult. Before you ask for the numbers, you need to clarify one or two questions that you really want to find answers to, either in your head or even better in a document. Everything else will distract you unnecessarily. Habit 2. Separate input data from output data. Most of the stress that leaders experience with regard to data comes from focusing on end indicators that they no longer have any influence over at a given moment. A leader who thinks in terms of data will ask, "What signals appear much earlier and indicate a potential problem or opportunity?" If revenue has started to decline, the first warning signs will have appeared earlier. For example, there may have been fewer new inquiries from month to month. Customers may have been renewed less frequently, or customers may have postponed subsequent projects until later. At the same time it was taking longer to close a sale, the proportion of returning customers was decreasing, and staff turnover was quietly beginning to rise in one of the teams. These signals are the input data. You can influence these factors right now. When you change them, the results will also begin to change over time, however this requires you to shift your attention from the end of the process to its beginning. In other words, from the effects to the causes. In return though, you will gain something very valuable. You will have a much greater sense that you are actually having an impact. Habit 3. You combine numbers with conversations. When you analyze only numbers, they will show you what is happening, but not why. Conversely, when you focus only on conversations, stories and people's experiences, they will provide context, but not indicate whether it is an isolated case or more widespread. You could say that numbers alone are like a picture without sound, and stories alone are like sound without a picture. They show something and tell a story, but the other half is missing. So to function properly, you need both at the same time. If turnover has increased in one of your organization's departments, the graph will show you by how much and when. However, it won't answer the question why, the why lies in conversations, interviews with departing employees, one-to-one meetings, and casual comments made in emails over coffee or in the corridor. It is in these contexts that the answers lie, and no dashboard will show you that. Conversely, the story of a terrible atmosphere can also be misleading. You need to check whether this is the perspective of one very vocal person, or whether it is reflected in many stories and the numbers. It is only when you combine these two layers, numbers and conversations, that you begin to see a more complete picture, moving away from viewing the company as a set of charts or individual stories, and starting to see it as a living organism. Habit 4. You consciously deal with your biases. Our brains are not designed to analyze data impartially. Throughout history, their main task has largely been to help us survive. This is why they react quickly, sometimes even too quickly. To speed up this process, they use mental shortcuts or invoke our instincts. While these mechanisms undoubtedly help us recognize danger in the past, today they can give us a distorted picture of the situation. If you tend to see threats everywhere, you will conclude from any graph that things are bad and will get worse. Conversely, if you are a born optimist, you will probably describe a downward trend as a temporary fluctuation that will soon reverse. Daniel Kahneman described this as the operation of two systems in our mind. System 1 is fast, intuitive and automatic. It is responsible for most of our everyday decisions and operates without much effort. System 2, on the other hand, is slow, analytical and requires concentration. Ideally, it would control system 1, but it is lazy, and often remains passive. Strategic business decisions require the conscious engagement of system 2, whereas we as leaders often rely unconsciously on the intuition of system 1. This explains those moments when, after the event, we think, "How could I not have noticed that?" the answer is simple. Your fast system made the decision before the slow one had a chance to kick in. It is impossible to completely escape these filters, however, you can learn to recognize them and adjust for how they influence your conclusions. A leader who thinks with data does not pretend to be free from their default styles of thinking or acting. They recognize their patterns and whether they tend to lean towards worst-case scenarios or it will be fine, they also consciously implement a few simple safeguards. For example, they might invite someone with a different way of thinking to look at the same numbers. They compare two independent sources of information and consider a longer time frame rather than drawing conclusions from a single week. This approach demonstrates responsibility and decision making. Habit 5, test on a small scale instead of making sweeping changes immediately. The easiest way to familiarize yourself with data is through small experiments. These are specific actions where you know in advance what you want to test. Rather than reorganizing the entire company, start with one department. Rather than changing the price list for all customers overnight, you test the new model on a few selected ones. Similarly, instead of imposing new working rules on the entire department, you could try them out with one team first and observe the results over a few weeks. After such an experiment, you can say with confidence, "We had a hypothesis. We tested it. Now let's see what came of it." Without data, every change is a bit of a guess. You won't know whether it worked, or if you just got lucky, when accompanied by numbers, even a small trial can teach you something for the future. Habit 6, you document decisions not just dry facts. Most organizations meticulously archive reports. You'll find gigabytes of presentations, spreadsheets, analyses, KPIS, and so on. It is much less common, however, to archive the decisions themselves, yet it is in these decisions that the most interesting knowledge lies. If you record them and specify why and how they were made, you can look at the record months or years later and see what the context was like at the time. You can see what numbers were on the table, how they were interpreted, what was considered important, what was consciously ignored, who was for and who was against, and what arguments were made. It doesn't have to be a complicated system. A short note after an important decision is enough, even five sentences. After a few months you will have material that you can refer back to to see where your thinking was accurate and where it was way off the mark. After all, leadership isn't about being infallible or always right, however, it's important that every mistake you make teaches you at least one specific lesson for the future. Habit 7, you review your decisions after some time has passed and calmly reflect on what happened. Once you have created a decision journal, it is a good idea to return to it regularly. Every so often, perhaps once a quarter or twice a year, you can find out more about the Select a few of the most important decisions from the recent period. Open your old materials and review the data you had at the time, the assumptions you made, and what you considered important. Then you can calmly compare this with what actually happened. Most leaders skip this stage very quickly. After all, once the project is over and the client has paid, you can move on. Another quarter comes along, bringing new opportunities to solve problems. Few people have the courage, or sometimes the time, to open old notes three, six, or twelve months later. And compare the decisions made at the time with what actually happened. It can also be uncomfortable because it can be painful when it turns out that we were simply wrong. This is why it is easier to speed up than to stop and admit it to yourself or your team. However, when you sit down to conduct this review, ask yourself a few questions. If I had to make the same decision today with the same data, would I do the same? What data was I missing at the time? What assumptions did I make? Was the data I relied on reliable? Were they isolated anecdotes or a trend over time? If you feel up to it, don't do this review alone, but with a trusted member of your team. Choose someone who isn't afraid to say, "I think you were looking for data to support your thesis here," or, "This decision was mainly based on the fact that you really wanted it to work out." This can be uncomfortable. However, nothing teaches us as much as a calm, honest conversation about how we think as leaders. It is in moments like these that data shows its true value. Not only are support for decision-making, but also as a tool for learning from previous decisions. If you teach your team that it's not about hunting for culprits, but jointly examining how we think and which assumptions didn't work out, a real change in organizational culture will begin. People will stop being afraid to admit their mistakes. Without this approach, no company can develop properly or healthily. I would also like to mention that on my website, you will find a downloadable decision log template. If after hearing all this, you are wondering what to do next. There are two options. You can nod your head and go back to your business. You can put it on a mental shelf labeled "interesting." I'll deal with it someday. I have such a shelf myself and it contains more thoughts on finished business, books, and articles than I would like to admit. However, you could also start with something simple. Take a piece of paper and ask yourself one question. Which three decisions in my role are my most likely to base on gut feeling rather than data? These may be situations where you promote someone because you feel they are ready. What perhaps you are considering entering a new market because you think there is potential there? You may also be wondering which customers to let go because you have the impression that some are more valuable than others. For each of these decisions, briefly note what you are actually basing it on. Are they based on individual stories from the past? The opinions of a few people on the team, your own observations, a few numbers taken out of context? Or is it simply because we've always done it this way? Then ask yourself a second question. What two or three pieces of information would give you more confidence and awareness when making these decisions? I'm not suggesting that you measure everything. I'm talking about doing the bare minimum to turn, I think, into "I know what I stand." How would things change if you took this seriously? You won't suddenly become a perfectly data-driven leader. It doesn't work that way. However, if you work through these habits honestly and don't cheat yourself, a few things will change. Firstly, you will see your blind spots and understand which types of decisions you most often make based on, I think, rather than facts. And where your intuition most often takes precedence. Secondly, you will also see which data in your company is truly useful and which is merely decorative. You may find that no one needs certain indicators and that the monthly reports contain colorful charts that no one cares about. Conversely, you may realize that two simple numbers are missing, which could transform the way you communicate and plan activities with your team. Thirdly, you will start to build a culture in which data is a tool for conversation, not a stick to beat people with. If you as a leader are willing to show how you think, admit your uncertainties and mistakes, and review your decisions, the people around you will also be more inclined to talk about facts rather than opinions. As I come to the end, I'd like to mention an additional advantage of data. You can use it to better protect people from burnout. For example, you can pick up on early signs of overload and notice when someone is overwhelmed with tasks. You can then reorganize the work before anyone gets hurt. You can distribute the workload more fairly because you will see the facts, not just loud complaints. Decisions can also be made based less on prejudice and personal preferences, and more on what people actually do and the results of their work. However, this is not the only way, as data can also have a darker side, you could end up using the same information to squeeze more out of people for the same salary. You can punish every deviation from the norm and every minute spent not where it should be spent. You could label people lazy, problematic, or uncommitted based on numbers taken out of context. You can create a system in which everyone is monitored down to the second and nobody has heard. In both cases, the technology will be almost the same. The dashboards will look similar. The reports contain the same charts. However, the difference lies in your intention. It's also about why you are reaching for these tools. This is why data-driven leadership does not start with technology. It starts with asking yourself a question, why do I really want this data? If your answer is to have more control over people, then know that this is not leadership. It's an outdated management model that should have been consigned to oblivion long ago. Instead, it's being dressed up in modern tools and camouflaged very well. However, if your answer is to make fairer, more informed decisions, including those affecting 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 to take yourself, your teams, and your entire company. It helps you identify the things that really drive your business forward and those that are based on outdated beliefs. It enables you to determine whether a new sales model is viable or merely appealing in theory. You can also use it to check whether investing in people pays off in more than just motivational stories. It can help you to improve turnover, customer retention, and the quality of work. Finally, data helps you distinguish temporary fluctuations from lasting trends, so you won't make hasty changes every time you have a bad month. After years of working with data in various contexts, I have also noticed that it is one of the most effective tools for defending people. When something goes wrong in a company, for example, when sales drop, some people's natural instinct is to look for someone to blame. However, if you have data, you can demonstrate that the problem originated much earlier. Perhaps it was a price change that scared customers away, or a cut to the marketing budget that halved the number of inquiries. Then you can prove in black and white that it wasn't Kowalski who messed up but simply had half as many opportunities as the previous year. Without data, all you have is a story. The story of the weaker party tends to lose out to the story of the stronger party. Well used data can reverse this logic. It highlights what is important to people. It provides concrete arguments for those who would otherwise only have hunches and frustrations. It transforms, I think it's bad, into, here's proof that it's bad, and here's what we can do about it. Finally, I would like to leave you with one more thought. In a world where everything can be measured, the tendency is to start by measuring the things that are easiest to measure. I have a feeling that this is exactly what was behind the proposal for a lecture on the internet of behaviour that I mentioned at the beginning. However, as a leader, if you clearly state that you will not judge people solely on their activity but on the value they bring, you are sending a clear signal that people are the most important thing, and that data is there to serve them, helping everyone to make decisions that you will not have to be ashamed of, neither in front of yourselves, nor in front of others. That's all for today. Thank you for listening to this episode to the end. If you found the topic interesting, please write to me or share your thoughts in the comments. If you haven't already, please subscribe to my channel, share your reflections in the comments and sign up for my newsletter. In it, I regularly share analyses and business cases to help leaders make better decisions. Subscribing to the newsletter will also give you access to my books, leadership in the age of AI, understanding cyber security, and the entrepreneur's mini-handbook.