[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"exercise-954":3},{"payload":4,"id":45,"user":46,"level":52,"course":53,"activity":54,"activity_slug":55,"title":6,"topic":56,"tone":57,"stats":58,"created":60,"score":61,"is_favorite":62,"public":63,"is_external":62},{"text":5,"title":6,"answers":7,"questions":38},"In many workplaces today, the phrase “data-driven” is used as if it automatically guarantees good decisions. Managers ask for dashboards, schools compare test results, and hospitals track waiting times. The basic idea is simple: if you measure what is happening, you can improve it. Yet the role of data in decision-making is more complicated than collecting numbers and following whatever they seem to say.\n\nTo begin with, data can reduce guesswork. A small online shop, for example, might feel that customers leave because delivery is slow. But when the owner checks the figures, she may discover that most complaints are actually about unclear product descriptions. In that case, rewriting a few pages could have more impact than changing the delivery company. In situations like this, data acts as a reality check, helping people focus on the problem that matters most.\n\nHowever, data is never completely neutral. What you measure depends on what you value, and what you value depends on your goals. A call centre that rewards staff for short calls may produce impressive average times, but it might also create frustrated customers whose problems are not solved. The numbers look positive, yet the experience gets worse. This is why experienced decision-makers often ask not only “What do the results show?” but also “What might these results be hiding?”\n\nAnother challenge is that data can be accurate and still misleading. A company might notice that sales rise whenever it runs online adverts and conclude that advertising causes the increase. But perhaps sales also rise at the start of each month, when people get paid, and the adverts simply happen to run at the same time. Without careful analysis, it is easy to confuse coincidence with cause. Good decisions usually require context: knowledge of timing, customer behaviour, and other factors that are not always visible in a spreadsheet.\n\nThere is also the human side of decision-making. People do not always trust data, especially when it contradicts their experience. A nurse who has worked in the same ward for twenty years may feel that a new scheduling system is unsafe, even if the hospital’s statistics suggest it reduces overtime. In such cases, leaders need to combine evidence with communication. If they treat data as a weapon to silence concerns, they may win an argument but lose cooperation.\n\nFor these reasons, the most effective organisations treat data as a tool rather than a judge. They use it to ask better questions, test ideas, and learn from mistakes. They also accept that some decisions involve values that cannot be fully measured, such as fairness, privacy, or long-term trust. Data can guide choices, but it cannot replace responsibility. In the end, the best decisions come from a balance: solid evidence, clear goals, and the willingness to think critically about what the numbers really mean.","Decisions in the Age of Data",{"1":8,"2":13,"3":18,"4":23,"5":28,"6":33},[9,10,11,12],"Using data doesn’t automatically mean decisions will be better.","Data-driven organisations never make mistakes.","Data-driven decisions always require expensive technology.","The main purpose of data is to create dashboards for managers.",[14,15,16,17],"It proves that delivery speed is the only thing customers care about.","It helps identify the real cause of a problem instead of relying on assumptions.","It guarantees that rewriting product pages will increase profits immediately.","It allows the owner to avoid reading customer complaints.",[19,20,21,22],"Short calls always lead to happier customers.","Customers prefer automated systems to human staff.","Focusing on one metric can improve the number while harming the real outcome.","Call centres should avoid measuring performance because it is unfair.",[24,25,26,27],"Sales may rise for another reason that happens at the same time as the adverts.","Online adverts are always ineffective in the long term.","Advertising only works at the start of the month.","The company should stop collecting sales data.",[29,30,31,32],"Avoid using data in areas where people have strong opinions.","Use data to prove people are wrong and end the discussion quickly.","Ignore experienced staff because statistics are always more reliable.","Combine evidence with explanation so concerns are addressed rather than dismissed.",[34,35,36,37],"Data is useful for guiding and testing decisions, but it cannot replace judgement and values.","Data should be treated as the final authority in every decision.","Only experts can use data, so most organisations should not try.","Data is mostly misleading, so decisions should rely on intuition instead.",{"1":39,"2":40,"3":41,"4":42,"5":43,"6":44},"What point does the writer make about the term “data-driven” in the first paragraph?","In the example of the online shop, what is the main benefit of using data?","What problem is illustrated by the call centre example?","Why might the company be wrong to assume advertising caused higher sales?","What does the writer suggest leaders should do when staff distrust data?","Which statement best summarises the writer’s overall view of data in decision-making?",954,{"id":47,"username":48,"first_name":49,"last_name":50,"image":51},23948,"harley-davidson","Harley","Davidson","https://lh3.googleusercontent.com/a/ACg8ocJD0KETXvAHpaISIfOtHmvNQSo2JhJOkmYOleW8KnChRvrtStjD=s96-c","B2","Reading","Long Text","long-text","Create an exercise about the role of data in decision-making","Neutral",{"times_played":59,"num_favorites":59},2,"2026-05-24T16:45:48",null,false,true]