![]() ![]() Regardless of any hype, stay focused on your business questions. Instead of using one technique, tool or template as a hammer, successful analysts are life long learners, curious about new tools and techniques, but not wasting time when it’s overkill. Superstar analysts know what analysis techniques can be reliably applied to their data. Don’t get sidetracked by shiny new toys.Seek to resolve collaboratively and keep your cool. They identify and resolve when results appear to be driven by system or process issues (e.g. Instead they use it as an opportunity to improve and resolve data capture processes and tools. Superstar analysts don’t get defensive when data quality or system issues occur. If results have changed quite a bit, you can ask: “Before we go down a rabbit hole, is there a data or system issue behind this?”. But only you with a business lens, can interpret whether this finding is insightful. A good analyst will demonstrate whether the results matter from a statistical perspective (statistically significant). Conversely, just because something has moved minimally (and not statistically significant), doesn’t mean that it’s not material to the business. Just because something has shifted doesn’t mean that it wasn’t due to chance (or measurement reasons). Regardless of how much results have shifted, you can ask: “Are these changes statistically significant? Are we looking at these results on an appropriate scale?”. Ask what assumptions they’ve made, what they’ve excluded and reasons why. Whilst it’s unlikely that they can cite performance off the cuff, they have code or tools that automates the ability to zoom into trends at any level of granularity. Ideally a superstar analyst knows more about business performance at every level of granularity. Don’t be blinded by what people are showing you.Although laser focused on solving business problems, successful data analysts never forget data foundations - zooming out to examine the structure of the data, proactively identifying outliers and anomalies, and examining the drivers of any unexpected behaviour. Ask whether anything ‘odd’ is skewing the results.Whilst it’s impossible to identify and measure the impact of every single possible factor contributing to your findings, seeking out ‘why’ is what separates fact from insight. A superstar data analyst is obsessed with the ‘why’ - proactively looking for the root cause, even if the question by the business was merely ‘what happened?’. Great data analysts know this, proactively benchmarking results over time and by meaningful points of comparison. What separates a fact from insight is context. Always ask to drill down, segmenting performance. location, product, line of business), but also by factors a business team wouldn’t have thought to ask. As such, they segment top-line results not only by common factors (e.g. Successful analysts know that true insights are often hidden in the details - that looking at results in aggregate, masks what’s really going on. Always push to drill deeper and deeper.If the analysis doesn’t make sense, people are overly complicating the discussion, or there is too much information (analysis paralysis!) - then go back to basics: what is the most important business question - not data question - but common sense business question you need to solve? Start here. Successful data analysts don’t go fishing for insights without a question or hypothesis in mind. And is there a way to automate this? (HINT - the answer is ‘yes’)ġ0 Ways For Anyone To Ask Better Questions Of Data Analysis:.Perfect for asking better questions in meetings. In this Medium article, we share 10 things anyone can do to build greater confidence in running your own analysis or evaluating the analysis of others. How can you improve your critical thinking skills (to rival a data analyst)?.So when it comes to data, what questions should you be asking?.They then struggle to assemble those parts (the analysis) to paint a picture of business performance - essential requirements for data-driven decisions. People lack confidence and the know-how to break data analysis into bite size components. But knowing what questions to ask to paint a clearer picture of the business situation, is another thing.Īnd the problem is that many people aren’t trained to know how to ask questions of data. 10 instant ways for anyone to ask better questions of data ![]()
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