Lost? 10 Questions To Ask Your Data Analyst (In Your Next Zoom Meeting)
10 instant ways for anyone — & we mean anyone — to ask better data analysis questions.
Having data is one thing. But knowing what questions to ask to paint a clearer picture of the business situation, is another thing.
And the problem is that many people aren’t trained to know how to ask questions of data. People lack confidence and the know-how to break data analysis into bite size components. They then struggle to assemble those parts (the analysis) to paint a picture of business performance — essential requirements for data-driven decisions.
- So when it comes to data, what questions should you be asking?
- How can you improve your critical thinking skills (to rival a data analyst)?
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.
Perfect for asking better questions in meetings.
TABLE OF CONTENTS:
- So when it comes to data, what questions should you be asking?
- How can you improve your critical thinking skills (to rival a data analyst)?
- And is there a way to automate this? (HINT — the answer is ‘yes’)
10 Ways For Anyone To Ask Better Questions Of Data Analysis:
- Have a business question. Successful data analysts don’t go fishing for insights without a question or hypothesis in mind. 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.
- Always push to drill deeper and deeper. Successful analysts know that true insights are often hidden in the details — that looking at results in aggregate, masks what’s really going on. As such, they segment top-line results not only by common factors (e.g. location, product, line of business), but also by factors a business team wouldn’t have thought to ask. Always ask to drill down, segmenting performance.
- Always benchmark. What separates a fact from insight is context. Great data analysts know this, proactively benchmarking results over time and by meaningful points of comparison. Benchmarking performance is everything.
- Keep asking ‘why’. 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?’. 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.
- Ask whether anything ‘odd’ is skewing the results. 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.
- Don’t be blinded by what people are showing you. Find out what’s missing. Ideally a superstar analyst knows more about business performance at every level of granularity. 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. Ask what assumptions they’ve made, what they’ve excluded and reasons why. Don’t be biased by the analyst’s bias.
- Don’t jump to conclusions — part 1. 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?”. Just because something has shifted doesn’t mean that it wasn’t due to chance (or measurement reasons). Conversely, just because something has moved minimally (and not statistically significant), doesn’t mean that it’s not material to the business. A good analyst will demonstrate whether the results matter from a statistical perspective (statistically significant). But only you with a business lens, can interpret whether this finding is insightful.
- Don’t jump to conclusions — part 2. 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?”. Superstar analysts don’t get defensive when data quality or system issues occur. Instead they use it as an opportunity to improve and resolve data capture processes and tools. They identify and resolve when results appear to be driven by system or process issues (e.g. data capture / quality). Don’t loose your cool. Data and systems issues happen. Seek to resolve collaboratively and keep your cool.
- Don’t get sidetracked by shiny new toys. Superstar analysts know what analysis techniques can be reliably applied to their data. 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. Regardless of any hype, stay focused on your business questions.
- Keep pushing for simple explanations. Successful data analysts know that communicating insights, up and down the business, is key to delivering value and becoming a trusted advisor. Not the ability to speak code alone. You don’t need to be a data scientist to explain and write up results in simple language, so that anyone could make sense of the findings without requiring a PhD.
So how do I get all 10 skills? → THE OPPORTUNITY
Instead of hoping to hire unicorns that have all 10 traits outlined above, there is a new way of up-skilling entire business teams, delivering on all 10 data capabilities:
Enter Automated Analytics (Hello Hyper Anna)
Hyper Anna’s sweet spot is this solving this very problem. How? Automatically analysing historical data, leaving no angle of your data unturned, so you have answers when you need them and can uncover truths you didn’t even know to look (or ask!) for. This means all 10 traits of successful data analysts are embedded in your organisation, minus the pain of hiring!
CONCLUSION
Best-in-class companies don’t wait until their five-year digital transformations are complete before thinking about streamlining data consumption and up-skilling business teams. Instead, they make their business-intelligence capabilities available to employees on a self-serve basis from the start, helping all teams build data skills for the digital age.
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ABOUT HYPER ANNA — The World’s #1 Automated Analytics platform used by companies like Microsoft, IAG, Singtel and Westpac worldwide. https://www.hyperanna.com/
