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How To Think Like A Data Analyst: The Business Leader's Guide

How To Think Like A Data Analyst: The Business Leader's Guide

Chelsea Wise
Learn how to ask better questions. Build new skills for the digital age.

We have the privilege of training Fortune 500 business leaders to better understand and derive value from their data. The participants, whilst data savvy, aren’t data analysts. In fact, spreadsheets overwhelm them. And dashboards whilst simplistic, are superficial, failing to satisfy their need for insights. These leaders want to up-skill their data literacy skills, however, the initial thought is overwhelming. In fact, the number one question we get asked by when we first get to know each participant is:

So, how do I know what questions to ask of my data?

Yes, most people are comfortable asking Google ad hoc questions like — “best cafes in West Village”or “opening hours of Target” — but when it comes to asking questions of data (and knowing where to start), it can be super overwhelming.

Case in point — Put a spreadsheet in front of most people, and ask them to start asking questions of the data in a structured, informative, problem solving manner. The reality is, many will struggle. They will simply ask “what’s driving my business?” or “how can we make more money” — great questions — however, are too broad for interrogating the data in front of them.

Having data is one thing. But knowing what questions to ask of data 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.

Table Of Contents
  1. So when it comes to data, what questions should you be asking?
  2. How can you improve your critical thinking skills (to rival a data analyst)?
  3. And is there a way to automate this? (HINT — the answer is ‘yes’)

11 Things Every Successful Data Analyst Has In Common

  1. They have a business question. Successful analysts don’t go fishing for insights without a question or hypothesis in mind.
  2. They know where to drill down, segmenting performance. 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.
  3. They know where and how to benchmark performance. What separates a fact from insight is context. Great data analysts know this, proactively benchmarking results over time and by meaningful points of comparison.
  4. They identify drivers of performance (e.g. what caused this). 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?’.
  5. They identify outliers and anomalies, especially in areas outside their usual line of questioning/investigation. 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.
  6. They identify trends at any level of granularity, especially where manual interrogation would be inefficient (i.e. taking hours & the risk of missing what could be important interactions). 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.
  7. They identify whether results are material from a statistical perspective (statistically significant). 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.
  8. They identify and resolve when results appear to be driven by system or process issues (e.g. data capture/quality). 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.
  9. They 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.
  10. They can explain and write up results in simple language, so that anyone could make sense of the findings without requiring a PhD. 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.
  11. They perform analysis in a timely manner, ideally as soon and as quick as possible — providing an opportunity to inform next steps, instead of finding out too late and never having an opportunity to act.

So how do I get all 11 traits? → THE OPPORTUNITY

Instead of hoping to hire unicorns that have all 11 traits outlined above, there is a new way of up-skilling entire business teams, delivering on all 11 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 11 traits of successful data analysts are embedded in your organisation, minus the pain of hiring!

Automated Analytics: How Does It Work?

Conclusion

Best-in-class companies don’t wait until their five-year digital transformations are complete before thinking about streamlining data consumption and upskilling 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.

Looking to read a bit more?

Our 100 page book is the go to automated analytics guide. Over 100 pages of the latest research for data and business teams, broken down into bite size data led intelligence for busy people.

DOWNLOAD THE FREE GUIDE HERE →

Download The Ultimate Guide To Automated Analytics — over 100 pages of the latest research for data and business teams (2021)
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