As I am reading through the book Now You See It written by Stephen Few, I came across a good chapter, which highlights 13 key traits of a data analyst.  Although much of the book is focused on visual techniques, the author explains how critical it is to understand the story behind the visuals before you can present it properly.  In his second chapter, Few focuses on prerequisites for enlightening analysis.

These 13 traits are qualities to keep in mind whether you are looking for your first market research job or even if you have 40 years of experience in the field.  They are a good point of reference for what types of personalities are needed to be successful in the field.  Here are the 13 traits that make the most productive data analyst (in no particular order), and my spin on exactly what each one references:

• Interested – point-blank, the most important trait for any job really.  It’s the single characteristic that differentiates a job from a career.  This is arguably the only personality trait on this list that cannot be cultivated.  You either have it or you don’t.
• Curious – the difference here is although you may be interested in a topic, you need to have the curiosity to dig deeper into data.  Don’t just report the 41% and move on, figure out why 41% is 41%.
• Self-motivated – this is a reference to being proactive in your analysis.  Don’t wait for your supervisor to ask, “Why?”.  Have the motivation to search and explore meanings and give the data your due-diligence.
• Open-minded and flexible – what Few stresses here is objectivity, which is a topic we’ve covered in prior blog posts.  Although you may have some preconceived notions about how the data will turn out, be open-minded and be able to adjust your findings on the fly.
• Imaginative  – being ‘imaginative’ not only in displaying the data but also in analyzing it.  Always think of a next step of how to slice and dice the data set: What if I did this? What if I break this demographic by this factor? And so on.
• Skeptical – this doesn’t necessarily have to be negative, but you must have the ability to question your data.  Truth be told, no data collection process is flawless so it often benefits you to take a step back and ask questions about the findings.  If you’ve been too immersed in the data, it could help to get a larger-picture perspective.  This trait of playing devil’s advocate sometimes gives the research department a bad rap in an organization but it’s essential to good analysis.
• Aware of what’s worthwhile – this comes down to knowing what’s important and what’s not.  You don’t have all the time in the world (or maybe you do) to run every possible cross-tabulation in the survey.  So you need the ability to decipher what’s critical to the objectives of the study and what isn’t.
• Methodical – being systematic in your analysis approach.  A good analyst can create a step-by-step approach and run through an internal checklist of what has to be done on a data set.
• Capable of spotting patterns – the ability to spot trends or themes in the data.  Spotting the data patterns takes a unique eye.  Oftentimes, this doesn’t materialize until you can see it in graphical format.
• Analytical – similar to reverse engineering.  Being able to take a percentage or a number and “decompose it,” as Few states, into the sum of the parts that make it up.  For example, don’t look at the fact that 66% of customers have used the product in the past month.  Look at what percent of those 66% are males/females, if they are females – What age are they? Do they have children? What type of job do they have? What is their income level?
• Synthetical – just simply engineering, not reverse engineering.  Taking all of the pieces of different data points, patterns, and themes and being able to compose them into a story.  This is also a critical trait of good market research report writing.

Your #MRX report doesn’t have to be a John Grisham novel, but in the end, your report should offer some closure to the reader.

— George Kuhn (@gwkuhn3) April 23, 2012

• Familiar with data – this involves knowing a little bit about the data collection process and the operations behind the data before simply jumping right into analysis.  It always helps to understand the background of the data before making conclusions.
• Skilled in the practices of data analysis – Yes, somewhat obvious but it takes practice to be a good data analyst.  You learn as you go, and each new data set you analyze helps you refine your internal process and improve for the next time.