Despite the promise of software vendors that their “methodologies extract meaningful and actionable insights from Big Data,” my experience is that no methodology can substitute for the human brain of an astute analyst. Great data analysts are a rare and valuable commodity. So what makes for a great one?
- They know the data inside and out
- They deliver accurate information
- They draw useful conclusions.
Most articles on this blog have focused on the first point—knowing your data. And this point alone can take you a long way in your career. But in this post, I’m going to tackle the third, softer and squishier realm—how to draw useful conclusions.
One of my colleagues contends that great analysts are born, not made. There may be some truth to that. (I do think a certain inate mindset makes for a good analyst. Curiosity, attention to detail, ability to solve problems are three qualities that come to mind.)
But at the same time, you can always improve your craft and learn from others. (I certainly do!) So here are some of the things I do regularly to improve the quality of my insights and conclusions. These tips can be applied to any type of data. They reflect a mindset and approach, not a recipe to follow.
Understand the Business Questions Driving the Analysis
You’ve heard this a million times, but it’s easy to forget when you’re rushing to fulfill a data request: Pause to ask a clarifying question. It’s the number one way to add value when you’re presented with a “business question” that’s phrased as a data order.
How do you do this in practical terms? Ask: “Can you give me more background on your data needs?” or “What are you going to use this information for?” or “What type of decision will you make with this data?”
Separate Analysis Process from Communication Process
Yes, you want to understand your audience’s needs and keep them in mind. But don’t start your analysis thinking about how you’ll communicate your answers. To commence work by attempting to look at data in a way that’s “right for your audience” will constrain your thinking and quality of your analysis. Instead, pull the data you need to draw your conclusions. Create graphs for yourself and delve into whatever details you want to examine. It’s never a waste of time to conduct an analysis for yourself first. For example, I usually do some graphing of data over time—often weekly data—to get a handle on trends. But I rarely show those graphs to clients. Only after I draw my final conclusions and think about the points I’m going to make do I decide how to visually display the information in summary form, targeted to the needs of my audience.
Be Creative About Finding Benchmarks and Other Ways to Add Context
Pointing out a single number or trend isn’t terribly enlightening. What brings insight is adding context, by comparing other products, other markets, other measures or other periods. For example, dollars are up, but what about units? This brand is down but what about the category? This category is flat but what about adjacent categories or the market as a whole? This retailer looks strong but how are they doing versus their competitors? We’re up versus last quarter but what about versus a year ago?
One of the most powerful tools of syndicated databases is how much context they contain (or how much more context you can buy if needed). Challenge yourself and exercise your creativity in finding multiple ways to add context. Not everything will pan out, but you won’t know until you look.
Drill Down One Level Deeper
You often need to drill down into more details than you’d like, to really understand what’s going on. It would be faster and less messy to stay high level but, invariably, it doesn’t work. For example, when I’m doing a retailer business review for a client, I pretty much always end up graphing UPC level distribution by week. For me, figuring out when new items appeared and when older items were discontinued is often the key to explaining what’s driving the big picture trends. In my experience, there’s rarely a substitute for a deep understanding of what’s happening in the data at a detailed level.
Rise Above the Details
A great analyst is a contradiction: you need to be both very detail oriented and big picture/strategic. It’s hard to find that balance. In my experience, it often requires deliberately swinging back and forth between details and a big picture perspective. When I’m mucking around in detailed data, sometimes I feel like I’m spinning my wheels. Then I remember to ask myself, “What’s really going on here?” and revisit the big picture question. In retrospect, it may seem like you spent more time in the muck than you needed to. But that’s part of the process. You have to go deep, then pull up; go deep and pull up. Here are some tactics to help keep the big picture in mind:
- Summarize what you’ve learned in a few sentences (no numbers).
- Guess what someone will ask you when you present your findings.
- Ask yourself what you would do, given what you’ve learned.
- When you think you are done, ask “why [fill in your conclusion]” and see if you can think of a way to go one layer deeper.
Want to read more about how to be a better analyst? Here are some links I like. Two of them are specifically about web analytics but the general principles they describe are relevant for any analyst in any industry.
Top Ten Signs You Are a Great Analyst, Avinash Kaushik, Occam’s Razor
13 Traits of a Good Data Analyst, George Kuhn, Research and Marketing Strategies
What Makes a Good Analyst, Neil Mason, ClickZ
Knowledge Workers are Bad at Working (And Here’s What to Do About it…), Cal Newport, Study Hacks Blog
In my next post, I’ll share some techniques for improving on my second great analyst requirement: delivering accurate information. In the meantime, I would love to hear your thoughts on how to deliver more value as a CPG (or any other type of) data analyst. If you have other “better analyst” articles you like, please share those as well!
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