What should You study or learn if I want to be a data analyst?

The short version is that you should learn how to be a great asker of questions and study the techniques and tools available to you for answering them.

Learn to Be a Great Asker of Questions

The first step to being a great analyst is asking great questions. No amount of technical knowledge makes up for a lack of analytical creativity and curiosity.

It’s said that smart people ask hard questions while really smart people ask simple ones. Indeed, many of the most important questions you can ask about your company are the simplest. Why do people choose our products over our competitors’? Why do customers leave us when they do? Should we offer discounts to boost sales? When you’re up to your neck in being a good do-er it’s easy to lose sight of these fundamental questions because people don’t ask you to answer them when you’re still green. But oh, the liberation once you do! This is how you can begin to understand and contribute to solving some of the most important challenges facing your business today.

Having managed a team of data analysts for years, my advice to anyone in this role or aspiring to it is to not start with the data. Start with your hypotheses about a business problem and then figure out how you would try to prove or disprove them in the abstract. What would need to be true in order for you to prove, or at least make a strong case for your idea? Only once you have this clear in your mind should you turn to the data and see what you can do with what’s available.

If you want to do this at a technology company, it’s even more important that you hone this skill first, because more people will be familiar with working with data, so it will be hard to set yourself apart. It’s hard to generalize the kinds of questions that will be important because every business is different, but I have written about How to Find the Hidden Problems at Your Company on my blog, which gives you a good place to start.

Of course, once you have ideas and hypotheses you want to test, you do actually need to have the chops to do the analysis. That’s where the studying comes in, though I truly believe it’s the easier part.

Learn What You Need to so that You Can Answer Questions for Yourself

One of the things I’ve found most surprising over the years is how little understanding most employees have of their company’s own data. Forget about having enough data scientists, most of the companies I’ve come across have shockingly few people who are capable of analyzing their data in the most basic ways. These are category-leading companies — household names — I’m talking about.

Why is this?

The systems companies have in place are partly to blame. Many enterprises, particularly ones that grew by acquisition and inherited multiple IT departments as a result, store their data in systems that are difficult for non-technical employees to use. That alone discourages the vast majority of people from ever touching their company’s raw data. But the larger obstacle is simply that even if decent tools are available or the data is fairly clean (as internal data is more likely to be at a tech company), it takes know-how and patience most people don’t have to analyze data that’s in a relational database as opposed to in a dashboard or an Excel file. It’s not just learning SQL, either. Understanding a company’s data model and how it stores data well enough to query it accurately takes patience and a lot of trial and error. There’s a big difference between the data you work with in business school and what you often see in the real world in terms of data reliability and clarity. This is why most people rely on aggregated reports and cleansed data they get from their IT departments; they can trust the data without thinking twice about it.

The problem with relying on dashboards and pre-built reports to do your analysis is that it’s hard to do work that sets you apart when you’re looking at the same small sliver of the facts as everyone else. Data quality is important, and companies emphasize having a single version of the truth for good reason, but it can seriously constrain your creativity. What happens when you have a question that you can’t answer with your pre-built dashboard or data cube? How do you, for example, analyze whether your hotel is sufficiently meeting the needs of road-tripping families if you can’t create your own dataset to isolate the spending patterns of people who only visited your hotel once, ordered room service off the children’s menu and requested roll-away cots? That’s the kind of analysis that makes others lean in and listen to what you’re saying.

Best of all, you don’t need more than junior-high math to answer that question. All you need is an inquisitive mind and access to data and tools.

Learning SQL and how to interrogate a company’s raw operations data to answer fundamental questions about its business was probably the most useful business skill I acquired in the early years of my career. As it turned out, I was a natural at asking good questions and just needed the tools to be able to answer them. But more than that, a marvelous thing happens inside the businessperson’s mind as a result of analyzing a business through its internal databases: the discipline of querying databases teaches you to ask better questions. More specifically, it teaches you how to structure big questions in such a way that they can actually be answered with precision. It forces you to clean up lazy thinking, because computers don’t allow vague questions. It teaches you to think in sets, an incredibly valuable mindset, without even realizing it. In short, it makes you a better businessperson by allowing you to more fully capitalize on your domain expertise. I know it changed my career tremendously for the better. Ream More

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