What Makes a Great Data Analyst?
By: Joseph Burton
Tags: Learning And Development, Data Analysis, Advice, Data Science, Growth

What Makes a Great Data Analyst?

Photo by Heidi Fin via Unsplash

The first point I should make here is that this summarizes my experience. Just like I tell people trying to reach the Tower of Americas in downtown San Antonio: You see that tall thing? Keep moving towards it.

Know where you’re at, know where you’re wanting to go, and figure out how to get there. There’s no set path.

What General Characteristics Should You Have?

This section is more focused on you as a person, not so much the skills/knowledge that you are expected to have.

Persistence/Resourcefulness: In the world of data, this is huge. Most work you will be involved with won’t be a 5-step easy cake recipe. In fact, you might want to be a little dubious when something is described as super simple. These projects might involve multiple iterations with very small gains for each iteration.

‘All I need is for you to…’ — Your Stakeholder

Curiosity, But With Purpose: When you start working with data, it’s very easy to ponder, ‘I wonder what this would look like if I…’ You can make all sorts of bar graphs, pivot tables, etc. until the cows come home to roost. Things tend to work a little easier with a question in mind first. Once that question is formed, remember the intro, and ask how you can get to that result. When you try to automate for everything that has the potential to be important, you can end up with ‘data vomit’.

When you start working with data, it’s very easy to ponder, ‘I wonder what this would look like if I…’ You can make all sorts of bar graphs, pivot tables, etc. until the cows come home to roost. Things tend to work a little easier with a question in mind first. Once that question is formed, remember the intro, and ask how you can get to that result. When you try to automate for everything that has the potential to be important, you can end up with ‘data vomit’. Problem Solving Skills: Are you the type of person that enjoys solving mysteries? Do you get excited when someone is trying to figure out what is causing a result and your mind starts to go to potential ideas? The thing about data is that, sometimes, results are not always on the surface. You’ll need to be able to think about how seemingly unrelated items/events can influence another. You’ll need to know how to build to get to that answer.

Are you the type of person that enjoys solving mysteries? Do you get excited when someone is trying to figure out what is causing a result and your mind starts to go to potential ideas? The thing about data is that, sometimes, results are not always on the surface. You’ll need to be able to think about how seemingly unrelated items/events can influence another. You’ll need to know how to build to get to that answer. Networking: But don’t we just sit in an isolated bubble and code all day? Hopefully not. You will not be able to answer all the questions thrown at you. Most importantly, you are not involved in a lot of meetings where stakeholders are discussing current/future plans. Information from these meetings has the potential to save you tons of time by not going down the wrong path or by better understanding the business goals. Being able to work with others to be plugged into the business pipeline is very helpful.

But don’t we just sit in an isolated bubble and code all day? Hopefully not. You will not be able to answer all the questions thrown at you. Most importantly, you are not involved in a lot of meetings where stakeholders are discussing current/future plans. Information from these meetings has the potential to save you tons of time by not going down the wrong path or by better understanding the business goals. Being able to work with others to be plugged into the business pipeline is very helpful. Lifelong Learner: This is a double edged sword. New analytical tools will come out. Within those tools, new add-ons will be released. On top of that, businesses change. Goals could change based on leadership or based on what your customers are wanting. New departments or business needs might spring up overnight. You will need to know what these new needs are and how you can assist them.

This is a double edged sword. New analytical tools will come out. Within those tools, new add-ons will be released. On top of that, businesses change. Goals could change based on leadership or based on what your customers are wanting. New departments or business needs might spring up overnight. You will need to know what these new needs are and how you can assist them. Storyteller: Most of the time, numbers are boring! That hits hard. You’re wanting a job where you work with numbers. You need to know that people need to understand what the numbers are doing. When you have a presentation full of just numbers/images of spreadsheets/graphs, eyes start to glaze over and brains tune you out. You are expected to make this interesting enough to keep your audience engaged. Your people need to leave the room and tell someone else what they just heard and be able to convey your major points.

‘That was interesting. I’ve never thought about it that way.’ — Audience members from your presentation

A Strong Backbone (The Point Immediately Following This Goes Hand In Hand With This, DO NOT SKIP IT!): This isn’t brought up as often as it should be. People will question your work. Defend it, but defend it with facts, not emotion. It’s easy for people to think you’re their personal assistant of data and all they need this time (and the next, and the time after that…) is a quick data pull. Your time is important as well. People in a hurry have a strong possibility of creating more time-consuming tasks in the end. Quick and dirty can often lead to long and painful if not managed properly.

This isn’t brought up as often as it should be. People will question your work. Defend it, but defend it with facts, not emotion. It’s easy for people to think you’re their personal assistant of data and all they need this time (and the next, and the time after that…) is a quick data pull. Your time is important as well. People in a hurry have a strong possibility of creating more time-consuming tasks in the end. Quick and dirty can often lead to long and painful if not managed properly. Tactfulness/Gracefulness: You will need to push back at points, obviously. Figure out how to do it without creating a wildfire of other problems. People have put their efforts into their ideas. Those ideas are their ‘babies’. Don’t bust into a room full of people saying someone’s ‘baby’ is ugly and a waste of time.

You will need to push back at points, obviously. Figure out how to do it without creating a wildfire of other problems. People have put their efforts into their ideas. Those ideas are their ‘babies’. Don’t bust into a room full of people saying someone’s ‘baby’ is ugly and a waste of time. Controlled Risk Taker: Have ideas. Experiment. Ask ‘what if…?’ I’ve said it before, Ernest P. Worrell probably would have perfect for this line of work. Be able to ask probing questions.

‘We’ve always done it that way.’ — The antithesis of creativity and overall development

Self-Awareness: Understand how you fit into your team. Know what you’re better at and where you have room for improvement. Your data team should not be just hardcore coders. You can bring super great value to your organization by being an expert in domain knowledge or being well connected with many important people from all departments.

The items discussed so far are generally noted as ‘soft skills’. They’re very important though! Next up are the hard skills…

Photo by Leon via Unsplash

Skills You Might Be Expected To Have

Thinking about a more technical/book/hard skills approach, data analysts generally need a common core skillset. You might even start to notice a bit of a trend when looking at job postings.

Excel: When needing a quick analysis, or direction, Excel can be very useful. Just remember that keeping track of how you found a result is key. Reproducibility is a thing. Sending a screenshot of your results to someone must include step-by-step instructions on how you got to that point AND the data put into the file to explain where you started.

When needing a quick analysis, or direction, Excel can be very useful. Just remember that keeping track of how you found a result is key. Reproducibility is a thing. Sending a screenshot of your results to someone must include step-by-step instructions on how you got to that point AND the data put into the file to explain where you started. SQL: SQL (Structured Query Language) is very helpful. Have you ever been passed along an Excel file where someone has concatenated 3 columns into a single file to then use as a column for a V-Lookup? It’s within your power, and responsibility, to avoid ‘Frankentables’. The most complex part of data analysis should not be ‘What in the world is this person’s file even doing?’ SQL can help solve a big part of that and allow for more time to be focused on solving the problems.

SQL (Structured Query Language) is very helpful. Have you ever been passed along an Excel file where someone has concatenated 3 columns into a single file to then use as a column for a V-Lookup? It’s within your power, and responsibility, to avoid ‘Frankentables’. The most complex part of data analysis should not be ‘What in the world is this person’s file even doing?’ SQL can help solve a big part of that and allow for more time to be focused on solving the problems. Scripting Language (Python or R Usually): Just like SQL, knowing how to script and manipulate data is a great skill to have. The thing about the ‘out-of-the-box’ analytical tools is that they can sometimes limit what you can do. Being able to use tools that allow you to go down to a more granular level is a good thing to have in your back pocket.

Just like SQL, knowing how to script and manipulate data is a great skill to have. The thing about the ‘out-of-the-box’ analytical tools is that they can sometimes limit what you can do. Being able to use tools that allow you to go down to a more granular level is a good thing to have in your back pocket. Dashboard and Report Building: At points, you will use tools like Power BI or Tableau to build tools for other to use. Work with your organization to set boundaries on what sort of questions these tools should be used to answer. In the data world, what you build is dependent on why you’re building it. There is no universal tool.

At points, you will use tools like Power BI or Tableau to build tools for other to use. Work with your organization to set boundaries on what sort of questions these tools should be used to answer. In the data world, what you build is dependent on why you’re building it. There is no universal tool. Statistics: You’ll most likely have a lot of visualizations with distributions and whatnot. Once in a blue moon, you might even run into something like Simpson’s Paradox. It’s useful to know these sort of things.

You’ll most likely have a lot of visualizations with distributions and whatnot. Once in a blue moon, you might even run into something like Simpson’s Paradox. It’s useful to know these sort of things. Visualizations: In line with Storytelling, you will be expected to present massive amounts of data into a much smaller window. Anyone can put together a bar graph, but being able to add just the right amount of useful text to accompany is where you can shine.

In line with Storytelling, you will be expected to present massive amounts of data into a much smaller window. Anyone can put together a bar graph, but being able to add just the right amount of useful text to accompany is where you can shine. Communication: Following along the trend of Storytelling and Visualizations, a big part of what you do is to convey ideas to others. They must be able to understand your point. You might have to explain something technical to a non-technical person. You might have to explain something more business-y to a very technical person. You also have to sell your audience on your idea. If you only sell the H-I-P-P-O, the people you will end up working alongside with might not be as interested in helping.

‘Do you understand?’ — Exactly how you should not end a conversation where you are explaining something

Domain Knowledge: This isn’t necessarily something you need to start in your role. This shows a little of the ‘life learner’ and ‘networker’ in it as well. Be able to learn about your business from your colleagues. As a reminder: Data doesn’t decide (link at bottom). Numbers can do all sorts of things. How you apply the ideas to the business can have a big impact to the bottom line.

Is This All?

Not by a long shot. One item I hadn’t mentioned until now is the ability to deliver a product that might not be perfect for the sake of having a deliverable. Chasing perfection will hold you back from being able to make large impacts. Don’t misunderstand this for having janky code that will lead others down the wrong path. Double check your work and do sanity checks.

Data analysis is a big world. There’s a lot to learn. It’s fun though. I have not covered everything you need to know about being a data analyst. It’s hard to cover everything when I’m learning myself.

I wanted to make sure to cover some important bases though and bring up some topics you might not normally read about. As always: keep learnin’!

An article about thinking differently:

Here’s a quick article that is very useful when using SQL and how to avoid a simple bug in your results: