The Tools You Use as an Analyst Will Define Your Success

I’ve been writing SQL for about 26 years, and I’ve been building world-class analytics teams for the last 12 years across Amazon, eBay, VMWare, and a lot more. I’m also a six-sigma process engineer and I incorporate process improvement techniques into every part of my work as well my team’s work. What I’ve learned over my years is that the tools that you use as a data analyst matter. I’ve also learned that you can’t be a world-class data analyst without understanding your tools and the best tools to use for the job.

You can’t be a world-class data analyst without understanding your tools and the best tools to use for the job.

Think of it this way. You could use a wrench to try to hammer a nail into a board. It might work. But it doesn’t mean that the right tool was used for the job. If you use a wrench as a hammer, there’s a good chance that the nail, board, and wrench are going to get mangled.  It would also take you a lot longer to do the work than had you use the right tool for the job.

The Tools to Choose From

In the analytics space, there are a handful of tools that analyst will commonly work with. These include Excel, SQL, Tableau (or some alternative visualization tool), and sometimes Python depending on the company and depth of the role. While each of these tools has their strengths and weakness, I’ve rarely seen people talk about how a tool impacts the individual analyst. Even more rare is how these tools impact the analytics environment within a company.

Instead, what tends to happen is that analyst will gravitate towards the easiest, prettiest, and most comfortable tool to use. The order of tool preference is frequently Excel, Tableau, SQL, then Python. While some tools may feel easier to the individual analyst, that tool isn’t always recipe for growth, quality, efficiency, or success.

Efficiency

If you want to get to the next level or simply be seen as a valuable employee, you have to be productive. But the word productivity is a relative term. What feels productive for one person, in one team or company, may be the furthest thing from productivity when benchmarked against top performing teams. Sadly, this means that many data analyst aren’t achieving their highest level of productivity because of the tools that they use.

For example, I’ve seen countless analyst that gravitate towards using a visualization tool while actively avoiding SQL as their tool of choice. I’ve even seen those same analysts claim that visualization tools are faster than SQL. But that’s almost never actually true.

For quick pivots of data, the visualization tool might seem faster. But when you add up all the time that it took to build the visualization, plus quality assurance checks, difficulty tracking down bugs, and duplicate work that inevitably occurs when needing to deeper dive or track down bugs, the tools are horribly inefficient. And that’s just scratching the surface of why they aren’t efficient.

Quality

Have you ever inherited an Excel workbook from a co-worker and been confused as to what the workbook is about? Ever clicked into a cell to find the most complex formula you’ve ever seen? These are just a few of the problems with Excel.

In Excel, there are no good way to document the purpose and details of the workbook. Then when you’re in the workbook, there are no good ways to document how that formula works. Worse, the formulas are hidden unless you click on the cell, and they can be incredibly difficult to debug. The result of this is confusion, frustration, inefficiency, and a lot of quality issues.

Without proper documentation, visibility, and formula structure, it’s nearly impossible to guarantee the quality and accuracy of the data being presented. This not only creates problems on a day-to-day basis for an analyst but can also severely damage the analyst’s personal brand and ability to get promoted.

But Excel isn’t the only tool that has quality control issues. All tools do, but some like Excel and Tableau create more issues than others.

Growth

Many analysts gravitate towards the tool that they think the easiest tool to use and they overlook the proper tool to use. I get it. New things are uncomfortable. But sticking with what everyone else is doing or what you’re most comfortable with isn’t necessarily the right thing to do or a recipe for growth. It’s very possible that you may have some success in your current situation using a sub-optimal tool.

But when you look to move to another company or role, you may likely find that you’ve fallen behind. This is because the best analysts in the industry are working with the proper tools and are gaining experience. They are moving ahead while in comparison, you appear to remain in the same place. This isn’t to say that you aren’t learning anything at all. You may be learning, but you may be learning how to hammer that nail with a wrench just a little bit better.

Conclusion

Contrary to what other’s may be telling you – the tools you use don’t matter or that you can do everything in your visualization tools, that simply isn’t the case. When you don’t use the right tool for the job, you won’t become one of the best in the industry. Not only will this lead to fewer promotions and a lower income, but it will also put you at risk of being pushed out of the company or having trouble landing your next job.

If you want to be a world-class analyst or engineer, you first have to learn what tools are in the toolbox. Then you have to learn how to use them and how to use them well. You also need to know when to use them and when to not use them. When you learn to use the proper tools, you’ll work more efficiently, produce deeper and higher quality insights, and you’ll open the doors to a more rewarding career.