Staying employable in an era of big data


Data matters more than ever. Progressive organizations such as Netflix, the University of Texas System and others are using contemporary data visualization tools to find the signal in the noise that is big data. Dataphobes won't be able to hide for much longer.

These facts were very much on my mind as I researched and wrote The Visual Organization. Of course, organizations don’t read books; people do. What can "data-challenged" employees do to increase their marketability?

It doesn’t matter if you’re looking to change careers or if you’re a recent college graduate saddled with astonishing levels of debt (represented visually here).

While no guarantee, consider the following tips to make yourself as employable as possible for as long as possible:

  • Brush off that dusty statistics book - These days, data is becoming the lingua franca of business. You might not have to create chi-square distributions as part of your job as a marketing analyst, but understanding some tenants of probability and statics certainly won’t hurt you. For instance, know what a normal distribution is and the difference between Type I and Type II errors.
  • Hone your data and tech chops - In the 1980s and early 1990s, it was common for employees in marketing, HR and finance to request data from the IT department. Now, that process seems antiquated. More and more data is available to laypeople. Play with data. Show that you can do more than just sort a spreadsheet. Be prepared to talk about ways you’ve analyzed data on different projects.
  • Learn a bit about design - In the book, I stress the importance of hybrid employees, and it’s silly to ignore a sexy topic these days: design. It doesn’t take much to influence others via colors, scales, shapes and layouts of dataviz. Being able to speak intelligently about the subject – and demonstrate some examples – puts you at an advantage over “pure quants.”
  • Teach yourself a new tool or two - Many robust dataviz tools are free (read: open source). While different organizations use different tools, it’s folly to think that Excel is the sole means of representing and analyzing information.
  • Visualize your resume - Text-heavy resumes seem so 1998. Why not create a visually compelling CV like Philippe Dubost did? His remarkable resume created quite the stir. This approach might not endear you to more hidebound organizations, but recruiters at creative and innovative companies may very well ask themselves, “What can this person do for us?”

Simon says

Unless you're at the end of your career, it's more than likely that you'll have to deal more with data tomorrow than you did yesterday. Don't expect that trend to wane. Rather than fight the inexorable, get on board. Realize that stats and data aren't just necessary evils. They can help you make better business decisions.


What say you?


About Author

Phil Simon

Author, Speaker, and Professor

Phil Simon is a keynote speaker and recognized technology expert. He is the award-winning author of eight management books, most recently Analytics: The Agile Way. He consults organizations on matters related to strategy, data, analytics, and technology. His contributions have been featured on The Harvard Business Review, CNN, Wired, The New York Times, and many other sites. In the fall of 2016, he joined the faculty at Arizona State University's W. P. Carey School of Business (Department of Information Systems).


  1. Tomas Mosquera on

    Hi Phil, could you please expand a little on your third bullet: Learn a bit about Design. Are there any books/tools you recommend? Perhaps some examples. Thanks a lot. Great post btw.

    • Phil Simon

      There are plenty of books about D3 and other dataviz tools. My point with number three was that it's essential to get your hands dirty and play with these things. Thinking about dataviz in isolation isn't enough. Show me don't tell me, to quote a Rush song.

  2. Kasper Christensen on

    I am currently experimenting with D3 visualization. I find it fun and extremely useful at presenting your results. Is it a library that SAS recommends or maybe already use...?

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