Summary:

  • Data Science principles can be scaled to benefit any company – meeting them where they are on the analytical continuum and improving analytical rigor and sophistication.
  • “Data Science Lite” employs all of the same skills as full-fledged Data Science.
  • The data science and analytics community must be willing to meet companies where they are to help them make meaningful advancements for their business.

Data Science is for Every Company

By now you have undoubtedly heard of the concepts of Data Science. You have likely heard success stories of companies that are employing big data and machine learning (ML) algorithms to advance their business. What if your organization’s data isn’t “big?” What if you aren’t quite ready to build and deploy a living, breathing ML algorithm? What if you are still making the case to your company’s leadership that there is value in using data in more creative ways and are not ready for a big investment?

I argue that data science is still for you! The same skills and techniques that highly sophisticated companies are using can easily be scaled to benefit organizations that aren’t as far along the analytical continuum to nudge them up in their level of analytical rigor and sophistication.

What you need is what I like to call: Data Science Lite.

What is Data Science Lite?

You have probably seen some version of this image, which has been used to describe the unique skill set of a Data Scientist. Data Scientists have:

  • Technical & programming skills that allow them to work directly with raw data and databases.
  • Quantitative & analytical skills that allow for the application of statistical techniques to data.
  • Subject matter expertise that allows the findings of analyses to be applied within the business context.

Data Science ‘Lite’ uses all of the same skills and principles, but they are scaled down to work for companies who might not be as far along the analytical continuum. For example, if the left-hand column of this table doesn’t sound like your organization, the right-hand column might:

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* For a number of reasons, I wouldn’t recommend keeping your data in an Access database, but a Data Scientist can work with it there and help you move it to a more stable platform.

** R appears in both columns because it can be used in a variety of ways ranging from very sophisticated to more basic.

The argument I’m making is that any company can benefit from Data Science techniques. Making informed, data-driven decisions benefits all companies, even if you aren’t ready to make a huge investment. We in the data science and analytics community should be committed to helping all companies move along the analytical continuum. We should be willing to meet companies where they are and set up a plan for making impactful, yet realistic progress toward more rigorous sophisticated data analysis.