Data scientist, big data, data lakes, Internet of Things – these have been some of the latest buzzwords in technology. To most people these terms are not completely accessible without the use of a dictionary or Wikipedia to clarify. We have more and more information at our fingertips, so looking up these words is not hard; understanding them, however, even for some people within technology, is more of a challenge. Many companies today, are asking themselves, how do we handle all the data we now have? What are we looking for? Who, within the ranks, will do this work, and are they going to do it well? Do we need to outsource?
Data scientist was named the sexiest job title of the century by Harvard Business Review in 2012. Although the work of data analysis stretches decades back, the coining of the term, data scientist, coincided with the increasing use of social media, cellular devices, and cloud computing. In the same article, Data Scientist: The Sexiest Job of the 21st Century, it was stated, “If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.”
Do you have a big data opportunity? Will you need to hire a data scientist?
Despite its sexy title, some believe that people don’t need to hire a data scientist. In a Wall Street Journal interview with Christian Rudder, the Ok Cupid founder and president shared his candid beliefs about the buzz surrounding data scientists, stating, “I think that data science is something that an intelligent mathematical person who can program, who understands human nature and statistics, can do. It’s not like it takes a special genius.” Furthermore, he implied that this may just be a matter of semantics, “I know plenty of people who are great data scientists at OkCupid whose titles are just programmers.”
According to TotalTrax partner, Swisslog, in their 2014 Big Data Solution Summit presentation, “big data will not make magically make all of your business problems go away.” Having a large pool of data in a vacuum does no good, so how you get the benefits— such as understanding consumer needs, streamlining processes, pin-pointing and correcting errors— from your data?
Collect and Log The Data
According to Swisslog, the very first thing you should do is to capture and log the data before asking questions. It is much cheaper to store data than it used to be, so rather than throwing away critical data, store it first. You can’t fully analyze what you don’t have. Once you do get to the point of analysis, Swisslog says, “master information and knowledge before trying to get into wisdom.”
Create Data Lakes, But Don’t Drown in Them
Instead of storing data in silos, or data warehouses, store your data in one large pool, allowing many people in an organization to have access it. This is an all-hands-on-board approach. However, according to Andrew White, VP and analyst at Gartner, people need to know how to manage the data lake: “getting value out of the data remains the responsibility of the business end user. Of course, technology could be applied or added to the lake to do this, but without at least some semblance of information governance, the lake will end up being a collection of disconnected data pools or information silos all in one place.”
Enhance Your Team, Don’t Replace Them
In a Harvard Business Review article, Stop Searching for the Elusive Data Scientist, author and research fellow at MIT Sloan School’s Center for Digital Business, Michael Schrage, writes, “The smartest thing I’ve seen organizations start doing is seed-fund and empower small cross-functional data-oriented teams explicitly charged with delivering tangible and measurable data-driven benefits in relatively short periods of time. The accent is on the word team.”
Schrage noticed that some teams don’t always have the capabilities of a data scientist, so occasionally situations may require a consultant, “Without exception, every team I ran across or worked with hired outside expertise. They knew when a technical challenge and/or statistical technique was beyond the capability. But, unsurprisingly, the outside advisors — in one case, an academic, in others, quants from digital consultancies — were better able to collaborate with teams that had really tried to get their minds around a design challenge.”
Most companies who are already analyzing data probably have very talented, capable employees doing this work. Take a step back to review processes, and don’t jump to “upgrade” with one star employee from the outside who may not know what you really need. Nurture the data and people you do have, before throwing both away.