Since our days as cavemen, people and companies have been tasked with what seems like an impossible job: Take in massive amounts of data, process that data, and make decisions based on that data for our benefit and the benefit of others. And while the types of data have changed and the tools used to analyze data have grown exponentially more sophisticated, the process is as old as our species itself.  

Over the past few years, the idea of harnessing data has been in the spotlight. Big data emerged as a C-level issue and has moved from the Trough of Disillusionment into the Slope of Enlightenment. During this time, no profession has been impacted more than the data scientist. Once an afterthought often perceived as a loner in front of a spreadsheet, data scientists have emerged as corporate rock stars charged with helping organizations harness massive volumes of data using advanced analytics.

Over time, the skill set for this group has evolved. We’ve seen a convergence of technological and math skills, and qualified data scientists are now part software architect and part mathematician. Data scientists must be able to understand technology and implement solutions in various languages while at the same time keep up with the advances in mathematics and machine learning that drive the profession.

Even the brightest minds have had to embrace technology tools to complement their analysis as the need to identify patterns in huge volumes of multidimensional data has outpaced the human brain’s ability to do so. Raw computing power has also become increasingly important as organizations demand that decisions be reached and executed quickly.

As we look back at the evolution of enterprise data and the role of data scientist, one thing becomes clear: the landscape looks nothing now like it did just a few short years ago. It is also clear that, just a few years from now, everything will look markedly different than it does now. As data scientists prepare to meet the challenges they’ll face now and in the future, I believe the following changes will take place:

  • Big Data, Small Market Segment: Up until now, segmentation has played a big role in how organizations market to their customers. If you share common characteristics with a group – age, income, race, etc. – it is expected that you will exhibit similar buying tendencies. We will, however, continue to see rapid advancements in computing power, which will allow marketers to refine and improve targeting. Instead of broad swaths of demographic-driven audiences, future data scientists will be tasked with targeting the whole world – on an individual level. This means gaining an even deeper and more advanced understanding of each individual customer or prospect and delivering customized offers to each one. Some early adopters are already starting to realize the benefits of segmentation down to the individual level, but the practice will soon be mainstream.
  • Forget Big Data – Huge Data is Coming: Data is everywhere, coming at us from every organizational nook and cranny. Spanning both traditional and digital sources, big data covers everything form social media to data warehouses to mobile data – and everything in between.

With technologies like natural language processing and speech recognition seeing huge advances, what we see now as “Big Data” will only be a drop in the bucket. These advances will drastically increase the quantity and types of data that data scientists are responsible for wrangling.

  • Get Ready to Make Friends: The ability to understand the math and technology needed to drive big data success is a unique skill set, and one historically held more often by introverts. But at the same time, any successful data scientist moving forward will have to be able to understand how the data he or she is analyzing links directly to their business – and that means interacting with and understanding the perspectives of organizational stakeholders who have been empowered by more user-friendly technology. As the volume of data and the sophistication of the tools and strategies to analyze that data have grown, the job will demand a broader spectrum of hard and soft skills. As the role becomes more important, the number of qualified candidates to perform the necessary duties will further diminish, making those who are capable of both the most popular people in the room.

The field of data science is flourishing, and shows no signs of slowing down as the pace of technological advancement continues to increase. Qualified data scientists – while few and far between – will see their responsibilities and their clout continue to grow, as the value of harnessing, analyzing and acting on huge volumes of data becomes increasingly clear. Data science has come a long way – but the evolution is only beginning. 
Luc Burgelman is CEO of NGDATA, a customer experience management solutions company focused on data analytics.

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