The age of the data scientist has arrived.  In today’s data driven world, it is no surprise that employers are looking to get the most information out of their data as they can.  Along with that has come an increase in demand for the people who can make that possible.

In a recent survey by hiring giant Glassdoor, data scientist was identified as the number one job for 2016.  Data Scientist was previously ranked number nine in 2015; the reason for the increased demand centers around the following business drivers:

1. Finding Meaning in the Data

As more information is gathered, and stored, digitally, the need for skilled employees who can work with that data has skyrocketed.  This goes beyond the tasks performed by data analysts that are used to working in a singular environment, as the data scientist often compiles information from a variety of sources, across multiple systems, to find the correlations wherever they lie.  The ability to cross-compare data held in different formats is key, as well as the ability to explain the connections between them.

Whether it is a need for the employees who can handle the nitty-gritty task of digging in and analyzing data to pull useful information or the managers who can lead their teams to success, data scientists have become the employee du jour.

2. Presentation and Reporting

Not only are most data scientists skilled in gathering information, their role also necessitates them being adept at presenting that information in meaningful and useful ways to the business.  They perform work in the area of trend analysis by identifying patterns that a lesser trained eye may not see.  They examine historical data in hopes of finding insights or anomalies that can help the business make changes in the hopes of welcoming a better and more profitable future.

While the job of a data scientist is technical, a trait that is often characteristic of the role is a certain level of innate curiosity.  The nature of the work is part art and part science, part knowledge-driven and part intuitive.  A successful data scientist has to leverage their curiosity and be open to asking questions and follow the data where it leads them.

It is more than just finding supporting details for already drawn conclusions.  Instead, it is about finding the conclusions within the supporting data.

3. Building the Structures to Support the Data

The more data a company collects, the easier it is to lose track of how to manage it.  Unstructured and semi-structured data is more challenging to analyze.  With differing formats and multiple potential storage points, the lack of organization and data silos can keep key information from being considered when a large scale analysis is undertaken.

Data scientists are not only capable of analyzing data, they are also trained in the proper organization, maintenance, and structuring of the information.  While it sounds simple in premise, the tasks involved when dealing with disparate data are actually quite complex.  It can take highly trained personnel to perform the duties successfully, especially when the desire is not just to organize the information of yesterday, but to format the data for easier maintenance in the future.

Not all systems are designed to communicate with one another effectively.  Data scientists work to connect the information in a way that is workable for the employees and the managers who need to examine multiple facets to draw out the data they need.

4. The Future of the Data Scientist

A McKinsey Global Institute study estimated a shortage of data scientists in the U.S. to the tune of 140,000 to 190,000 qualified workers by the year 2018.  As the use of complex data metrics continue to reach new industries coupled with the continued growth of Big Data analytics and the emergence of The Internet of Things (IoT), the need for data scientists is likely going to continue to grow for the foreseeable future.