Data scientist just earned the prestigious title of “best job in America” based on Glassdoor’s 2018 rankings. According to the same source, the median base salary currently resides around $110,000, and there are more than 4,500 job openings for this title. And this is not the first year data scientist came out on top; this job title has earned the distinction for three years running.

These statistics paint a picture of a highly in-demand position for organizations looking to make the most of their data strategies. But does your team need one for ad hoc analytics, or is there a better way to go about deriving insights from stored data? Furthermore, where should data scientists factor into your overall data strategy?

What Do Data Scientists Do?

If you think data scientist sounds like a relatively broad title, you’re not the only one. Only understanding the breadth of this position will help you make the most out of each hire. University of Wisconsin Data Science writes that “a data scientist’s job is to analyze data for actionable insights.”

Within this overarching definition, job duties can include:

  • Identifying data analytics problems that hold opportunity.
  • Collecting large data sets from different sources.
  • Cleaning and validating collected data for accuracy and uniformity.
  • Using models and algorithms to mine data stores.
  • Picking out patterns and trends from stored data.
  • Interpreting data insights with an eye on solutions.
  • Communicating data findings through visualization.

There’s some flexibility in exactly how you utilize data scientists to improve business outcomes. However, what organizations definitely want to avoid doing is turning their data scientists into report factories—you want to put data scientists to more dynamic use than simply having them create static reports for other team members.

Advantages of Self-Service Analytics

When data scientists act as the gatekeepers to stored data, it takes longer for end users to get insights. Say a marketing manager needs insight into customer behavior by segment so their team can improve targeting for upcoming campaigns. This user may be waiting days, weeks or months if they all they can do is request a report from a centralized team of data scientists. In this model, there’s a lot of time between asking the question and receiving the answer, which tends to slow down decision-making.

Now imagine this marketing manager can ask questions directly using embedded dashboard analytics, getting answers in seconds. Today’s self-service data analytics platforms like ThoughtSpot allow non-technical users to query data, create visualizations and share their findings with others in communal workflow applications. It’s akin to entering a search query into any online engine; users only need to have the right questions in mind to use self-service analytics.

Think of using self-service analytics as driving a car. You don’t necessarily need to know how to build or fix a car to drive one. But, of course, your organization needs people on staff who do understand the inner workings of automobiles. This is why data scientists will always serve an absolutely vital role within your company. By empowering non-technical users to derive their own insights and create their own custom visualization models, you’re enabling data scientists to work on bigger front-end data endeavors.

Fewer gatekeepers means fewer delays and bottlenecks. This speeds up decision making. IT teams no longer “own” data due to this widespread shift toward data democratization. But rather than making data scientists obsolete, this actually means these specialists can focus on bigger and better things.

Your team doesn’t need a data scientist for ad hoc analytics if it has a platform that democratizes data. But it does need data scientists to gather, format and interpret data.

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