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Minding—and Bridging—the Skills Gap: 3 Ways to Use Data Science

Blog: The Tibco Blog

I read in a recent report that the sexiest job of the 21st century is that of a data scientist. Only a teensy fraction of the human race has the skills to do it, so no wonder it’s so sexy. Statistics, business analysis, graphic design, programming/modeling, and communications skills rolled into one? I’m exhausted already. No wonder there is a shortage. It’s projected that there will be 190,000 data science job openings by 2018, but no one to fill those seats.

The advent of big data is partly to blame. As data itself exploded, the opportunities to glean insights from it exploded too. With data analytics serving as an accelerator for competitive advantage, both vendors and customers need to plan for a future in which qualified data scientists are far and few between. So here are the $100,000 questions: How can companies attain the value of advanced analytics without a team of data scientists? And what are analytics vendors doing to help them?

Many analytics vendors have responded by creating dumbed-down software that is user friendly, but lacks the juice required to analyze large, complex sets of data. Companies are looking for solutions that combine the power of a Porsche with the utility and ease-of-use of a Honda.

Your ride is here.

Consider the Tesla S. It’s one of the most advanced vehicles in its class, but looks like a family sedan. Anyone could drive it, but an electromechanical engineer might appreciate it most. So what is the Telsa of analytics? I’d pick TIBCO Spotfire.

Approachable yet powerful, only Spotfire has the ability to bridge the gap between the average business user/analyst and the elite data scientist. Let’s pop the hood, kick the tires, and take it for a spin. Using plain speak, let’s learn how Spotfire does it.

A look under the hood.

The following three capabilities make advanced analyses accessible to a wide variety of users and help bridge the skills gap that exists today.

  1. Recommendations: Recommendations does what it says. It recommends visualizations that suit your data set best. It takes the guesswork out of picking a visualization so you can start analyzing your data faster. The simple UI is point and click. Analysts and data scientists use Recommendations as well, and with business users self-enabled, they’ll be able to focus on more complex analyses.
  1. Statistical Features: Spotfire places statistical capabilities in the hands of users, allowing them to, for example, apply an advanced clustering algorithm to a dataset, simplifying the resulting visualization for faster recognition and understanding. Once created, an analysis can be exported and used in the creation of more complex analyses.
  2. Predictive Analytics: Spotfire enables analysts and data scientists alike to create apps and visualizations that allow business users to anticipate events and act on them. Analyzing historical data allows users to predict future opportunities and reduce customer churn by analyzing patterns and trends that are presented in the visualizations. The analyses exported from Spotfire and loaded into Spotfire Statistical Services become available to analysts and developers, who can then embed them in applications for business users and others with varying skillsets, bridging the gap that currently exists among business users and data scientists.

Read the full article, Top Three Ways to Use Data Science. Or if you’d rather drive the Tesla, try Spotfire today.

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