5 tips for embracing open data science in the enterprise

5 tips for embracing open data science in the enterprise

5 tips for embracing open data science in the enterprise

Businesses are continually seeking competitive advantage. Lately, the focus has been on leveraging data to seize opportunities, detect possible weaknesses, and triumph over competitors. Big data, in particular, offers a multitude of ways to use data to drive strategic, operational, and execution practices. And, increasingly, data science is the way to get there.

First, a definition: data science is a multidisciplinary field that combines the latest innovations in advanced analytics—including machine learning and artificial intelligence—with high-performance computing and visualizations to extract knowledge or insights from data.

The tools of data science originated in the scientific community, where researchers used them to test and verify hypotheses that include “unknown unknowns.” These tools have entered business, government, and other organizations gradually over the past 10 years as computing costs have dropped and software has grown more sophisticated.

But proprietary tools and technologies have proved to be inadequate to support the speed and innovation happening in the data science world. Enter the open source community.

Open source communities want to break free from the shackles of proprietary tools and embrace a more open and collaborative work style that reflects the way they work—with teams distributed all over the world. These communities are not just creating new tools; they’re calling on enterprises to use the right tools for the problems at hand.

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Open data science is revolutionary. It transforms the way organizations approach analytics. With open data science, you can boost the productivity of your data team, enhance efficiencies by moving to a self-service data model, and overcome organizational and technical barriers to making the most of your big data.

Here are five things you can do to embrace open data science:

Wholeheartedly adopt open source. Traditional commercial data science tools evolve slowly. Although stable and predictable, many of them have been architected around 1980s-style client-server models that don’t scale to internet-oriented deployments with web-accessible interfaces. On the other hand, the open data science ecosystem is founded on concepts of standards, openness, web accessibility, and web-scale-oriented distributed computing. And, open data science tools are created by a global community of analysts, engineers, statisticians, and computer scientists who have hands-on experience in the field. This global community includes millions of users and developers who rapidly iterate the design and implementation of the most exciting algorithms, visualization strategies, and data processing routines available today. These pieces can be scaled and deployed efficiently and economically to a wide range of systems. By enthusiastically adopting—and contributing to—this community, your chances of having successful deployments multiplies exponentially. Build a data science team with diverse skills. Successful projects start with gathering together the right people and organizing them in a way that makes operational sense. Open data science is no different, but the diverse range of skills required might surprise you. True, data science inherently rests on mathematics and computer science. A strong statistical background has traditionally been assumed necessary for one to work in data science. However, these magical “data scientist” unicorns are very difficult to find. Moreover, open data science is a practical real-world discipline that requires a team that includes business analysts, data scientists, developers, data engineers, and devops engineers.

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