5 tips for embracing open data science in the enterprise
Transform the way you approach analytics.
Transform the way you approach analytics.
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.
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:
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.
It also requires new organizational structures—centers of excellence, lab teams or emerging technology teams are a way to dedicate personnel to jump-start the changes. These groups are typically charged with actively seeking out new open data science technologies and determining the fit and value to the organization. This facilitates adoption of open data science and bridges the gap between traditional IT and lines of business. Additionally, roles may shift—from statistician to data scientist, and from database administrator to data engineer—and new roles, such as computational scientist, will emerge. It pays to be flexible and to welcome diversity.
In the open data science world, you’ll have the advantage of moving faster and getting things up and running more quickly, as the open source software is freely available for people to download and start using right away. No need to wait for corporate purchasing cycles. Neither do you have to wait for the long upgrade cycles of commercial software, as the brightest minds around the world are continuously contributing to open source software innovation, and their efforts are made instantly available. That’s a definite plus. Less up-front big planning and big budgeting is needed. But you do have to continually make new choices and new investments, as your needs—and the technology—evolve. This requires making some organizational process changes in budgeting and procurement.
The pace of business today demands responsive data science collaboration from empowered teams with a deep understanding of the business that can quickly deliver value. They also require the right open data science tools—and increasingly, that’s a wide array of programming languages, analytic techniques, analytic libraries, visualizations, and computing infrastructure.
Open data science is truly revolutionary, and has the chance to change business decision-making as we know it.