2014 was a fantastic year for data science. Funding rounds were huge, the mergers and acquistions space was active all year, data science skills proved to be the hottest of the year. But will data science continue to flourish in 2015? We asked four industry experts- working in AI, big data strategy, Hadoop and data transformation respectively- to share their thoughts on how big data will progress in 2015.
1. Data Scientists Not So Sexy in 2015
“In 2015, CEOs will demand more from their data than the elusive “big insight” that data scientists keep promising but haven’t been able to deliver.They will decrease investments in human-powered data science and adopt scalable automation solutions that understand data, unlock insights trapped in it and then provide answers to ongoing problems of understanding performance, logistics, provisioning and HR just to name a few.”
Kris Hammond, Chief Scientist for Narrative Science
Read our interview with Kris here.
2. Big Data Goes Mainstream in the Enterprise
In 2014 one of the things that we noticed changing rapidly in Big Data was its increasing enterprise focus. Adoption of open source platforms like Hadoop was originally limited to specific applications within early adopters like ad-tech and global web properties. But today, more and more mainstream companies view Big Data as a must-have. Manufacturing companies, for example, are now able to combine reliability and performance data from the field with testing data from the factory to help design and build better and more profitable products. Expect to see Big Data make major impacts on the competitive landscape in 2015. Companies which effectively embrace and deploy these solutions will expand their market and profit shares at the expense of lagging competitors.
Ron Bodkin, Founder of ThinkBig
Read all of Ron’s predictions here.
3. Self-Service Big Data Goes Mainstream
In 2015, IT will embrace self-service Big Data to allow business users self service to big data. Self-service empowers developers, data scientists and data analysts to conduct data exploration directly. Previously, IT would be required to establish centralized data structures. This is a time consuming and expensive step. Hadoop has made the enterprise comfortable with structure-on-read for some use cases. Advanced organizations will move to data bindings on execution and away from a central structure to fulfill ongoing requirements. This self service speeds organizations in their ability to leverage new data sources and respond to opportunities and threats.
John Schroeder, CEO of MapR
4. Data Science Will Belong to the Economists
We will start to see data science (to the extent that it operates as a coherent entity) increasingly rely on the domain expertise of economists. The early days of data science were very math, statistics and programming oriented. Then there was the rise of the “computational social scientist,” which added sociology to the mix.
Many trend setting data science places are finding that sociology, and similar disciplines, tend to be retrospective, while other fields, like economics, offer simulation and auction modeling and other techniques to get more proactive and predictive with data. Of course, most economists don’t have the programming chops to land most data science jobs, but I think we’ll see that start to change significantly.
Tye Rattenbury, Data Scientist at Trifacta & Former Data Scientist at Facebook
Read our interview with Tye here.
(Image credit: “Happy New Year” by Peter Thoeny)
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