Data is now creating opportunities for business growth and profit like never before. In the last decade, the emergence of advanced data technologies and superior analytics tools has made it possible for business operators to reap numerous benefits from their data assets, yet for most they’ve only just scratched the surface of data’s potential. Data Science is allowing enterprise’s to successfully leverage that potential like never before.
A particularMcKinsey report published in 2013 predicted that the global business community would feel the pinch of an acute shortage of Data Science professionals for the next decade, specifically a shortage of “1.5 million analysts” skilled at deriving competitive intelligence from the vast amounts of static and dynamic (real-time) data. While such a prediction is coming true, a greater focus on marketing the importance of Data Management to enterprises and within higher education institutions is enabling the entire industry to cope with shortages in ways that were not fully understood only a few years ago. The upheavals within the Data Science industry will continue throughout 2017, but so will more growth and more possibility.
To understand why Data Science is so critical for business success, there are a few pre-conditions that need to be understood:
Given the above information, it is possible to understand why Data Science is going through a global revolution at this juncture in time. The limitations of science and technology that had hitherto withheld the power of Data Science are gradually eroding, and the Data Management industry can expect some major changes to sweep through global Data Science practices in 2017. Some calculated predictions about where the Data Science industry is headed next year are listed below.
2017 Data Science Prediction 1: Machine Learning to Rule the Industry Quora featured a query about how Machine Learning will impact the evolution of the Data Science industry. To answer this query, Claudia Perlich, Chief Scientist at Distillery and Adjunct Professor at NYU, confirms that given the close relationship of Data Science and Machine Learning (ML), the future business analytics world will not be able to survive without ML. Perlich expects that as ML is increasingly becoming more relevant to Data Scientists, a basic skill level in Machine Learning will soon become mandatory to even begin a career in Data Science. Read the full explanation in Forbes blog post titled Machine Learning Will Bring Some Big Changes to Data Science As We Know It.
The Machine Learning fever will continue to envelop Data Scientists in 2017. Organizations will go the extra mile to locate and attract Data Scientists will solid Machine Learning skills to enrich their Data Science Departments.
Gartnermade these predictions a few years ago, but they will be more relevant in 2017 than ever before. As sensor-driven devices continue to engulf all facets of human society, about 50 percent of Business Intelligence (BI) platforms will capitalize on event data streams. This trend will result in a new breed of BI solutions surfacing on the horizon to capture and harvest real-time data troves from attached devices in a wide range of applications like weather forecasting, manufacturing, electrical, voice recognition, and health monitoring systems, to name a few. Also more and more, as Self-Service Analytics pick up, the analytics capabilities offered by Business Intelligence vendors and those by SaaS providers will become indistinguishable.
According to GE’s Industrial Internet Insights Report, the Internet of Things (IoT) market will contribute between $10 and $15 trillion to global GDP in the next 20 years, which can be witnessed in the rising popularity of IoT skills in the Data Science marketplace. IBM, Intel, Verizon, and Microsoft are all aggressively hiring Data Scientist manpower with IoT skills. Also review the article The Life of a Data Scientist.