The creation and consumption of data continues to grow by leaps and bounds and with it the investment in big data analytics hardware, software, and services and in data scientists and their continuing education. The availability of very large data sets is one of the reasons Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend, with Google, Facebook, Baidu, Amazon, IBM, Intel, and Microsoft, all with very deep pockets, investing in acquiring talent and releasing open AI hardware and software.
The increasing interest and investment in AI, in turn, will lead to the emergence of new tools for collecting and analyzing data and new enterprise roles and responsibilities. Here are a few predictions—based on analysis by the International Institute for Analytics (IIA), IDC, and NewVantage Partners—regarding the market for big data analytics.
The big data analytics market will soon surpass $200 billion
IDC says that worldwide revenues for big data and business analytics will grow from $130.1 billion in 2016 to more than $203 billion in 2020, at a compound annual growth rate (CAGR) of 11.7%. In addition to being the industry with the largest investment in big data and business analytics solutions (nearly $17 billion in 2016), banking will see the fastest spending growth.
There’s gold in them there mountains of data
By the end of 2017, revenue growth from information-based products will double the rest of the product/service portfolio for one third of Fortune 500 companies, says IDC. Raw data and various value-added content will be bought and sold either via marketplaces or in bilateral transactions and enterprises will begin to develop methods for valuing their data (see Chief Data Officer below). “Data monetization” will become a major source of revenues, as the world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015, according to IDC.
Creating a data-driven culture will continue to be a challenge
More than 85% of respondents report that their firms have started programs to create data-driven cultures, but only 37% report success thus far, according to NewVantage Partners’ 5th annual survey of senior corporate executives on the topic of Big Data. Technology is not the problem. The culprits include management understanding, organizational alignment, and general organizational resistance. “If only people were as malleable as data,” say the authors of the NVP report.
Cloud vendors will increasingly compete with traditional analytics providers
With the rapid growth in analytics capabilities on cloud platforms, users will leverage large cloud vendors more prominently for analytical software in 2017, says the International Institute for Analytics (IIA). Similarly, IDC predicts that by 2018, new cloud pricing models for specific analytics workloads, will drive up to 5 times higher growth in spending on cloud versus on-premises analytics solutions.
Chief Data Officers will lead their enterprises data monetization initiatives
Today, most executives see the role of the Chief Data Officer as largely defensive, focused on ensuring compliance with regulatory requirements. But going forward, Chief data Officers will drive innovation and will build a data culture for their organizations, say 48.3% of respondents to the NewVantage Partners’ survey.
Data translators will proliferate
In its much-quoted 2011 report on big data, the McKinsey Global Institute (MGI) quantified the coming shortage of data scientists (140,000 to 190,000 people with “deep analytical skills” in the U.S by 2018). Now they forecast that “millions of people” will be needed to serves as translator of the results of the work of data scientists to the rest of the organization. Similarly, Chris Brady, Mike Forde, and Simon Chadwick write in the Sloan Management Review “…it may be easier for domain experts, with deep knowledge of the business in which they are engaged and the requisite interpersonal skills, to obtain sufficient knowledge about data analysis to act as the translator for data scientists than for data scientists to gain enough knowledge about the domain, especially the language of that domain.”
Originally published on Forbes.com