Big data analytics is the next trillion-dollar market, says Michael Dell. IDC has a more modest and specific prediction, forecasting the market for big data technology and services to grow at a 23.1% compound annual growth rate, reaching $48.6 billion in 2019.
The larger market for business analytics software and business intelligence solutions which now includes the new disciplines of data science and cognitive computing (e.g., IBM Watson) is at least 5 times bigger. But a much larger market, which may indeed approach a trillion dollar sometime in the not-distance future, includes the revenues companies in any industry will generate from “monetizing” their data and algorithms.
Here’s my summary of predictions for big data analytics and cognitive computing from the International Institute for Analytics (IIA), Forrester, IDC, and Gartner.
Big data analytics will be embedded everywhere
IIA predicts that computing will become increasingly microservice-enabled, where everything – including analytics – will be connected via an API. IDC predicts that by 2020, 50% of all business analytics software will include prescriptive analytics built on cognitive computing functionality and that Cognitive Services will be embedded in new apps. Embedded data analytics will provide U.S. enterprises $60+ billion in annual savings by 2020.
Goodbye data preparation, hello data science
IIA predicts that automated data curation and management will free up analysts and data scientists to do more of the work they want to do. Forrester says that in 2016, machine learning will begin to replace manual data wrangling and data governance dirty work, and vendors will market these solutions as a way to make data ingestion, preparation, and discovery quicker. Through 2020, according to IDC, spending on self-service visual discovery and data preparation tools will grow 2.5x faster than traditional IT-controlled tools for similar functionality.
The meager supply of people with the right data analysis skills will continue to baffle experts
Automated data preparation will help address the limited supply of analysts and data scientists. However, opinions differ regarding when supply will start meeting demand. The talent crunch, says IIA, will ease as many new university programs come online and it will stop being a challenge for large corporations—they will find ways to address their requirements for number-crunching, model-spewing staff.
No, says IDC, the shortage of skilled staff will persist and extend from data scientists to architects and experts in data management. As a result, the market for big data professional services will expand rapidly, with a CAGR of 23% through 2020. Forrester agrees that the “huge demand” will not be met in the short term, “even as more degree programs launch globally.” In 2016, Forrester predicts, firms will turn to insights-as-a-service providers and data science- as-a-service firms and to labor-savings options such as algorithm markets and self-service advanced analytics tools.
There’s risk in them thar data hills
Gartner predicts that due to the volume and variety of data and the sophistication of advanced analytics capabilities, the risks associated with big data analytics projects will continue to be larger than those associated with typical IT projects. In addition, by 2018, 50% of business ethics violations will occur through improper use of big data analytics, according to Gartner. Forrester highlights some of the risks associated with the ever-changing big data vendor hype, predicting that half of all “big data lake” investments will stagnate or be redirected. Forrester also warns that immature data science teams will improperly exploit algorithm markets, and spend precious time either developing an algorithm they could have bought or trying to apply an algorithm incorrectly.
We will have a new buzzword
Cognitive technology will become the follow-on to automated analytics, predicts IIA. For many enterprises, the association between cognitive computing and analytics will solidify in much the same way that businesses now see similarities between analytics and big data. IIA adds to the mix yet another term, predicting also that data science and predictive/prescriptive analytics will become one and the same.
How about going back to “data mining”?
Data monetization will take off
By 2020, IDC predicts, data monetization efforts will result in enterprises increasing the marketplace’s consumption of their own data by 100-fold or more. Also by 2020, the amount of data that is worth analyzing will double. Forrester predicts that as firms will try to sell their data, “many will sputter.” In 2016, an increasing number of firms will look to drive value and revenue from their “data exhaust.” Only 10% of enterprises took their data to market in 2014, but 30% reported data commercialization efforts in 2015, a 200% increase.
Forrester declares that “all companies are in the data business now.” IDC predicts that by 2020, organizations able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers. A similar figure for revenues associated with data monetization will get us closer to Michael Dell’s trillion-dollar prediction. In the same interview, Dell described the current state of data mining/predictive analytics/data science/prescriptive analytics/cognitive computing: “If you look at companies today, most of them are not very good at using the data they have to make better decisions in real time.”
2016 analytics priorities and predictions webinar
2016 analytics priorities and predictions research brief
Predictions 2016: The Path From Data To Action For Marketers
IDC On-Demand Webcasts: Worldwide Big Data and Analytics 2016 Predictions
New IDC Forecast Sees Worldwide Big Data Technology and Services Market Growing to $48.6 Billion in 2019, Driven by Wide Adoption Across Industries
Gartner Says Customer Data Has Monetary Value but Many Organizations Ignore It
Gartner Says, By 2018, Half of Business Ethics Violations Will Occur Through Improper Use of Big Data Analytics
Originally posted on Forbes.com