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Industry Predictions: Key Trends in 2017

Please visit http://www.kdnuggets.com/2016/12/industry-predictions-key-trends-2017.html/2 for the full article

With 2017 almost upon us, KDnuggets brings you opinions from industry leaders as to what the relevant and most important 2017 key trends will be.

By Matthew Mayo, KDnuggets.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon.

In this post we present predictions from those in industry, which do not follow a prescribed question but which do address what to keep a look out for in different sectors in the upcoming year. Quotes are organized alphabetically by name of the company which they have been submitted on behalf of, and we have reserved the right to edit (extract excerpts from) for both content and length.

As the predictions come from across industry and reach into its many different niche sectors, there is no general consensus or over-arching themes herein, which makes intuitive sense. You will, however, read about the impact of GPUs, the future of the data lake, both NoSQL and SQL, IoT, machine learning, deep learning, and much more.

Submitted by Ramon Chen, CMO of Data Management Innovator, Reltio

1. AI and analytics vendor M&A activity will accelerate – There’s no doubt that there’s a massive land grab for anything AI, machine learning or deep learning. With a limited number of startups offering these integrated capabilities, the quest for relevant insights and ultimately recommended actions that can help with predictive and more efficient forecasting and decision-making will lead to even more aggressive M&A activity in 2017.

2. Data lakes will finally become useful – Many companies who took the data lake plunge in the early days have spent a significant amount of money not only buying into the promise of low cost storage and process, but a plethora of services in order to aggregate and make available significant pools of big data to be correlated and uncovered for better insights. With existing big data projects recognizing the need for a reliable data foundation, and new projects being combined into a holistic data management strategy, data lakes may finally fulfill their promise in 2017.

3. Data monetization strategies will start to mature – For enterprises to tap into the data they use to run their businesses as a potential new revenue stream, the data must be reliable, relevant, segmented, secure, anonymized, if necessary, and audited to guarantee ownership of data. Last year, Gartner highlighted that only 10% of CEOs said they monetize information assets by bartering with them or selling them outright. That number, fueled by modern data management technology, is sure to grow in 2017.

4. Cloud and data security agility will gain further importance – This is a rather obvious prediction, given the phobia of data breaches and the reticence of industries such as the financial sector to use public cloud technologies. In 2017, vendors offering Platform as a Service (PaaS) and tools themselves must also do their part in complying to Service Organization Control (SOC) types, as well as in the case of healthcare data, HITRUST (Health Information Trust Alliance), that provides an established security framework that can be used by all organizations that create, access, store or exchange sensitive and regulated data.

5. Systems of Record will have a path to Systems of Engagement, and beyond – In 2011, celebrated author Geoffrey Moore first defined the term Systems of Engagement (SoE), contrasting how Systems of Record (SoR) needed to evolve in order to focus on people, not processes. 2017 may be the year more companies finally shift to SoE, in part due to increased use and adoption of AI. Last year, Geoffrey Moore extended his thinking towards Systems of Intelligence (SoI), combining AI with the big data scale of Internet of Things (IoT). In order to achieve true SoI, companies are now pushing to accelerate SoE in the form of data-driven applications for their workers.