An IDC FutureScape webinar discussed the key predictions that will impact the Big Data, business analytics, and cognitive software markets over the next one to three years.
I had the opportunity to attend a session hosted by Dan Vesset, Group Vice President, Analytics and Information Management at IDC and David Schubmehl, Research Director at IDC. I came away learning a lot about how leaders inside and outside of IT can leverage developments to inform and guide their technology implementations.
Before I highlight the predictions, it’s worth noting what’s driving them worldwide. Vesset and Schubmehl cited the rise of computer-based intelligence, shifting economics with data as digital capital, a global demand for digital workers, a platform economy, the convergence of technology and privacy, and the revolutionizing of industrial and commercial processes. In the last year, we’ve talked about almost all of these here!
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. Raw data and various value-added content will be bought and sold either via marketplaces or in bilateral transactions, and organizations will begin to develop approaches and methods for valuing their data. The analysts recommended that leaders begin developing plans for valuing data as a strategic asset, and assess interaction with all external parties to understand the value of data in relationships.
By 2018, 75 percent of enterprise and ISV development will include cognitive/AI or machine learning functionality in at least one application, including all business analytics tools. New enterprise software will necessitate the review of existing IT and business processes, and enterprise application upgrade cycles may accelerate to leverage cognitive/AI features. The analysts suggested that leaders prepare by reviewing IT providers’ cognitive/AI capabilities and/or roadmaps, and that they model the potential value of cognitive/AI functionality business outcomes.
By 2020, the dominant analytics architecture will include purpose-built, optimized solutions, one-third of which will be based on federation of non-relational and relational data. Analytics, cognitive/AI and big data architecture will become more complex, and new technology options will tempt some organizations into uncontrolled experimentation. The analysts commented that leaders should assess the decision support and automation needs of all internal and external constituents, and that they should invest in hiring and retention of top tier architects with data and analytics expertise.
By 2019, all effective IoT efforts will merge streaming analytics with machine learning trained on data lakes, marts, and content stores, accelerated by discrete or integrated processors. An increase in the use of machine learning will lower reliance on programmatic model development, and a need for at-rest and continuous analytics will require IT investments beyond data lakes. In 2017, programs should aim to increase the machine learning skills of existing developers, and adopt technology that puts data in motion in near real time.
The analysts briefly covered other predictions, including new cloud pricing models, the rise of intelligent personal assistants, and an increase in usage of advanced classification solutions. What do you think is in store for big data and business analytics this year?