Not another 2017 trend predictions blog, right? Well, you’ll note that the three trends I’m focusing on aren’t so much as predictions as reality at this point. And given that these topics are almost certainly going to be relevant in 2017, I’d like to provide some perspective from a business point of view, from the SAP Database & Data management group, and from my own personal observations.
NoSQL Becomes a Feature of General Purpose Databases
As the market for it grows, leading relational and NoSQL vendors alike will move to adopt a multi-model approach to expand their addressable opportunities. While NoSQL data models have unlocked new addressable opportunities, general purpose databases that are able to address a broad range of use cases still dominate the market landscape from a revenue perspective. As evidence, leading relational database vendors have added NoSQL models to their portfolios to remain competitive; we’re also seeing similarly leading NoSQL vendors adding additional models to expand their applicability.
Today these multi-model components are mostly distinct, however, we can expect convergence and integration, at least at a management and administration level, as vendors evolve to position a unified multi-model data platform.
Smart development organizations will invest in NoSQL today to reap the benefits in coming years.
Machine Learning Extends Beyond Advanced Analytics
No doubt you’ve heard about machine learning, or the ability for computers to learn without explicitly being programmed. We’re seeing it as consumers already – your phone can already predict your text typing based on previous messages you’ve sent. How will this affect the enterprise? Majorly: it’s poised to become a core component for every data platform. As SAP’s own Chief Innovation Officer, Juergen Mueller, explained “Think about machine learning the way we think about electricity: it’s hard to imagine the world without it.” Machine learning (ML) is being driven by advances in compute power and availability of Big Data. As a result it is becoming feasible to apply ML to a wider range of data-driven applications beyond just predictive analytic algorithms. In addition to a stand-alone algorithmic platform service, ML is also being leveraged to make existing data and analytic tools more intelligent (such as smart data discovery, preparation, quality, visualization). As even more evidence, key platform vendors have added ML services to their platforms and are creating various smart data and analytic tools for organizations to use.
In 2017 more than ever, machine learning (ML) and cognitive computing will leave their indelible mark on the data platform market – ML makes things smarter, cognitive computing makes things easier to use. We can only guess at what the market will develop, but something tells me the business and consumer landscape will look much different in 10 years’ time thanks to these advances.
Data Transforms into a Tradable Asset
It’s been said before and I can’t underscore it enough – we are living in an age of a data explosion. I could throw statistics around but we all know that most companies have more data than they know what to do with, and they don’t use it to their advantages well enough (for example, by spotting trends and developing forecasts). This problem will only get worse as more data piles up.
Organizations are beginning to understand the potential commercial value of their internal proprietary data sets and will increasingly look for ways to monetize them as profitable assets. For smart organizations that have a handle on their data, they will be wise to commercialize their own internal data, while augmenting analytics with third-party external data. Data brokers, professional services firms, technology vendors, and other specialist service providers will begin to develop services to help enable this, with data marketplaces arising as a result. Organizations will also look to improve their internal analytics by augmenting them with third-party external data; vendors will look to differentiate their data and analytics platforms by distinguishing them with value-add content.
This gives an idea of what myself and my team are seeing, and how SAP, its customers, and other organizations can shape their priorities for 2017. Do you agree? What are you seeing out there? Please feel free to leave a reply below or to let me know your thoughts on Twitter or LinkedIn.