Summary Bullets:

• In 2017 the enterprise data and analytics vendor community emphasized opportunity in the cloud and the democratization of data. What will 2018 bring?

• We expect to see a shift in focus towards quality, to solving problems such as data governance, and putting AI to work within tactical business workflows.

What does the coming year have in store for the enterprise data and analytics marketplace? Sometimes, the best way to predict the future is to look at the past. To that end, here’s what we predicted for 2017 back in December of last year.

• IoT success will ride on pre-built data models and packaged software
• Smaller players will drive cognitive software innovation
• Vendors will prioritize self-service data integration, prep and management
• Vertical markets and specific use cases will fuel data-as-a-service adoption

For the most part (aside from predicting that AI would come from smaller vendors), the year played out as anticipated with a distinct emphasis on direct business outcomes and the broad adoption of analytics among business users. How will these trends move forward? In short, we don’t expect to see grand speculation and rabid investment in unproven ideas. Yes, we’re looking at you blockchain!
Of course we should expect continued interest in cloud readiness and hybridization as a key differentiator. And we will continue to see artificial intelligence (AI) cut a broad swath across a wide range of use cases. However, in tackling those opportunities, we believe vendor objectives and methodologies will shift from driving new business models to improving existing operations and business workflows. Here’s a quick rundown of how that mentality will find its way into everyday technology offerings.

• All data and analytics technology providers will productize and operationalize several key AI functions within user-facing analytics solutions such as guided workflows and recommendations.
• Similarly, enterprise buyers will see an influx of AI-informed line of business solutions that feature pre-built, trained and refined data models.
• Platform providers will seek to provide as many choices as possible to customers looking for a particular database, data processing engine or development library.
• Data visualization and discovery vendors will prioritize the governance of data used by a broad swath of business users in a more egalitarian manner.
• Data management platforms and tools will prioritize shared data artifacts, allowing for a true 360 degree view of data for both IT and business users.

How should enterprise buyers respond to this shift in emphasis during the coming year? First and foremost, buyers should take advantage of the recent influx of hybrid options for real-time data feeds and queries, but they should not be content with interoperability alone. Moving on-premises software to the cloud is no longer enough; vendors must create a fluid and permeable connection between public and private cloud resources that extends to the purchasing, provisioning, and management of those systems. Second, enterprise buyers should approach the issue of data security and management cautiously when selecting a pure-play analytics offering. Vendors are inconsistent in their approach and level of maturity in supporting data preparation requirements, for example. And lastly, with the oncoming platformization of analytics, where everything is accessible via API and publishable as a microservice, enterprise buyers should begin thinking about analytics, not as a passive, informational adjunct to business processes, but instead as an active participant in the business itself. This is not about minimizing the number of applications a user has to pivot between. It is instead about blending those applications. It is about combining action with analysis.