What’s coming next in data analytics?
There’s a whole lot of data out there. New analytics tools will help your customers use at least some of that data to make better business decisions.
What’s coming? The market watchers at Gartner believe they know. Speaking at a recent conference in Australia, they presented their top 10 data and analytics tech trends.
“The story of data and analytics keeps evolving,” says Gartner researcher Rita Sallam.
She’s right. Here’s your tech provider’s summary:
Augmented analytics: “Augmented” here means analytics bolstered with machine learning and other artificial intelligence techniques. Gartner believes augmented represents the next wave of analytics, and that it will transform how analytics content is developed, consumed and shared.
Augmented data management: More machine learning, this time with the goal of producing self-configuring, self-tuning data-management systems. Gartner predicts nearly half (45%) of today’s manual data-management tasks will be automated by 2022, freeing up data specialists to focus on higher-value tasks.
Continuous intelligence: This means using real-time contextual data to help improve decisions. Essentially, analytics get integrated within a business operation, so that they can process current and historical data and then prescribe real-time responses to new events.
Explainable AI: Yes, artificial intelligence can recommend actions. But increasingly, organizations want justification. How did the AI system arrive at its decision? And did it use logic that was in any way biased? To help address these and other related concerns, future AI models will be designed for much easier interpretation and explanation.
Graph analytics: This is a set of techniques to explore the relationships between organizations, people and transactions. As companies ask increasingly complex questions about increasingly complex data, Gartner expects the growth of this approach to double annually through 2022.
Data fabric: This enables frictionless access and data-sharing in a distributed-data environment. It also enables a company to run a single, consistent data-management framework.
Conversational analytics: Gartner predicts that by 2020, fully half of all analytics queries will be made with search, natural language processing (NLP) or voice. In this way, analytics should become accessible to nearly everyone in the organization, also driving much broader adoption.
Commercial AI and machine learning: Commercial systems, rather than open source platforms, will dominate new end-user analytics by 2022, Gartner predicts. Still, these commercial solutions will include built-in connectors to the open-source ecosystem.
Blockchain: This ledger technology isn’t just for cryptocurrencies. It can also be used for analytics, creating what Gartner calls “decentralized trust.” But this could still be several years off. Meantime, the cost of integration may outweigh potential benefits.
Persistent memory servers: Persistent is a new tier of memory, located between DRAM and NAND, that provides affordable memory for high-performance workloads. For analytics applications, it could also help lower costs while improving application performance, boot times, clustering and security.
Analytics are certainly evolving. Now you know in which direction.