Ed Tech Horizons

10 Emerging Trends in Analytics Every CIO Should Know About

Augmented analytics and “explainable AI” are just two changes to data and analytics technology that should have "significant disruptive potential"over operations in the next three to five years.

• By Dian Schaffhauser
• 04/18/19

Gartner's latest research into data analytics has uncovered trends the IT analyst and consulting firm said it expected to take hold in the enterprise over the next three to five years. During the recent Gartner Data & Analytics Summit, research analysts Rita Sallam and Donald Feinberg shared 10 coming changes in data and analytics technology that could have "significant disruptive potential" over operations.

Trend 1: Augmented Analytics

Augmented analytics, which uses machine learning (ML) and artificial intelligence (AI) techniques to automate multiple aspects of data work will change how analytics content is "prepared, discovered and shared," the analysts said. Those capabilities already exist to some extent in various product offerings, and as those grow in popularity, the company suggested, organizations will want to take advantage of them and gain an understanding about how augmented analytics will affect "roles, responsibilities and skills" in their workforces.

Trend 2: Augmented Data Management

This form of augmentation uses ML functionality and AI engines to automate data operations related to quality, metadata management, integration and database management system self-configuring and self-optimization. The purpose is to enable less skilled users to be able to work with the data more autonomously and to free up highly-technical people to focus on more high-value activities.

Gartner predicted that by the end of 2022, data management manual tasks will be reduced by 45 percent through the addition of ML and automated management.

Trend 3: Continuous Intelligence

Continuous intelligence is the idea of integrating real-time analytics into operations to prescribe the way forward. According to Gartner, it requires the use of augmented analytics, event stream processing, business rule management and other technologies. Calling it a "grand challenge" as well as a "grand opportunity," Sallam said achieving the continuous intelligence requires changes in how an organization's analytics and business intelligence work.

Gartner estimated that by 2022, the majority of new business systems will use continuous intelligence for real-time decision-making.

Trend 4: Explainable AI

As AI grows, so does the distrust of the algorithms driving the decisions. "Unfortunately, most of these advanced AI models are complex black boxes that are not able to explain why they reached a specific recommendation or a decision," Gartner stated. As a result, the company expected "explainable AI" to emerge in data platforms, to "auto-generate" explanations about the details of the models in use.

Trend 5: Graph Analytics

While people will be able to "model, explore and query data" across data silos with these analytics techniques to explore relationships among entities, a lack of the specialized skills required has held back adoption to date, according to Gartner. However, the company also expected to see the use of such technology to double over the next three years, "due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries."

Trend 6: Data Fabric

This is the term Gartner is using to describe "frictionless access and sharing of data" in data environments that are distributed in multiple, often-siloed sources. These data fabric designs are customized to the organization but provide a "single and consistent framework" for accessing data.

Trend 7: Conversational Analytics

By 2020, Gartner reported, half of queries will be generated via search, natural language processing (NLP) or voice — or are expected to be generated automatically. This trend is being driven by the need to make analytics accessible to more people in the organization and will allow users to use analytics tools as easily as they speak with virtual assistants now.

Trend 8: Commercial AI and ML

Open source for AI and ML is out; commercial products are in. Gartner expected that by 2022, three-quarters of new end-user systems integrating AI and ML will be produced with commercial offerings, as people are drawn to the enterprise features currently lacking in open source technologies. The process will be helped along by the connectors being built by companies to help their customers access data in their open source systems.

Trend 9: Blockchain

This form of distributed ledger technology will take four or five years to gain major pick-up in providing the "decentralized trust" needed for a network of "untrusted participants" to work together. Gartner noted that "blockchains are a data source, not a database," and therefore won't replace "existing data management technologies."

Trend 10: Persistent Memory Servers

Persistent-memory, which resides between DRAM and NAND flash memory in terms of price and performance, will provide more cost-effective memory for high-performance workloads, Gartner explained, and holds the potential to boost "application performance, availability, boot times, clustering methods and security practices," while also "keeping costs under control." This will help data centers trying to keep up with new server workloads that demand faster CPUs alongside "massive memory and faster storage."

"The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers," said Sallam, in a statement. "It's critical to gain a deeper understanding of the technology trends fueling that evolving story and prioritize them based on business value."

"The size, complexity, distributed nature of data, speed of action and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down," added Feinberg. "The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change."