Use of AI and automation together an analytics trend in '22

Operationalizing AI and automation

But rather than merely adopt AI capabilities such as natural language processing (NLP) and automated machine learning (AutoML) -- which many vendors have been developing in recent years -- this year is expected to be when organizations actually operationalize AI capabilities to take data-driven action.

That means deploying capabilities that combine AI capabilities with process automation.

"Organizations know that they're competing for data supremacy," Wang said. "It's about going from data to decisions and winning on data velocity. We make a decision per second, but it takes a week, a month, a year for decisions to get out of a management committee.

"Machines are making a hundred or a thousand decisions per second, and that real-time input -- that real-time insight -- is important," he continued.

Wang added that to get that real-time insight, organizations need to automate processes such as data capture and data management while using AI and machine learning to capture and contextualize events like interactions between employees and customers or suppliers and partners.

"We see analytics AI and automation powering the future," Wang said. "Ask the right business questions, automate the data capture, automate the next best action -- a suggestion -- and use AI to build the back end. That is powerful. That is building the long-term future."

Similarly, Gualtieri said he sees the combined use of AI and automation as an analytics trend this year.

We see analytics AI and automation powering the future. Ask the right business questions, automate the data capture, automate the next best action -- a suggestion -- and use AI to build the back end. That is powerful. That is building the long-term future.

And enterprises are employing that synergy between the technologies not only to enable business users to more easily interact with and analyze data to come up with insights, but also to deliver automatically generated insights directly to those business users at the moment they need them.

Analytics vendors have been developing and enhancing NLP and AutoML capabilities for years, but enterprises have been relatively slow to adopt them.

In a 2021 survey conducted by Dresner Advisory Services, only about a quarter of respondents said they were using natural language capabilities to enable analytics. Natural language analytics ranked 32nd out of 41 business intelligence-related technologies.

Meanwhile, a report from Prescient & Strategic Intelligence showed the global AutoML market is a fraction of what it will be in 2030, with a market size of $346.2 million as of 2020 and expected size of $14.8 billion in 2030.

NLP and AutoML reduce barriers to analytics by eliminating the need to know and write code, but they still force end users to explore data, train models and develop their own insights.

Process automation combined with AI, however, delivers insights.

MicroStrategy, with an aim of enabling intelligence everywhere, is one vendor focused on delivering insights. Qlik, whose strategy is to enable active intelligence, is another.

"There's going to be continued use of AI and automation," Gualtieri said. "Now, where will AI be used in automation? Within decisions. I'm looking forward to accelerated use of AI, and for transforming digital business processes."

Regarding the timing of combining automation and AI being an analytics trend, Bange said 2022 will be the year because organizations have been experimenting with the capabilities and they're ready to put into action what they've developed.

"The change for many companies is they're saying, 'Playtime is over,'" he said. "They experimented a lot, tried pilots, but now it's about operationalizing it, bringing it into production to generate real business value. That is a huge challenge, and an underrated change."

More analytics trends

Beyond combining automation and AI to generate insights, a few underrated analytics trends also will be important in 2022.

In particular, the emphasis on data quality and data governance.

The two concepts and their enabling technologies are not as groundbreaking as automation and AI, but they're equally important. In fact, without good data quality and a strong data governance framework, insights generated with automation and AI could be completely untrustworthy.

"Nobody wants to talk about data quality, but it's still the number one problem for everyone working with data," Bange said. "That time is running out, and maybe in 2022 or next year is the last chance for many companies to get data quality right. If they don't get the foundation right, they will start to lose their competitiveness."

Data governance is closely tied to data quality in forming a foundation for analytics, and their importance remains a significant analytics trend, according to Aslett.

"Data governance is an enabler of improved and accelerated data analytics," he said. "Data was previously under-governed, and better data governance can be the foundation for delivering the right data to the right people at the right time and actually accelerate projects moving forward."