Gartner: 10 changes coming to data analytics

Businesses that trust AI to operate will leverage different kinds of data input and infuse automation into how they extract insights.


The year began with an ambitious data mandate for organizations: leverage data analytics and AI techniques to keep up with the competition and increase efficiency.

Pressed by the challenges of a redrawn business landscape, leaders searched for guidance in their data and analytics toolkit. In the pivot to distributed work, AI helped field rising help desk requests from a mobile workforce. Data analytics informed leaders in near-real time how consumption patterns shifted, helping manage supply chain constraints. Radical change and uncertainty now challenges organizations to forge new paths within their data and analytics strategies, said Rita Sallam, distinguished VP analyst at Gartner speaking at a Gartner IT Symposium/Xpo Americas session last week.

"It's not just to make it to the other side, but to thrive when we get there," Sallam said. Organizations emerging from the initial reactive phase will look to their data analytics initiatives to enable a smarter company, one that automates insight generation and finds ways to monetize its data inventory.  Diverse types of data — such as audio and video — will come under the magnifying glass to equip companies with richer insight into their operations.

Here are 10 trends set to shape businesses' data strategies:

1. Smarter, faster, more responsible AI Initially groundbreaking and available to a select few, access to AI techniques has expanded across company departments. More enterprises will operationalize their AI initiatives by 2024, according to Gartner. 

But "if users don't trust data, if they can't understand how a model works, the more complex it gets, the more opaque it typically gets, the less likely they are to trust that model and use it," said Sallam.

Organizations need to arrive at AI strategies that augment employees in their daily work and decisions. AI that's smarter, faster and more responsible powers the applications technology that can help cities manage traffic flow, assist doctors in diagnosing illnesses or applying algorithms in near-real time to manage time-sensitive tasks in the financial market. 

Still, challenges lie ahead. Data sets may no longer be accurate at predicting outcomes amid disruption induced by COVID-19.

2. Decline of the dashboard Dashboards are a ubiquitous tool in platforms that promise AI-driven insights. A quickly adjustable tool to let analysts and middle management arrive at conclusions from available data sets.

"The predefined dashboard with predefined KPIs, predefined relationships, is likely to be displaced," Sallam said.

In its stead, platforms will combine techniques such as augmented analytics, natural language processing and anomaly detection to keep users from performing tasks associated with analysis.

But Gartner projects that by 2025, data stories will be the most widespread way of consuming analytics, with 75% of those stories automatically generated through augmented analytics techniques. 

Get tech news like this in your inbox daily. Subscribe to CIO Dive: Email: Sign up

3. Decision intelligence By 2023, Gartner projects more than one-third of large organizations will have analysts practicing decision intelligence, including decision modeling.

Decision intelligence includes a range of decision-making techniques such as rules-based approaches to AI and machine learning in order to fine-tune decision-making, Sallam said.

Though current adoption remains low, these techniques are used by financial services companies to assist in deciding mortgage applications. 

Enterprise adoption will increase, with business leaders lured by the reduction in time and effort it delivers to the design, development and deployment of complicated business logic.

4. X analytics The types of data points that companies use to make decisions will expand, with organizations reaching into video, audio, olfactory, vibration, natural language, sentiment or emotion data to derive actionable insights. 

In the term "X analytics," X works as a variable that can be replaced with any type of content. "Over the past 10 years, with the rise of big data, we've done a great job at storing and managing content, or X data," said Sallam. "What we haven't done is a great job at using that pervasively across the organization."

By 2025, AI for video, audio, vibration, text, emotion and other content analytics will trigger significant innovations and transformations at more than three-quarters of Fortune 500 companies, according to Gartner.