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Gartner: Top 10 data and analytics technology trends for 2021

The pandemic forced D&A leaders to step up research and analysis to respond effectively to change and uncertainty, the firm says.


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While much of the loudest buzz surrounding the impact of COVID-19 was focused on the dramatic shift from on premises to remote work, the pandemic further affected every aspect of the enterprise, which includes data and analytics technology. The uncertainty of what the tech industry would face forced D&A leadership to quickly find tools and processes—and put them in place—so they could identify key trends and prioritize to the company’s best advantage, said Rita Sallam, research vice president at Gartner, in the company’s recently released information.

Gartner has now identified 10 trends as “mission-critical investments that accelerate capabilities to anticipate, shift and respond.” It recommended that D&A leaders review these trends and consider and apply as necessary. Following is a summary from Gartner of the trends:

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Trend 1: Smarter, responsible, scalable AI

Artificial intelligence and machine learning are key factors. Businesses must apply new techniques for smarter, less data-hungry, ethically responsible and more resilient AI solutions.  When smarter, more responsible, scalable AI is applied, organizations will be able to “leverage learning algorithms and interpretable systems into shorter time to value and higher business impact,” Gartner’s report said.

Trend 2: Composable data and analytics

Composable data and analytics leverages components from multiple data, analytics and AI solutions to quickly build flexible and user-friendly intelligent applications to help D&A leaders make the correlation between the discovered insights to actions they must execute. Open, containerized analytics architectures make analytics capabilities more composable.

Public or private, data is unquestionably moving to the cloud and composable data, rendering analytics “a more agile way to build analytics applications enabled by cloud marketplaces and low-code and no-code solutions.”

Trend 3: Data fabric is the foundation

D&A leaders use data fabric to help address “higher levels of diversity, distribution, scale and complexity in their organizations’ data assets,” as a result of increased digitization and  “more emancipated” consumers.

Data fabric applies analytics in order to constantly monitor data pipelines; data fabric “uses continuous analytics of data assets to support the design, deployment and utilization of diverse data to reduce time for integration by 30%, deployment by 30% and maintenance by 70%.”

Trend 4: From big to small and wide data

Using historical data for ML and AI models was rendered irrelevant, once changes based on the pandemic had an extreme effect on business. D&A leaders need a greater variety of data for better situational awareness because human and AI decision making grows more complex and demanding.

Therefore, D&A leaders need to choose analytical techniques that can use available data more effectively and they can with more insight that now requires less data. 

“Small and wide data approaches provide robust analytics and AI, while reducing organizations’ large data set dependency,” Sallam said in a press release. “Using wide data, organizations attain a richer, more complete situational awareness or 360-degree view, enabling them to apply analytics for better decision making.”

Trend 5: XOps

DataOps, MLOps, ModelOps and PlatformOps, which comprise XOps, are necessary to achieve efficiencies and economies of scale through DevOps and using best practices of reliability, reusability and repeatability. This also reduces duplication of technology and processes and enabling automation.

Operationalization must be addressed initially and not as an afterthought because the latter is why most analytics and AI projects fail.  The report said,  “If D&A leaders operationalize at scale using XOps, they will enable the reproducibility, traceability, integrity and integrability of analytics and AI assets.”

Trend 6: Engineering decision intelligence

D&A leaders can make engineering decisions more accurate, repeatable, transparent and traceable, as decisions grow more automated and augmented. Gartner refers to “engineering decision intelligence,” which applies to a series of decisions  of business processes as well as grouped emergent decisions and consequences.

Trend 7: Data and analytics as a core business function

D&A is now making the shift into a core business function, rather than a secondary activity. D&A now is a shared business asset aligned to business results. Gartner noted that D&A silos break down because of better collaboration between central and federated D&A teams. 

Trend 8: Graph relates everything

Graphs form the foundation of most modern data and analytics capabilities and are reliant on the foundation to find relationships between people, places, things, events and locations across a wide variety of data assets. D&A leaders rely on graphs as quick answers to complex business questions, which require contextual awareness and an understanding of the nature of connections and strengths across multiple entities. 

Gartner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the organization.

Trend 9: The rise of the augmented consumer

Today, most business users use predefined dashboards and manual data exploration, but this can lead to incorrect conclusions and flawed decisions and actions. Time spent in predefined dashboards will progressively be replaced when users’ needs can be delivered  with automated, conversational, mobile and dynamically generated insights customized through a predefined dashboard.

“This will shift the analytical power to the information consumer, the augmented consumer, giving them capabilities previously only available to analysts and citizen data scientists,” Sallam said.

Trend 10: Data and analytics at the edge

Support for data, analytics and other technologies are found in edge computing environments, closer to assets in the physical world and outside IT’s purview. Gartner predicts that by 2023, over 50% of the primary responsibility of data and analytics leaders will comprise data created, managed and analyzed in edge environments.

Gartner concluded: “D&A leaders can use this trend to enable greater data management flexibility, speed, governance, and resilience. A diversity of use cases is driving the interest in edge capabilities for D&A, ranging from supporting real-time event analytics to enabling autonomous behavior of things.”

Gartner Data and Analytics Summit

Gartner analysts offer more analysis on data and analytics trends at the Gartner Data & Analytics Summits 2021, taking place virtually May 4-6 in the Americas, May 18-20 in EMEA, June 8-9 in APAC, June 23-24 in India, and July 12-13 in Japan. Follow news and updates from the conferences on Twitter using #GartnerDA.

Also see

This post was written by and was first posted to TechRepublic

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