Gartner: Top 10 data and analytics technology trends for 2021

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:

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.

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.”

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%.”

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.