Efficiency and refinement define what’s next for D&A.
Gartner has released a report with its top 10 data and analytics (D&A) technology trends for 2021.
Business insights are rapidly becoming an integral part of any organisation’s operations. A holistic look at these trends shows that the market is now moving toward refining the use of technologies and behaviours to improve efficiencies and gain higher-value results.
Distinguished research vice president Rita Sallam said, “The speed at which the COVID-19 pandemic disrupted organisations has forced D&A leaders to have tools and processes in place to identify key technology trends and prioritise those with the biggest potential impact on their competitive advantage.”
Trend 1: AI – refined
Machine learning has become a ubiquitous technology in almost every area of IT.
Organisations should now apply new techniques for smarter, less data-hungry, ethically responsible and more resilient AI solutions to gain shorter time to value and higher business impact.
Trend 2: Composable D&A
The increasing demands of D&A solutions and the trend toward cloud means a need to more efficiently use infrastructure.
Having open, containerised analytics architectures make analytics capabilities more composable – leveraging components from multiple solutions to build flexible and user-friendly intelligent applications that help connect insights to actions.
Composable data and analytics will become a more agile way to build analytics applications enabled by cloud marketplaces and low-code and no-code solutions.
Trend 3: Fabric is the foundation
A data fabric uses analytics to constantly monitor data pipelines, utilising continuous analytics of data assets to support the design, deployment and utilisation of diverse data.
It can reduce time for integration by 30 percent, deployment by 30 percent and maintenance by 70 percent.
Trend 4: Small and wide data
According to Gartner, big data is becoming less relevant as analytical techniques become more advanced, even as the demands on insights are rising.
Wide data enables the analysis and synergy of a variety of small and large, unstructured and structured data sources. Small data is the application of analytical techniques that require less data but still offer useful insights.
“Small and wide data approaches provide robust analytics and AI, while reducing organisations’ large data set dependency. Using wide data, organisations attain a richer, more complete situational awareness or 360-degree view, enabling them to apply analytics for better decision making,” said Sallam.
Trend 5: XOps
XOps, including DataOps, MLOps, ModelOps, and PlatformOps, is about using DevOps best practices across various operations functions.
Most analytics and AI projects fail because operationalisation is only addressed as an afterthought, Gartner explained. If D&A leaders operationalise at scale using XOps, they will enable the reproducibility, traceability, integrity and integrability of analytics and AI assets.
Trend 6: Engineering decision intelligence
Engineering decision intelligence is applying deliberate decision making practices to both individual decisions and sequences of decisions, grouping them into business processes and even networks of emergent decisions and consequences.
Gartner pointed out that as decisions become increasingly automated and augmented, engineering decisions give the opportunity for D&A leaders to make decisions more accurate, repeatable, transparent and traceable.
Trend 7: D&A as business DNA
D&A is shifting to a core business function rather than a secondary activity.
As it becomes a shared business asset aligned to business results, this will break down D&A silos due to better collaboration between central and federated D&A teams.
Trend 8: Graphs
D&A leaders rely on graphs to quickly answer complex business questions. Gartner has predicted that by 2025, graph technologies will be used in 80 percent of data and analytics solutions, up from 10 percent in 2021.
Trend 9: Augmented consumers
Predefined dashboards and manual data exploration will progressively be replaced with automated, conversational, mobile, and dynamically generated insights customised to a user’s needs and delivered to their point of consumption.
“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,” said Sallam.
Trend 10: At the edge
Gartner has predicted that by 2023, over 50 percent of the data that makes up the primary responsibility of data and analytics leaders will be created, managed, and analysed in edge environments.
This is driven by a range of use cases, including supporting real-time event analytics and enabling the autonomous behaviour of ‘things’.