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  /  Analytics   /  Top 6 Data and Analytics Trends by Gartner to Watch Out in 2021
Data and analytics

Top 6 Data and Analytics Trends by Gartner to Watch Out in 2021

The top data and analytics (D&A) technology trends for 2021, according to Gartner, will help organizations deal with changes, complexity, and possibilities this year.

Enterprises produce a wealth of data that provides useful insights, and data analytics is the key to unlocking them. Data analytics can assist a company in a variety of ways; from modifying marketing pitch for a specific client to detecting and reducing operational risks.

The top data and analytics technology trends for 2021, according to Gartner, will help organizations deal with changes, complexity, and possibilities this year.

Here are the top 6 data and analytics trends by Gartner to watch out in 2021 –


Smarter, Responsible, and more Scalable AI 

Because of the increased influence of artificial intelligence (AI) and machine learning (ML), companies must employ new techniques to develop AI solutions that are smarter, least data intensive, ethically compliant, and more robust. Organizations can use learning algorithms and comprehensible systems to achieve faster time to value and greater business effect by applying smarter, more responsible, and scalable AI.

Information Week mentioned, Gartner is forecasting that 75% of enterprises will shift from piloting to operationalizing AI by the end of 2024, driving a 5x increase in streaming data and analytics infrastructures. There are challenges with current approaches. Pre-covid models based on large amounts of historical data may no longer be valid.


Composable Data and Analytics 

The aim of composable data and analytics is to combine elements from a variety of data, analytics, and AI solutions to create a versatile, convenient, and accessible platform that enables leaders to relate data insights to corporate behavior. According to Gartner client inquiries, most large corporations have several “enterprise standard” analytics and business analytics resources.

Creating new apps from each company’s bundled business capabilities improves flexibility and reliability. Composable data and analytics will not only promote cooperation and improve the firm’s analytics capabilities, but it will also expand access to analytics.


The Foundation is Data Fabric

D&A leaders are rapidly using data fabric to handle greater levels of diversity, delivery, size, and sophistication in their organizations’ data assets as a consequence of enhanced digitization and more liberated customers.

The data fabric employs analytics to keep track of data pipelines in real time. A data fabric uses continuous analytics of information assets to enable the design, implementation, and usage of diverse data, cutting integration time by 30%, deployment time by 30%, and maintenance time by 70%.


The aim of XOps, which includes DataOps, MLOps, ModelOps, and PlatformOps, is to gain benefits of economies of scale while ensuring reliability, recyclability, and repeatability using DevOps best practices. Simultaneously, it eliminates replication of infrastructure and procedures while allowing for automation.

Since operationalization is only tackled as reconsideration in most analytics and AI ventures, they collapse. The reproducibility, traceability, authenticity, and configurability of analytics and AI assets will be enabled if D&A leaders use XOps to incorporate at scale.


Developing Decision Intelligence

Engineering decision intelligence means using systematic decision-making practices to organize individual decisions and series of decisions into enterprise applications and even channels of evolving decisions and repercussions.

Engineering decisions, according to Gartner, enable D&A leaders to make choices that are more reliable, reproducible, clear, and traceable as decisions become highly automated and augmented.


Data and analytics at the very Edge 

More and more data analytics tools are getting closer to physical assets as they move outside of conventional data center and cloud environments. For data-centric solutions, this decreases or removes lag, allowing for more synchronous value. Through moving data and analytics to the edge, data teams will be able to broaden their skills and effect across the entire organization. It can also help in cases where data cannot be excluded from particular geographical areas due to legitimate or administrative constraints.