Data analytics is a discipline focused on extracting insights from data, including the analysis, collection, organisation, and storage of data, as well as the tools and techniques used to do so. Gartner has identified the top 10 data and analytics (D&A) technology trends for 2021 that can help organisations respond to change, uncertainty, and the opportunities they bring in the next year.
10: Data and Analytics at the Edge
More data analytics technologies are beginning to live outside of the traditional data center and cloud environments. Edge computing is computing that’s done at or near the source of the data, instead of relying on the cloud at one of a dozen data centers to do all the work. Shifting data and analytics to the edge will open opportunities for data teams to scale capabilities and extend impact into different parts of the business.
09: The Rise of the Augmented Consumer
The augmented consumer refers to business users who leverage powerful automated, contextual, mobile and natural language capabilities as part of their analytics workflow.
Business users have been restricted to using predefined dashboards and manual data exploration, which can lead to incorrect conclusions and flawed decisions and actions. Gartner believes these will be replaced with automated, conversational, mobile, and dynamically generated insights customised to a user’s needs and delivered to their point of consumption.
08: Graph technologies
Data visualisation gives us a clear idea of what information means by giving it visual context through maps or graphs. Graphs can allow researchers to find relationships between people, places, things, events and locations across diverse data asset. 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 organisation.
07: Data and Analytics as a Core Business Function
Data and analytics is shifting to a core business function. As organisations demand more data analytics capabilities to compete, more businesses are funding these efforts. Two-thirds of senior executives plan to increase investment in automation and AI as the COVID-19 recovery phase advances, according to a report from McKinsey.
06: Engineering Decision Intelligence
Decision intelligence contains a range of decision-making methods to design, model, align, execute, and track decision models and processes. The implementation offers a structure for organisational decision-making and processes with the integration of ML algorithms.
Engineering decision intelligence not only applies to individual decisions, but sequences of decisions, grouping them into business processes and even networks of emergent decisions and consequences. 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.
The goal of XOps, which includes DataOps, MLOps, ModelOps, and PlatformOps, is to create an enterprise technology stack that enables automation and reduces the duplication of technology and processes. In simple terms it is the natural evolution of DataOps in a workplace, and across the enterprise, to enable AI and Machine Learning (ML) workflows.
According to Gartner if D&A leaders operationalise at scale using XOps, they will enable the reproducibility, traceability, integrity, and integrability of analytics and AI assets.
04: From Big to Small and Wide Data
Big Data is about sheer size, and gathers a large amount of data in specific domains. The concept of ‘Small and Wide Data’ is about diverse inputs, taking disparate sources and learning from them and their correlations without necessarily requiring the brute force of size.
“Small and wide data approaches provide robust analytics and AI, while reducing organisations’ large data set dependency,” said Rita Sallam, distinguished research vice president at Gartner. “Using wide data, organisations attain a richer, more complete situational awareness or 360-degree view, enabling them to apply analytics for better decision making.”
03: Data Fabric
Data fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning on-premises and multiple cloud environments. Data fabric simplifies and integrates data management across cloud and on premises to accelerate digital transformation. With increased digitisation and more emancipated consumers, D&A leaders are increasingly using data fabric to help address higher levels of diversity, distribution, scale and complexity in their organisations’ data assets.
02: Composable Data and Analytics
Composable data and analytics leverages components from multiple data, analytics and AI solutions to rapidly build flexible and user-friendly intelligent applications that help D&A leaders connect insights to actions. With the center of data gravity moving to the cloud, 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.
01: Smarter, Responsible, Scalable AI
Data and analytics leaders know that data and platform capabilities and the correct application of data and AI skills deliver successful AI applications. However, Gartner found that the majority of organisations miss the critical collaboration required across data management and AI disciplines when organising these roles.
By deploying smarter, more responsible, scalable AI, organisations will leverage learning algorithms and interpretable systems into shorter time to value and higher business impact. These AI systems must also protect privacy, comply with federal regulations and minimise bias to support an ethical AI.