These trends can help organizations respond to the changes, uncertainties and opportunities of the current scenario.
In an organizational environment increasingly based on data analytics, finding practical ways to drive information use has gained the attention of business leaders. Against this backdrop, Gartner, Inc., the world leader in business research and advice, highlights the top 10 trends in business technologies. Data & Analytics (D&A) for 2021, with opportunities that can help organizations respond to the changes, uncertainties and opportunities present in the current scenario.
"The speed with which the COVID-19 pandemic disrupted the performance of organizations forced Data & Analytics leaders to look for tools and processes to identify key technology trends and prioritize those with the greatest potential to impact their competitive advantage." says Rita Sallam, Vice President of Research at Gartner.
To do so, the analyst suggests that D&A leaders should evaluate these 10 trends below as specific investments to accelerate their companies' capabilities to anticipate, modify and act on the events in their day-to-day operations.
Smarter, more responsible and scalable Artificial Intelligence – For maximum impact from Artificial Intelligence (AI) and Machine Learning (or ML for Machine Learning) companies are being forced to implement more “intelligent” Artificial Intelligence solutions, with less data consumption, in addition to being ethically responsible and resilient. By relying on these tools, organizations will leverage learning algorithms and interpretable systems to reduce time to value and increase their business relevance.
Compositions of Data & Analytics – Open and containerized analytics architectures make analytics more composable, or composable. Composite D&A solutions leverage multiple data components, analytics, and Artificial Intelligence solutions to quickly build smart, flexible, and easy-to-use applications that help leaders connect the value of insights gained with data to their strategic planning.
With the data center of gravity shifting to the Cloud, Data & Analytics composites will become a more agile way to develop analytics-enabled applications for the Cloud and low-code or no-code solutions markets.
Data Fabric is the basis – With increasing digitization and more independent consumers, D&A leaders are increasingly using Data Fabric to help them address higher levels of diversity, distribution, scale and complexity in their organizations' data assets.
The data mesh uses analytics to constantly monitor data sources. A Data Fabric does ongoing analysis of data assets to support the design, implementation and utilization of diverse data to reduce integration time in 30%, development in 30% and maintenance in 70%.
From Big Data to Small and Comprehensive Data – The extreme changes that have taken place in the business due to the health emergency have made ML and Artificial Intelligence models based on large amounts of historical data less relevant. At the same time, human decision-making based on Artificial Intelligence is more complex and demanding – which ends up calling on D&A leaders to have a greater variety of data to obtain a better awareness of the situation experienced.
As a result, D&A leaders must choose analytical techniques that enable them to use available data more efficiently, whether by relying on ample data that allows for analysis and synergy from a variety of small and large, unstructured and structured data sources, as well as specific data that enable the application of analytic techniques that require less data but still provide useful insights.
“Small and large data approaches provide robust analytics while reducing organizations' reliance on large data sets,” says Gartner analyst. “Using broad data, companies gain richer, more complete situational awareness or a 360-degree view, enabling more assertive decision-making.”
XOps - The goal of XOps – which includes DataOps, MLOps, ModelOps and PlatformOps – is to achieve scalable efficiencies and savings using DevOps best practices and ensure reliability, reuse, and repeatability. At the same time, this tool reduces the duplication of technologies and processes, in addition to enabling automation.
Most analytics and Artificial Intelligence projects fail because the operation is just treated as an afterthought. If D&A leaders operate at scale with the help of XOps, they will enable the reproducibility, traceability, integrity and completeness of analytics and Artificial Intelligence assets.
Engineering Decision Intelligence - Engineering decision intelligence applies not just to individual decisions, but to sequences of decisions, grouping them into business processes and even networks of emerging decisions and consequences. As decisions become increasingly automated and augmented, engineering decisions give D&A leaders the opportunity to make more accurate, repeatable, transparent, and traceable decisions.
Data & Analytics at the heart of business functions – Instead of remaining a sideline, D&A is moving to a core business function. In this situation, Data & Analytics capabilities become business assets that must be shared and aligned with business results, enabling greater collaboration across teams.
The chart lists everything – Graphs form the basis for many modern data and analytics capabilities because with them you can find the relationship between people, places, things, events, and places in various data assets. D&A leaders rely on charts to quickly answer complex business questions that require situational awareness and an understanding of the nature of connections and strengths across diverse entities.
Gartner predicts that by 2025, graphics technologies, with highly customizable Dashboards, will be used in 80% of D&A technologies, compared to 10% expected for 2021, which will facilitate quick decision-making.
The rise of the increased customer – Most business customers use predefined dashboards and manual data mining, which can lead to incomplete conclusions and wrong decisions. Time spent on pre-defined dashboards will progressively be replaced by dynamic, automated, conversational, insights tailored to users' needs and delivered at their point of consumption.
“This will change the analytical power of the information consumer – or extended consumer – by giving them capabilities previously available only to analysts and data scientists,” explains Sallam.
Data & Analytics in Edge Computing – Data, analytics and other technologies that support them increasingly reside in Edge Computing (or Edge Computing) environments, so they are closer to assets in the physical world and beyond the reach of IT. Gartner's expectation is that, by 2023, more than 50% of primary responsibility for D&A leaders will include data created, managed, and analyzed .in edge environments.
Leaders can use this trend to enable greater flexibility, speed, governance and resiliency in data management. A variety of use cases are generating interest in D&A edge capabilities, ranging from supporting real-time event analysis to enabling the autonomous behavior of “things”.
