The top 10 data and analytics technology trends for 2021. These trends can help organizations respond to change, uncertainty, and the opportunities they bring in the next year. Data & Analytics leaders should use the following ten trends as mission-critical investments. This will also expedite their capabilities to anticipate, shift and respond.
Trend 1: Smarter, Responsible, Scalable AI
The greater influence of artificial intelligence (AI) and machine learning (ML) requires institutions to apply new techniques for smarter, less data-hungry, ethically responsible, and more resilient AI solutions. By expanding smarter, more responsible, scalable AI, organizations will leverage learning algorithms and interpretable systems into shorter time to value and higher business impact. This must be one of the most adopted data and analytics technology trends.
Trend 2: Composable Data and Analytics
Open, containerized analytics structures make analytics capabilities more composable. Composable data and analytics support components from multiple data, analytics, and AI solutions. This rapidly build flexible and user-friendly intelligent applications that help D&A managers connect insights to actions.
With the data gravity moving to cloud, composable data and analytics will become a flexible way to build analytics applications. Moreover, the cherry to the cake will be analytics applications enabled by cloud marketplaces and low-code and no-code solutions.
Trend 3: Data Fabric Is the Foundation
With boosted digitization and more emancipated buyers, D&A leaders are increasingly using data fabric. This also helps address higher levels of diversity, distribution, scale, and complexity in their institutions’ data assets.
The data fabric uses analytics to monitor data pipelines constantly. A data fabric utilizes continuous analytics of data assets to maintain the design, deployment, and utilization of diverse data. This also reduces the time for integration by 30%, deployment by 30%, and maintenance by 70%.
Trend 4: From Big to Small and Wide Data
The extreme business changes from the COVID-19 pandemic caused ML and AI models based on large amounts of historical data to become less relevant. At the same time, decision-making by humans and AI are more complex and demanding, requiring D&A leaders to have a greater variety of data for better situational awareness.
As a result, D&A leaders should choose analytical techniques that can use available data more effectively. D&A leaders rely on wide data that enables the analysis and synergy of a variety of small and large, unstructured, and structured data sources, as well as small data which 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 organizations’ large data set dependency. Using wide data, organizations attain a richer, more complete situational awareness or 360-degree view, enabling them to apply analytics for better decision making.
Trend 5: XOps
The goal of XOps, including DataOps, MLOps, ModelOps, and PlatformOps, is to achieve efficiencies and economies of scale using DevOps best practices and ensure reliability, reusability, and repeatability. At the same time, it reduces duplication of technology and processes and enabling automation.
Most analytics and AI projects fail because operationalization is only addressed as an afterthought. If D&A leaders operationalize 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 applies to not just 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 allow D&A leaders to make decisions more accurate, repeatable, transparent, and traceable.
Trend 7: Data and Analytics as a Core Business Function
Instead of being a secondary activity, D&A is shifting to a core business function. In this situation, D&A becomes a shared business asset aligned to business results. Also, D&A silos break down because of better collaboration between central and federated D&A teams.
Trend 8: Graph Relates Everything
Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across diverse data assets. D&A leaders rely on graphs to quickly answer complex business questions. This also requires contextual awareness and an understanding of the nature of connections and strengths across multiple entities.
Studies predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, facilitating rapid decision-making across the organization.
Trend 9: The Rise of the Augmented Consumer
Most business users are today using predefined dashboards and manual data exploration. These things are leading to incorrect conclusions and flawed decisions and actions. Also, time spent in predefined dashboards will progressively be replaced with automated, conversational, mobile, and dynamically generated insights. Augmented consumer is also becoming one of the best data and analytics technology trends.
Trend 10: Data and Analytics at the Edge
Data, analytics and other technologies supporting them increasingly reside in edge computing environments, closer to assets in the physical world and outside IT’s purview. Studies predicts that by 2023, over 50% of the primary responsibility of data and analytics leaders will comprise data created, and analyzed in edge environments.
D&A leaders can use this trend to enable greater data management flexibility, speed, governance, and resilience. Diversity of use cases is driving the interest in edge capabilities for D&A. The diversity ranges from supporting real-time event analytics to enabling autonomous behavior of “things”