The top 10 data and analytics technology trends for 2021. In the coming year, these trends can help organizations respond to change, uncertainty, and the opportunities they bring. The following ten trends are mission-critical investments for data and analytics leaders. Additionally, they will be able to anticipate, shift, and respond faster.
Trend 1: Smart, responsible, scalable AI
The increased influence of artificial intelligence (AI) and machine learning (ML) is requiring institutions to look for smarter, less data-hungry, more ethically responsible, and more resilient AI solutions. Organizations will be able to improve their efficiency and business impact by leveraging smarter, more responsible, and scalable AI algorithms. This must be one of the most adopted data and analytics technology trends.
Trend 2: Composable and analytics data
Analytics capabilities become more flexible when they are built using open, containerized structures. A multi-solution platform that combines data, analytics, and artificial intelligence components. This provides flexible and easy-to-use applications that help D&A managers connect insights to actions.
As data gravity moves to the cloud, the concept of composable data will be applied to the construction of analytics applications and data composability tools like the Composable Codex from Voltron Data will be more trendy. Additionally, cloud marketplaces and low-code solutions will enable analytics applications.
Trend 3: Data Fabrics Are the Foundation
The D&A industry is increasingly utilizing data fabric with rising digitization and empowered buyers. Furthermore, this helps to address the higher levels of diversity, distribution, scale, and complexity of their institutions’ data assets.
Data pipelines are constantly monitored by the data fabric using analytics. The purpose of data fabric is to create, deploy, and utilize diverse data assets through continuous analysis. Likewise, integration, deployment, and maintenance are all reduced by 30%, 30%, and 70%, respectively.
Trend 4: From big data to small data and wide data
ML and AI models based on large amounts of historical data became less relevant due to the extreme business changes caused by the COVID-19 pandemic. At the same time, decision-making by humans and artificial intelligence is becoming more complex and demanding, requiring D&A leaders to have a greater variety of data to provide better situational awareness.
Therefore, D&A leaders should choose analytical techniques that take advantage of available data more effectively. DA leaders rely on the wide data used to analyze and synergize unstructured, structured, and some structured data sources, as well as small data, which uses techniques requiring less data, but providing useful insight nonetheless.
While reducing organizations’ reliance on large data sets, small and wide data approaches provide robust analytics and AI. With wide data, organizations empowered to gain a 360-degree view of their operations and apply analytics to make more informed and effective decisions.
Trend 5: XOps
XOps, including DataOps, MLOps, ModelOps, and PlatformOps, aim to achieve agility, reusability, and redundancy through the use of DevOps techniques. At the same time, it eliminates duplication and enables automation.
Operationalization primarily addressed as an afterthought to most analytics and AI projects. With XOps, D&A leaders will be able to ensure the reproducibility, traceability, integrity, and integrability of analytics and AI assets.
Trend 6: Engineering Decision Intelligence
It applies not only to individual decisions, but to sequences of them, grouped into business processes, and even to networks of emergent decisions and consequences. Engineering decisions provide D&A leaders with an accurate, repeatable, transparent, and traceable option when making decisions.
Trend 7: Analytics and data are core business functions
D&A is becoming a core business function instead of being a secondary activity. In this scenario, D&A becomes an asset of the company that aligns with its results. Additionally, D&A silos dissolve because central and federated teams collaborate more effectively.
Trend 8: Everything is related to a graph
Data and analytics are based on graphs to discover relationships between people, places, things, events, and locations within diverse datasets. D&A leaders can answer complex business questions quickly with graphs. Besides contextual awareness, there is a need to comprehend the nature of the connections and the strengths between the various entities involved.
By 2025, graph technologies will provide rapid decision-making across an organization in 80% of data and analytics innovations.
Trend 9: The Rise of Augmented Consumers
Predefined dashboards and manual data exploration today used most often by business users. As a result, incorrect conclusions are drawn and flawed decisions are taken. In addition, predefined dashboards will gradually be replaced by automated, conversational, mobile, and dynamic insights. The augmented consumer is also becoming one of the best data and analytics technology trends.
Trend 10: Insights from data and analytics
Analytics, data, and other technologies supporting them increasingly reside in edge computing environments, closer to assets on the ground and outside of IT’s control. The majority of primary responsibilities of data and analytics leaders predicted to involve data generated and analyzed at the edge by 2023.
By leveraging this trend, D&A leaders can enable greater flexibility, speed, governance, and resilience in data management. D&A edge capabilities are proving to be popular due to their diversity of use cases. In addition to providing real-time event analytics, we can also enable autonomous behavior in “things.”
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