This year’s trends in Data and Analytics revolve around three priorities: activating dynamism, augmenting decision-making, and institutionalizing trust. Here are the data and analytics trends to keep on your radar this year.
2022 Data and Analytics Trends
Adaptive AI systems
Adaptive AI is on full throttle. It learns from new data and revises itself — changing its code to incorporate learning from new data. Through this learning process, adaptive AI adjusts operation conditions to address current needs.
These dynamics allow businesses to leverage advanced concepts such as machine learning and neural networks to drive business results.
As decisions become more connected, it’s increasingly essential to reengineer decision-making. Adaptive AI systems offer faster and more flexible decisions by adapting more quickly to changes.
A data-centric approach to AI entails building AI systems with quality data — ensuring that the data conveys what the AI must learn.
However, many organizations tackle AI without anticipating AI-specific data management issues.
Building AI without accurate data poses risks. Therefore, Data-centric AI addresses data bias, labeling more systematically as part of your data management strategy.
Metadata-driven Data Fabric
Data fabric is a custom-made design that listens, learns, and acts on the metadata. It flags and recommends actions of people and systems, enabling seamless sharing of data in a distributed data environment.
A single and consistent data management framework allows frictionless data access and processing by design across otherwise siloed storage.
Ultimately, Data Fabric enhances trust in the use of data within organizations and reduces data management tasks — including design, deployment, and operations by 70%.
Always Share Data
Being able to share data freely speeds up collaborations within organizations. Data and analytics leaders are finding ways to exchange data at scale and with trust. This requires synergy across business and industry lines to increase access to the right data for the business case.
Organizations are considering adopting a data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.
Context-enriched content includes data from articles and advertising and seeks to understand the context behind the content.
The context-enriched analysis delves into relationships between data points. It plots the user’s context and needs on graph technologies to identify and create further context based on similarities, constraints, paths, and communities.
Capturing, storing, and utilizing contextual data requires capabilities in building data pipelines, X analytics techniques, and AI cloud services that can handle various data types. By 2025, context-driven analytics and AI models will overtake 60% of the existing traditional data models.
Business-composed D&A allows business users to collaboratively craft business-driven data and analytics capabilities. The focus is on the people’s side, shifting from IT to business.
As we delve deeper into the Fourth Industrial Revolution, businesses adopt digital transformation, including AI, machine learning, RPA, data science, and other technologies to improve operations and optimize cost. However, there is a gap between tech innovation and the realities on the front lines.
A decision-centric approach aims to improve decision-making within the organization. People, Rules, Data, and Processes are organized to empower decision-makers in real-time. Decision Intelligence lets leaders tackle problems during pivotal moments to improve operational efficiency.
By 2023, over 33% of large organizations will employ some form of decision intelligence.
Skills and Literacy Shortfall
Data and Analytics are changing the way organizations operate and make decisions —
hopefully at a much faster pace. However, as more organizations adopt D&A initiatives, the competition for D&A talents intensifies.
Moreover, there is a need to enhance data literacy — the ability to read, write and communicate data in context — within the workforce.
By 2025, Gartner Research estimates that the majority of CDOs will fail to foster the necessary data literacy within the workforce.
Connected Governance addresses existing operational challenges, and makes organizations flexible and responsive to evolving market dynamics.
The challenge begins with cross-functional collaboration and readiness to change organizational structures to reach business model agility, achieving a virtual D&A governance layer across departments and geographies.
AI Risk Management
AI TRiSM is shorthand for AI (T)rust, (Ri)sk, & (S)ecurity (M)anagemen. AI TRiSM ensures AI model governance, trustworthiness, fairness, efficacy, and data protection.
AI trust (TRiSM) improves AI outcomes — adoption, achieving business goals, and internal and external user acceptance. Increased application of AI TRiSM leads to stable implementation and operation of AI models.
Gartner forecasts that by 2026, organizations that develop trustworthy AI will see over 75% of AI innovations triumph.
Vendor and Region Ecosystems
Regional data and security laws shape the way global organizations build regional D&A ecosystems. Companies need to play by the rules, migrating, and duplicating some or all parts of their D&A stack within specific regions. They may manage a multi-cloud and multi-vendor strategy.
Expansion to the Edge
More D&A activities are happening in distributed devices, gateways, or servers outside data centers and public cloud infrastructure. They now inhabit edge computing environments.
By 2025, more than half of enterprise data will be created and processed beyond the cloud or data center.
Organizations must extend D&A governance capabilities to edge environments and provide visibility through active metadata. Moreover, offer support for data in edge environments through edge-resident IT-oriented technologies.