Top trends in managing data and analytics for AI
Adaptive artificial intelligence systems, data sharing, and data fabrics are among the trends that data and analytics leaders need to build on to drive new growth, resilience and innovation.
Russia’s invasion of Ukraine added a geopolitical crisis to the enduring global pandemic and managing consequent and persistent uncertainty and volatility will be a key focus for data and analytics leaders this year.
Gartner expects far fewer artificial intelligence failures, including incomplete artificial intelligence projects, and reduction in unintended or negative outcomes
Now is the time to anticipate, adapt and scale the value of your data and analytics strategy by monitoring, experimenting with or aggressively investing in key data and analytics technology trends based on their urgency and alignment to business priorities.
Institutionalise trust to achieve value from data and analytics at scale.
This year’s trends in data and analytics relate to three imperatives:
- Activate diversity and dynamism. Use adaptive artificial intelligence systems to drive growth and innovation while coping with fluctuations in global markets.
- Augment people and decisions to deliver enriched, context-driven analytics created from modular components by the business.
- Institutionalise trust to achieve value from data and analytics at scale. Manage artificial intelligence risk and enact connected governance across distributed systems, edge environments and emerging ecosystems.
Gartner has identified data and analytics trends that represent business, market and technology dynamics that enterprises cannot afford to ignore. These trends also help prioritise investments to drive new growth, efficiency, resilience and innovation.
Manage artificial intelligence risk and enact connected governance across distributed systems, edge environments and emerging ecosystems.
Adaptive artificial intelligence systems
As decisions become more connected, contextual and continuous, it’s increasingly important to reengineer decision making. You can do so by using adaptive artificial intelligence systems, which can offer faster and flexible decisions by adapting more quickly to changes.
However, to build and manage adaptive artificial intelligence systems, adopt artificial intelligence engineering practices. artificial intelligence engineering orchestrates and optimises applications to adapt to, resist or absorb disruptions, facilitating the management of adaptive systems.
Data-centric artificial intelligence
Many organisations attempt to tackle artificial intelligence without considering artificial intelligence-specific data management issues. Without the right data, building artificial intelligence is risky and possibly dangerous. As such, it is critical to formalise data-centric artificial intelligence and artificial intelligence-centric data. They address data bias, diversity and labelling in a more systematic way as part of your data management strategy — including, for example, using data fabric in automated data integration and active metadata management.
Without the right data, building artificial intelligence is risky and possibly dangerous.
Always share data
While data and analytics leaders often acknowledge that data sharing is a key digital transformation capability, they lack the know-how to share data at scale and with trust. To succeed in promoting data sharing and increasing access to the right data aligned to the business case, collaborate across business and industry lines. Consider adopting data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.
Context-enriched analysis builds on graph technologies. The information on the user’s context and needs is held in a graph that enables deeper analysis using the relationships between data points as much as the data points themselves. It helps identify and create further context based on similarities, constraints, paths and communities. By 2025, context-driven analytics and artificial intelligence models will replace 60% of existing models built on traditional data.
Many organisations attempt to tackle artificial intelligence without considering artificial intelligence-specific data management issues.
Business-composed data and analytics
Business-composed data and analytics builds on this trend, but the focus is on the people side, shifting from IT to business. Business-composed data and analytics enables the business users or business technologists to collaboratively craft business-driven data and analytics capabilities.
Decision-centric data and analytics
The discipline of decision intelligence, which is careful consideration of how decisions should be made, is causing organisations to rethink their investments in data and analytics capabilities. Use decision intelligence disciplines to design the best decision, and then deliver the required inputs.
As such, it is critical to formalise data-centric artificial intelligence and artificial intelligence-centric data.
Skills and literacy shortfall
data and analytics leaders need talent on their team to drive measurable outcomes. However, virtual workplaces and the heightened competition for talent have increased the lack of data literacy — the ability to read, write and communicate data in context — within the workforce.
As the cost of investing in data literacy and employee upskilling is constantly rising, start inserting claw-back or payback clauses into contracts with new hires to recover costs in the event that an employee departs your organisation.
Organisations need effective governance at all levels that not only addresses their existing operational challenges, but is also flexible, scalable and highly responsive to changing market dynamics and strategic organisational challenges.
Artificial intelligence risk management
If organisations spend time and resources on supporting artificial intelligence trust, risk and security management, they will see improved artificial intelligence outcomes in terms of adoption, achieved business goals and both internal and external user acceptance. Gartner expects far fewer artificial intelligence failures, including incomplete artificial intelligence projects, and a reduction in unintended or negative outcomes.
They address data bias, diversity and labelling in a more systematic way as part of your data management strategy
Expansion to the edge
More data and analytics activities are executed in distributed devices, servers or gateways located outside data centres and public cloud infrastructure. They increasingly reside in edge computing environments, closer to where the data and decisions of interest are created and executed.
Gartner analysts estimate that by 2025, more than 50% of enterprise-critical data will be created and processed outside the data centre or cloud.
Gartner has identified data and analytics trends that represent business, market and technology dynamics that enterprises cannot afford to ignore.