Gartner has released the top 10 data and analytics (D&A) trends for 2023. These trends can guide data and analytics leaders in anticipating change and turning large variables into new business opportunities that can open up new sources of value for their enterprise organizations. Here are the top 10 data and analytics trends for 2023 (Gartner, May 2023)

     Trend 1: Value Optimization

Most data and analytics leaders struggle to articulate the value they create for enterprise organizations using business terms. In order to optimize value using an enterprise organization's data, analytics, and artificial intelligence (AI) portfolio, they need to ensure that the expected value is realized by applying an integrated and comprehensive set of value management capabilities such as value storytelling, value stream analytics, investment ranking and prioritization, and business outcome measurement.

     Trend 2: AI Risk Management

With the increasing use of AI, organizations are facing a variety of new risks that must be mitigated, such as moral hazard, training data poisoning, and fraud detection avoidance. Managing AI risk goes beyond regulatory compliance; effective AI governance and responsible AI practices are equally critical to gaining the trust of interested parties and driving AI adoption and use.

     Trend 3: Observability

Observability is a characteristic that helps understand the behavior of data and analytics systems and asks questions about their behavior.

Observability enables organizations to reduce the time it takes to discover the root cause of performance issues and make timely and cost-effective business decisions with reliable, accurate data," said Gareth Herschel, research vice president at Gartner. Data and analytics leaders need to evaluate data observability tools in order to understand the needs of key users and determine how to integrate these tools into the entire enterprise ecosystem."

     Trend 4: Data sharing becomes a necessity

Data sharing is divided into internal (between departments or subsidiaries) and external (between parties not owned by your business organization and not controlled by your business organization) data sharing. Organizations can "productize" data, treating data and analytics assets as a deliverable or shared product.

Data sharing collaboration, including external data sharing collaboration, increases the value of data sharing by adding previously created reusable data assets," said Kevin Gabbard, senior research director at Gartner. Data weaving designs can be adopted to enable a single data sharing architecture across heterogeneous internal and external data sources."

     Trend 5: Data and Analytics Sustainability

To improve sustainability, data and analytics leaders must not only provide analytics and insights for corporate environmental, social, and governance (ESG) programs, but they must also work to optimize their processes, which can potentially bring them significant benefits. Data and analytics and AI practitioners are increasingly aware of their growing energy footprint. As a result, they are starting to adopt new practices such as using renewable energy sources in (cloud) data centers, using more energy-efficient hardware, and using small data and other machine learning (ML) techniques.

     Trend 6: Practical data weaving

Data weaving is a data management design approach that uses various types of metadata to observe, analyze, and recommend data management solutions. By bringing together and enriching the semantics of the underlying data and analyzing the metadata on an ongoing basis, data weaving produces alerts and recommendations that can be acted upon by humans and systems. It enables business users to consume data with confidence and helps less-skilled citizen developers acquire more comprehensive process integration and modeling capabilities.

     Trend 7: Emerging AI

ChatGPT and generative AI are the "vanguard" of upcoming emerging AI trends. Emerging AI will change the way most organizations operate in terms of scalability, versatility, and adaptability. The next wave of AI will enable organizations to apply AI to situations where it is not currently feasible, making AI more pervasive and valuable.

     Trend 8: Converging and Composable Ecosystems

Data and analytics platforms designed and deployed by the Converged Data and Analytics Ecology achieve operational and functional consistency through seamless integration, governance, and technology interoperability. Ecologies achieve composability by building, assembling, and deploying configurable applications and services.

The right architecture increases the degree of modularity, adaptability, and flexibility of data and analytics systems, enabling them to dynamically scale and become leaner and more efficient to meet growing and changing business needs and evolve with inevitable changes in business and operational environments.

     Trend 9: Consumers become creators

The time users spend on predefined dashboards will be replaced by conversational, dynamic and embedded user experiences that meet the immediate needs of specific content consumers. Enterprise organizations can expand the adoption and impact of analytics by providing content consumers with the easy-to-use automated and embedded insights and conversational experiences they need to become content creators.

     Trend 10: Humans Remain Key Decision Makers

If enterprise organizations focus on driving decision automation while ignoring the role of humans in decision making, they will become a data-driven organization with no conscience and a disorganized mindset," said Herschel. Enterprise organizations need to emphasize the integration of data and analytics with human decision-making in their data literacy programs."