Top 10 data and analytics trends for 2023: Gartner


Gartner, Inc. identified the top 10 data and analytics (D&A) trends for 2023 that can guide D&A leaders to create new sources of value by anticipating change and transforming extreme uncertainty into new business opportunities.

“The need to deliver provable value to the organization at scale is driving these trends in D&A,” said Gareth Herschel, VP Analyst at Gartner. “Chief data and analytics officers (CDAOs) and D&A leaders must engage with their organizations’ stakeholders to understand the best approach to drive D&A adoption. This means more and better analysis and insights, taking human psychology and values into account.”

Gartner analysts presented the top 10 D&A trends business and IT leaders must engage and incorporate into their D&A strategy (see Figure 1) at the Gartner Data & Analytics Summit, taking place in Mumbai through today.

Top 10 Trends in Data and Analytics for 2023

Source: Gartner (May 2023)

Trend 1: Value Optimization

Most D&A leaders struggle to articulate the value they deliver for the organization in business terms. Value optimization from an organization’s data, analytics and artificial intelligence (AI) portfolio requires an integrated set of value-management competencies including value storytelling, value stream analysis, ranking and prioritizing investments, and measuring business outcomes to ensure expected value is realized.

“D&A leaders must optimize value by building value stories that establish clear links between D&A initiatives and the organization’s mission-critical priorities,” said Herschel.

Trend 2: Managing AI Risk

The growing use of AI has exposed companies to new risks such as ethical risks, poisoning of training data or fraud detection circumvention, which must be mitigated. Managing AI risks is not only about being compliant with regulations. Effective AI governance and responsible AI practices are also critical to building trust among stakeholders and catalyzing AI adoption and use.

Trend 3: Observability

Observability is a characteristic that allows the D&A system’s behavior to be understood and allows questions about their behavior to be answered.

“Observability enables organizations to reduce the time it takes to identify the root cause of performance-impacting problems and make timely, cost-effective business decisions using reliable and accurate data,” said Herschel. “D&A leaders need to evaluate data observability tools to understand the needs of the primary users and determine how the tools fit into the overall enterprise ecosystem.”

Trend 4: Data Sharing Is Essential

Data sharing includes sharing data both internally (between or among departments or across subsidiaries)
and externally (between or among parties outside the ownership and control of your organization). Organizations can create “data as a product,” where D&A assets are prepared as a deliverable or shared product.

“Data sharing collaborations, including those external to an organization, increase data sharing value by adding reusable, previously created data assets,” said Kevin Gabbard, Senior Director, Analyst at Gartner. “Adopt a data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.”

Trend 5: D&A Sustainability

It is not enough for D&A leaders to provide analysis and insights for enterprise ESG (environmental, social, and governance) projects. D&A leaders must also try to optimize their own processes for sustainability improvement. The potential benefits are enormous. D&A and AI practitioners are becoming more aware of their growing energy footprint. As a result, a variety of practices are emerging, such as the use of renewable energy by (cloud) data centers, the use of more energy-efficient hardware, and the usage of small data and other machine learning (ML) techniques.

Trend 6: Practical Data Fabric

Data fabric is a data management design pattern leveraging all types of metadata to observe, analyze and recommend data management solutions. By assembling and enriching the semantics of the underlying data, and applying continuous analytics over metadata, data fabric generates alerts and recommendations that can be actioned by both humans and systems. It enables business users to consume data with confidence and facilitates less-skilled citizen developers to become more versatile in the integration and modeling process.

Trend 7: Emergent AI

ChatGPT and generative AI are the vanguard of the coming emergent AI trend. Emergent AI will change how most companies operate in terms of scalability, versatility and adaptability. The next wave of AI will enable organizations to apply AI in situations where it is not feasible today, making AI ever more pervasive and valuable.

Trend 8: Converged and Composable Ecosystems

Converged D&A ecosystems design and deploy the D&A platform to operate and function cohesively through seamless integrations, governance, and technical interoperability. An ecosystem’s composability is delivered by architecting, assembling and deploying configurable applications and services.

With the right architecture D&A systems can be more modular, adaptable and flexible to scale dynamically and be more streamlined to meet the growing and changing business needs and enable evolution as the business and operating environment inevitably change.

Trend 9: Consumers Become Creators

The percentage of time users spend in predefined dashboards will be replaced by conversational, dynamic and embedded user experiences that address specific content consumers’ point-in-time needs.

Organizations can expand the adoption and impact of analytics by giving content consumers easy to use automated and embedded insights and conversational experiences they need to become content creators.

Trend 10: Humans Remain the Key Decision Makers

Not every decision can or should be automated. D&A groups are explicitly addressing decision support and the human role in automated and augmented decision making.

“Efforts to drive decision automation without considering the human role in decisions will result in a data-driven organization without conscience or consistent purpose,” said Herschel. “Organizations’ data literacy programs need to emphasize combining data and analytics with human decision-making.”


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