Gartner: Top 10 Data and Analytics Trends

Value optimization is the top trend identified by Gartner analysts at its data and analytics summit held recently in India.

Helen Hwang, Contributor

May 17, 2023

3 Min Read
Shannon Fagan/Getty Images

Data and analytics teams struggle with explaining the value they deliver to the company in a way business leaders understand, according to a new report by Gartner.

As such, data and analytics (D&A) leaders must engage cross-functionally to understand the best way to drive adoption, combining better analysis and insights with an understanding of human psychology and values.

This and other top data and analytics trends were announced at the recent Gartner Data & Analytics Summit in Mumbai, India.

Here are the top 10 trends:

1. Value optimization: D&A leaders should clearly convey the connection between business priorities and D&A initiatives by honing their value-management competencies, including value stream analysis, value storytelling, and the measurement of business outcomes.

2. Managing AI risks: This includes taking note of new risks such as poisoning of training data, fraud detection circumvention or ethical risks. Creating trust among stakeholders is imperative to the increased adoption of AI.

3. Observability: This enables companies to reduce the time it takes to identify the cause of problems that can impact performance and also enables them to tap reliable and accurate data to make timely business decisions. D&A leaders must evaluate their data observability tools in light of the needs of users and how these tools fit into the enterprise overall.

4. Data sharing is essential: Whether internally and externally, collaborating to share data is key. “Adopt a data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources,” said Kevin Gabbard, senior director and analyst at Gartner.

5. D&A sustainability: D&A leaders must not only provide analysis and insights of data, but also must improve their own processes to aid in sustainability. Here, a variety of practices are emerging, which include using renewable energy to power data centers as well as tapping more energy-efficient hardware, use of small data and other machine learning techniques.

6. Practical data fabric: A data fabric is a data management design pattern that weaves together all types of metadata to observe, evaluate, and recommend data management solutions. It can generate alerts and recommendations to users and enables business users to confidently absorb the data.

7. Emergent AI: With generative AI and ChatGPT rising in popularity, these emergent AI technologies will change how businesses adapt and scale. The next generation of AI technologies will give companies capabilities that are not feasible now.

8. Converged and composable ecosystems: These ecosystems design and deploy D&A platforms for seamless integrations, technical interoperability, and governance. With the right architecture, these systems can be more modular, adaptable and flexible to scale dynamically as needed.

9. Consumers become creators: The use of predefined dashboards will be replaced by dynamic, conversational, and embedded user experiences to address content consumers’ needs in real time. Companies can expand the adoption of analytics by providing users easy-to-use automated and embedded insights and conversational experiences so they can be creators.

10. Humans remain key decision-makers: D&A leaders are evaluating the role of humans in automated and augmented decision-making since not all decisions can or should be automated. “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 Gareth Herschel, VP analyst at Gartner.

About the Author(s)

Helen Hwang

Contributor, AI Business

Helen Hwang is an award-winning journalist, author, and mechanical engineer. She writes about technology, health care, travel, and food. She's based in California.

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