Gartner Data & Analytics Summit 2024 India: Day 2 Highlights

We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week in Mumbai, India. Below is a collection of the key announcements and insights coming out of the conference. You can read the highlights from Day 1 here.

On Day 2 of the conference, we are highlighting the significance of data products and how to create and implement them, discussing the role of chief data and analytics officers (CDAOs) in leading data-driven change management, as well as how GenAI is changing the way data is managed. Be sure to check this page throughout the day for updates.

• Gartner Identifies the Top Trends in Data and Analytics for 2024

Presented by Ehtisham Zaidi, VP Analyst, Gartner

Data engineering teams are struggling to support business domains in their demands for integrated and harmonized data. Simply adding more data engineers to the data management team is not a sustainable solution. In this session, Ehtisham Zaidi, VP Analyst at Gartner, discussed the importance of data products, highlighted its pros and cons and explained how  organizations can start building and managing their data products.

• “A data product is a curated and self-contained combination of data, metadata, semantics and templates. It includes access and implementation logic certified for tackling specific business scenarios and reuse.”

• “Data products are costly to build and maintain. If they are developed without considering scalability and reusability, they will eventually be abandoned.”

• “D&A leaders must collaborate with domain teams to identify use cases that are well-established and have the broadest-understood context. These are ideal use cases where data products can be beneficial.”

• “Data products should be managed appropriately, following the product life cycle and product management practices.”

•  “Organizations must invest in the data product manager role. Besides being skilled in product management, data product managers must be adept at translating business requirements for data engineering teams.”

Presented by Donna Medeiros, Sr Director Analyst, Gartner

Chief data and analytics officers (CDAOs) play a critical role in driving and overseeing major business changes to deliver enterprise value. In this session, Donna Medeiros, Sr Director Analyst at Gartner, explained why clear, actionable change management plans are essential for both proactive and reactive data-driven change.

• “Recruit change team members who represent the stakeholders implementing the changes and who are affected by data-driven change management programs themselves. Only a few of the ‘must have roles’ are oriented substantively to matters of engaging with people, rather than technologies.” 

• “The building of stuff is the means to the end, not the end in itself. If data management isn't understood as primarily an intellectual endeavor, then you've totally missed the point and you shouldn't be doing data management.”

• “CDAOs must communicate hard benefits for both the organization and the individual stakeholders.”

• “You have to be able to measure the results - and the metrics help shape the narrative. People don't make decisions based on data; they make decisions based on narrative.”

• “Address resistance to change by diagnosing cultural and behavioral barriers. Guide stakeholders by contrasting desired and resistant stakeholder behaviors.”

Presented by Ramke Ramakrishnan, VP Analyst, Gartner

Generative AI (GenAI) is revolutionizing data management, with innovation occurring at a rapid pace. In this session, Ramke Ramakrishnan, VP Analyst at Gartner, discussed how GenAI is reshaping the landscape of data management.

• “Through 2026, GenAI will reduce manually intensive data management costs up to 20% each year while enabling four times as many new use cases.”

• “GenAI capabilities can simplify processes and speed up results, whether by augmenting the efforts of human workers, generating software code or optimizing chatbot performance.”

• “Identify potential use cases that are being slowed down by engineering bottlenecks and the lack of business involvement.”

• “Build data literacy and GenAI skills within your organization to safely use these emerging data management technologies.”

• “Partner with business stakeholders to evaluate and demonstrate the business value of GenAI by tracking improvement of data management to show tangible benefits.”