Through 2025, poor data quality will persist as one of the most frequently cited challenges impeding the implementation of advanced analytics (e.g., Artificial Intelligence), according to the Gartner, Inc.. Therefore, Data & Analytics (D&A) leaders must focus on three interdependent journeys to advance data analytics initiatives. AI of companies. These journeys include business outcomes, D&A capabilities, and behavioral changes.
“AI continues to drive the business planningl, with more than half of the Chief Executive Officers (CEOs) believing that technology will have a more significant impact on their industry in the next three years,” he says Jorg Heizenberg, Vice President and Analyst at Gartner. “With this in mind, Data & Analytics leaders are in a unique position to drive maximum impact on business outcomes due to their proximity to this technology.”
“With Artificial Intelligence being a key focus area in businesses, Data & Analytics leaders must overcome the hype and focus on investments in trust, adaptability and people”, he adds Debra Logan, Vice President and Distinguished Analyst at Gartner.
During the opening keynote of the Gartner Data & Analytics Conference, Gartner analysts discussed three interdependent trajectories in depth to better guide D&A leaders on their AI journey.
Journey to Business Results
Gartner advises data and analytics leaders to prioritize value in demonstrating business outcomes from AI.
“Demonstrating the value of AI remains one of the top barriers to implementation,” says Heizenberg. “Data & Analytics leaders must focus on creating the right levels of trust, based on context, as the first step to demonstrating value.”
Data & Analytics leaders can take the following steps to positively impact business outcomes:
· Establish trust models: Trustworthy, high-quality data is essential to enabling a data-driven enterprise. However, many AI initiatives fail due to poor data quality. Trust models analyze the value and risk of data and provide a trustworthiness score based on lineage and curation.
· Monetize productivity improvements: Data & Analytics leaders must consider value and competitive impact against total cost, complexity, and risk.
· Communicating the value of D&A: Consider all costs, including data management, governance, and change management.
Journey to D&A Resources
Data & Analytics leaders must ensure they are using a variety of tools and technologies to build their stack of technology when it comes to AI solutions.
“The comparison of stack versus the best solution in each category (best of breed) is not new, but the dynamics of this decision are,” says Krensky. “Data & Analytics leaders must cultivate an adaptable ecosystem that scales to meet the demands of building the best possible AI offerings.”
To achieve this adaptability, Data & Analytics leaders must:
· Create a modular and open ecosystem: Update or replace architectural components to meet new requirements and rapidly changing technologies.
· Make the data AI-ready and reusable: Integrate trust across FinOps, DataOps, and PlatformOps to transition from a stack of technology for a stack trustworthy.
· Explore AI agents: Leverage dynamic agents that adapt to change using an AI-ready data ecosystem powered by active metadata.
Journey to Behavior Change
Focus on data governance, in communicating value and increasing analytics is vital, but addressing the human aspect is crucial to the success of Artificial Intelligence.
“AI is transforming everything, and people are expected to transform themselves as well,” Heizenberg says. “But people are not all the same, and we engage with data and analytics in different ways.”
To establish the foundation of the right culture to adopt and best utilize Artificial Intelligence, Data & Analytics leaders should follow these steps:
· Establish repeatable habits: Prioritize training and education with an emphasis on data literacy and AI.
· Adopt new roles and skills: Develop functions that facilitate adaptation to the change management requirements of Generative Artificial Intelligence (GenAI).
· Collaborate with others: Work with diverse teams, including security and software engineering, to achieve smooth integration.
Data & Analytics leaders can learn more about how to assess their own effectiveness using the Gartner CDAO Effectiveness Diagnostic, a unique tool that allows Chief Data & Analytics Officers (CDAOs) understand their effectiveness as leaders and discover their strengths and areas for improvement.
About the Gartner Data & Analytics Conference
Gartner analysts provided additional analysis on data and analytics trends at the Gartner Data & Analytics Conference, held in Sao Paulo (Brazil). More insights will be highlighted from May 12-14 at London (England); from 20 to 22 May in Tokyo (Japan); on June 2 and 3 in Mumbai (India) and on June 17 and 18 in Sydney (Australia). Follow conference news and updates on X using #GartnerDA.
About Gartner for Data & Analytics Leaders
The Gartner for Data & Analytics Leaders provides objective and actionable insights for CDAOs and Data & Analytics leaders, helping them accelerate their D&A strategy and operating model to increase business value. Additional information is available at https://www.gartner.com/en/data-analytics. Follow news and updates from Gartner for D&A Leaders on X and LinkedIn using #GartnerDA.
About Gartner
O Gartner, Inc. provides objective, actionable insights for executives and their teams. Gartner’s guidance and tools enable faster, smarter decisions and stronger performance on enterprises’ mission-critical priorities. To learn more, visit www.gartner.com
