Through 2025, poor data quality will persist as one of the most frequently mentioned challenges prohibiting advanced analytics (e.g., AI) deployment, according to Gartner, Inc. Because of this, data and analytics (D&A) leaders must focus on three interdependent journeys to advance enterprises’ AI initiatives. These journeys include business outcomes, D&A capabilities and behavioral change.
“AI continues to drive enterprise planning, with more than half of CEOS believing the technology will most significantly impact their industry over the next three years,” said Carlie Idoine, VP Analyst at Gartner. “With this in mind, D&A leaders are uniquely positioned to drive maximum impact on business outcomes due to their proximity to this technology.”
“With AI being a primary focus area in organisations, D&A leaders must cut through the hype and focus on investments in trust, adaptability and people,” said Gareth Herschel, VP Analyst at Gartner.
Gartner outlines three interdependent journeys in-depth to better guide D&A leaders along their AI journey.
Journey to Business Outcomes
Gartner advises D&A leaders to prioritise value in demonstrating AI’s business outcomes.
“Demonstrating the value of AI continues to be a top barrier to implementation,” said Idoine. “D&A Leaders must focus on building the right trust levels based on context as the first step to demonstrating value.”
D&A leaders can take the following actions to best affect business outcomes:
- Establish trust models: Trusted, high-quality data is key to enabling a data-driven enterprise, yet many AI initiatives fail because of poor data quality. Trust models look at the value and risk of data and provide a trust rating based on lineage and curation.
- Monetise productivity improvements: D&A leaders must consider the value and competitive impact as it relates to total cost, complexity and risk.
- Communicate value of D&A: Consider all costs, including data management, governance, and change management.
Journey to D&A Capabilities
D&A leaders must ensure they are using a range of tools and technologies to build their technology stack when it comes to AI solutions.
“Stack versus best of breed is not new, but the dynamics of this decision are,” said Herschel. “D&A leaders must cultivate an adaptable ecosystem that scales in order to meet the demands of creating the best AI offerings possible.”
To achieve this adaptability, D&A leaders must:
- Create a modular and open ecosystem: Update or replace architecture components to address new requirements and rapidly changing technologies.
- Make data AI-ready and reusable: Integrate trust into FinOps, DataOps, and PlatformOps to transition from a tech-stack to a trust-stack.
- Explore AI Agents: Utilise dynamic agents that adapt to changes using an AI-ready data ecosystem powered by active metadata.
Journey to Behavioral Change
Focusing on data governance, value communication, and analytics augmentation is vital, but addressing the human aspect is crucial for AI success.
“AI is transforming everything, and people are expected to transform too,” said Idoine. “But people are not the same, and we engage with data and analytics in different ways.”
To lay the foundation for the proper culture to best adopt and utilise AI, D&A leaders should take the following steps:
- Establish repeatable habits: Prioritise training and education with an emphasis on data and AI literacy.
- Embrace new roles and skills: Develop roles that facilitate adaptation to GenAI’s change management requirements.
- Collaborate with others: Work with diverse teams, including security and software engineering, for seamless integration.
Data & analytics leaders can learn more about how to evaluate their own effectiveness using the Gartner CDAO Effectiveness Diagnostic, an exclusive tool that allows CDAOs to understand their effectiveness as leaders and discover their strengths and areas for improvement.
