AI or Data Governance? Gartner Says You Need Both

It has been almost three years since generative AI was introduced to the world with OpenAI’s ChatGPT, but only a few businesses have been able to achieve more than modest gains in employee productivity. The good news, according to Gartner, is that more than half of CEOs believe artificial intelligence will have the most significant impact on their industry over the next three years.

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Data and analytics leaders, such as chief data officers, or CDOs, and chief data and analytics officers, or CDAOs, play a significant role in driving their organizations’ data and analytics, D&A, successes, which are necessary to show business value from AI projects.

Gartner predicts that by 2028, 80% of gen AI business apps will be developed on existing data management platforms. Their analysts say, “This is the best time to be in data and analytics,” and CDAOs need to embrace the AI opportunity eyed by others in the C-suite, or they will be absorbed into other technical functions.

With high D&A ambitions and AI pilots becoming increasingly ubiquitous, focus is shifting toward consistent execution and scaling. But D&A leaders are overwhelmed with their routine data management tasks and need a new AI strategy.

During the Day 1 keynote at the recently held Gartner Data and Analytics Summit in Mumbai, Ehtisham Zaidi, vice president analyst at Gartner, and Aura Popa, senior director analyst at Gartner, discussed how D&A leaders can continue innovating their data management solutions while scaling success for their AI journey.

“Data is exhausting. We are trapped in endless cycles of data preparation and crazy stakeholder expectations. This relentless era of technology transformation is draining morale even faster than it drains our resources. We are struggling with growing complexity and growing doubts, even in our own ability to deliver,” Zaidi said.

Data and AI Challenges

D&A leaders face many challenges when it comes to data and AI, including technical debt, relentless costs, endless preparation, uncertain regulations, pressure to deliver value, constant technological change, unrealistic expectations and governance.

One of the top three challenges for D&A leaders is that many businesses lack a clear understanding of D&A. Data quality is another major challenge, with over half of organizations reporting issues with their data. Forty-nine percent of organizations identify demonstrating the value of AI as a top barrier.

“Misplaced trust in data for AI has led to a collection of issues,” Popa said. The issues she was referring to were data breaches, bias, lack of leadership, failed ROI, deepfakes, harm, job loss and hallucinations.

D&A Governance

A key theme of the summit was D&A governance, with some analysts making statements about its significance.

“We’ve never been good at governance, and now AI demands that we be even faster, which means you have to take more risks and be prepared to fail. We have to accept two things: Data will never be fully governed. Secondly, attempting to fully govern data before delivering AI is just not realistic. We need a more practical solution like trust models,” Zaidi said.

He said trust models provide a trust rating for data assets by examining their value, lineage and risk. They offer up-to-date information on data trustworthiness and are crucial for fostering confidence. They let us set a strictness parameter for governance, but for a given context. Innovation and governance can go hand-in-hand, accelerating the adoption of trust models.

Sumit Agarwal, vice president analyst at Gartner, gave a technical overview of AI governance and spoke about managing AI, trust and security.

The key takeaway from the session on managing AI was that gen AI has highlighted the need for AI governance and security funding at the board level. Most companies still have a significant amount of work to do in terms of policies and governance.

Top Fears and Risks

In one of the sessions, Anirudh Ganeshan, senior principal analyst at Gartner, discussed the top fears, risks, objections and hurdles D&A leaders face and how they handle them.

Quoting a recent Gartner D&A survey, Ganeshan said the top five challenges hindering success for D&A leaders are non-technology related. Apart from budget and resourcing constraints, challenging company culture, skills and staff shortages, and poor data literacy, ineffective or inadequate D&A governance is a rising challenge.

“Surprisingly, none of these challenges relate to technology. From a business perspective, it is more about the fear of missing out on a new technology, efficiency, experience, productivity, acceleration as well as some overlaps – ROI, privacy, security and reliability,” Ganeshan said.

Gartner Recommendations

To counter these challenges, Gartner analysts made these recommendations:

• Establish trust: Move from fear to trust. Provide a heads-up of industry and technology trends to key stakeholders. Focus on impact, not hype.
• Demonstrate benefits: Tie data pain points and opportunities to organizational goals by pinpointing what is inhibiting data-driven decision-making and determining its downstream impact on business outcomes.
• Work with early adopters: Deliver high-value use cases to advertise value for the organization.
• Ensure D&A systematically captures experiences and lessons learnt: Be the catalyst to foster prototyping.
• Establish a solutions-first approach: A solutions-first approach requires a deep understanding of the problem and what it’s causing. Once the problem is understood, identify or create a solution to address it.
• Focus on more than just technology: Technology changes quickly, so stay open to new possibilities. Build agile governance principles that can adapt to changing circumstances. Trust employees to make decisions within defined parameters. Continuously monitor progress and adjust strategies as needed.
• Determine responsibilities between business and IT teams: Set up a hybrid multi-tiered organizational model and determine where to position the global hub and CDAO. Balance traditional and emerging roles and actively engage with domain roles.
• Develop a talent development plan: Build the right skills, knowledge and competencies – work with HR to embed changes enterprisewide.

How to Create an AI Strategy

D&A leaders can turn the promise of AI into reality by developing and executing their own AI strategies. Here are some ways to do it:

• Start with vision, value and risks by assessing the importance of AI for your organization, identifying where the value lies and determining your risk appetite;
• Create a road map for AI adoption, maturing the key enabling capabilities of organization, people and culture, governance, engineering, and data;
• Regularly recalibrate your AI strategy by engaging all relevant stakeholders. Ensure that AI initiatives are aligned with your business, data and analytics, and IT strategies, and vice versa.