Lack of AI-Ready Data Puts AI Projects at Risk

Q&A with Roxane Edjlali, Senior Director Analyst, Gartner

Overview

Sixty-three percent of organizations either do not have or are unsure if they have the right data management practices for AI, according to a survey by Gartner. A survey of 1,203 data management leaders in July 2024 found that organizations that fail to realize the vast differences between AI-ready data requirements and traditional data management will endanger the success of their AI efforts.

In fact, Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.

Ahead of the Gartner Data & Analytics Summit, taking place from March 3-5 in Orlando, we sat down with Roxane Edjlali, Senior Director Analyst at Gartner, to discuss how AI is forcing CIOs and chief data and analytics officers (CDAOs) to change their data management practices to support and implement AI in their businesses.

Q: How can IT leaders integrate AI-ready data strategies with existing data management systems?

A: Gartner recommends that organizations build on their existing data management practices by iteratively adding AI-specific data innovations that help extend and improve data management to support new use cases, such as GenAI. These could include vector data stores, chunking, sampling, embedding and retrieval-augmented generation (RAG) integration, among others.

Remember that AI-ready data is not “one and done.” Think of it as a practice where the data management infrastructure needs constant improvement based on existing and upcoming AI use cases. As the organization invests in AI, develop an AI-ready data practice and ensure continued investment and ongoing maturity in metadata management, data observability, and D&A and AI governance.

IT leaders can’t continue to only rely on formal data management practices if they want to successfully integrate AI in their data and analytics (D&A) strategy. Traditional data management operations are too slow, too structured, and too rigid for AI teams. Moreover, in traditional data management, uses of data are not well-documented, and data is often collected in siloes across various repositories, multiple systems and platforms. Organizations lack the required practice and metadata to assess the readiness of data for AI.

Q: What steps can leaders take to ensure their data is AI-ready?

A: First, leaders must define what constitutes AI-ready data. The data must be representative of the use case, of every pattern, errors, outliers and unexpected emergence that is needed to train or run the AI model for the specific use.

Proving AI readiness of the data is a process and a practice based on the availability of metadata to align, qualify and govern the data.

There are five steps that CIOs and CDAOs should consider to make their data AI-ready.

1. Align data to AI use cases: CDAOs should consider various data sources for AI use cases, including internal or external data sources.

2. Identify data governance requirements for AI to prevent or mitigate the risks of violating legal requirements and the unethical use of AI products. CDAOs should work closely with legal and business leaders to answer questions such as whether the data will be interoperable across many user communities and applications, how sensitive data can be automatically detected, and how this data should be protected when being fed into AI models.

3. Evolve metadata from passive to active to build intelligence and provide continuous iterative improvement and automation. CDAOs should discover, enrich and analyze metadata and infer a recommendation from the results.

4. Prepare data pipelines to build an AI model dataset for training purposes as well as for a live data feed to AI production systems based on the requirements gathered.

5. Assure and enhance data: Once the data is available for AI model training, CDAOs should test and monitor it to optimize the models. They can implement DataOps and data observability processes to track data patterns and changes, adjusting data requirements as needed.

Above all, if the data has issues, then the data is not ready for AI.

Q: How do organizations govern AI-ready data?

A: As organizations move from AI pilots to fully operational AI, using collaborative and cross-domain strategies to manage and govern AI across the company becomes crucial for continued success.

CIOs and CDAOs can consider enterprise governance of AI (EGoAI), a flexible method that helps combine different governance areas to make responsible AI decisions and achieve business goals.

The governance journey often starts at the executive level, establishing a virtual layer of decision-making practices across existing IT, D&A and risk governance domains. For example, if an organization wants to use AI to improve customer experience and drive growth, the decision process will start at the executive level. Then, the CIO or CDAO will shape the governance response, clarify risk, value, and cost, and orchestrate the necessary decision-making to execute and deliver on executive expectations.

While governance practices are critical to data readiness, organizations with basic or manual metadata management practices will face challenges in making their data AI-ready. CDAOs and CIOs need to start maturing their metadata management practices now as a first step to AI-readiness.