Quest Research identifies emerging data intelligence strategies

Quest Software, in collaboration with ESG (Enterprise Strategy Group), has released the 2024 State of Data Intelligence Report, an annual report highlighting the critical role data intelligence plays in unlocking value, optimizing resources, and building resilient, data-driven enterprises.

As 2024 has been a transformative year for data intelligence with organizations adapting to the growing demands of AI, this year has also brought AI data readiness and operational efficiencies to the forefront as businesses move to democratize data access while safeguarding against emerging risk and regulatory compliance needs.

The 2024 State of Data Intelligence Report provides IT and business leaders in enterprise-level organizations with the guidance and benchmarking needed to inform their technology choices, work process and practices, and overall strategy for successfully executing data intelligence initiatives.

Key findings from the report include:

• AI is firmly in sight for governance teams and data intelligence initiatives. Improving data quality (42%), security (40%), and analytics (40%) remain top data governance drivers. In 2024, ensuring data readiness and quality for AI (34%) debuted as the fourth most cited driver of data governance programs. Organizations additionally reported evolving data and governance to an AI-ready state (33%) as a top three bottleneck impacting an organization’s data value chain, behind understanding the quality of source data (38%) and tied with finding, identifying and harvesting data assets (33%).

• Data marketplace adoption is surging and yielding strong business benefits. Data marketplace adoption is up 71% year-on-year, with 95% of organizations either planning to create or already have a self-service data marketplace; 78% of respondents cited significant or game-changing benefits. Correlated with the data marketplace adoption, the number of respondents who cited skills shortages as the top challenge to the strategic use of data in their organizations has decreased by 2.6 times since 2023.

• Data modeling is the foundation for data product delivery and collaboration. Today, 84% of organizations are delivering data products, with 86% modeling their data. Of these, 71% view data modeling as crucial or transformative for enhancing data product delivery and fostering collaboration. Additionally, 86% plan to invest in data products in the next 12 to 24 months.

“As AI continues to be a force multiplier of the data-driven enterprise, ensuring that your organization’s data and governance is AI-ready is now a top-level business need,” said Bharath Vasudevan, VP of Product Management at Quest Software. “With data intelligence emerging as a key enabler of AI data readiness and operational efficiency, businesses will now have the ability to effectively position and ensure their data as a strategic growth asset rather than an accelerator of business risk.”

The report also revealed that 36% more organizations believe they have a clearly articulated data intelligence strategy in comparison to last year. The top three data intelligence strategy priorities include increasing the strategic use of high-value data (38%), enriching data quality (38%), and developing and strengthening data and governance practices for future AI use (34%).

“Organizations are seeing the business returns of focusing time and investment in data intelligence programs when a clearly articulated data intelligence strategy is in place,” said Stephen Catanzano, senior analyst at Enterprise Strategy Group. “The challenge for organizations today is to balance their attention between getting more business value from reliable data right now while at the same time laying the groundwork to reduce the risk from and accelerate value from future AI use.”

When specifically asked about managing key components of data intelligence programs, including data mapping, data lineage and data policies, organizations reported governing the use of AI models and data as the most difficult management challenge. AI governance topped the list with metadata management — a key component of AI data readiness — rising by 21% year over year. Data quality monitoring, data quality remediation, data profiling and quality scoring, and data policies and control rounded out the top six challenges organizations are currently grappling with in the AI era.

“The fundamentals of data intelligence such as strong metadata management, data modeling, data lineage, integrated data quality and business-supporting governance, visibility and accessibility to high-value, trusted data are non-negotiables today,” according to Bharath Vasudevan, VP of Product Management at Quest Software. “They are proving to be the difference makers in succeeding in this era of greater business self-service and ensuring your data will be an asset.”