Quest’s New Report Highlights the Evolving Landscape of Data Intelligence

Organizations are increasingly leveraging data intelligence to enhance their data and analytics, according to an annual report released this week by Quest, a provider of cybersecurity, data performance, and database solutions.

This year’s Quest State of Data Intelligence Report highlights that it will be a transformative year ahead for data intelligence. Organizations are adopting strategies that balance offensive governance, which promotes data literacy and accessibility, with defensive governance which manages risks and enhances data visibility.

The report is based on a year-long survey of over 200 business and IT professionals in North America who are either responsible for or familiar with data management, data governance, and data operation strategies at their organization. All participants were from organizations with at least 1,000 employees and annual revenues of over $100 million.

The findings of the report reveal data modeling is the foundation for data product delivery and collaboration. A significant 84% of organizations are now delivering data products to enhance the consumption of high-value and trusted data. Among these, 86% are modeling their data products, with an average of 155 products. This trend underscores the critical role of data modeling in modern business strategies, ensuring that data is relevant, well-structured, and ready for analysis.

The respondents shared that the top benefits of data modeling include improved collaboration between business and IT stakeholders, reduction in time in data product development and deployment, and creation of a foundation for data governance.

Another key finding from the report is that data governance has evolved from being a matter of legal compliance to becoming a crucial component of AI-ready strategies. While the top data governance drivers are improving data quality (42%), security (40%), and analytics(40%), AI has debuted in the fourth spot (34%), emphasizing the increasing focus on the role of AI in data government and data readiness.

“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.”

One-third (33%) of organizations are still struggling to evolve their data and governance to an AI-ready state. This bottleneck has significantly impacted the data value chain.

Additionally, many organizations are struggling to understand the quality of their source data, a challenge affecting 38% of them. The challenge to find, identify, and harvest data assets ties closely with the governance issue, also impacting 33% of organizations. Together, these issues highlight the critical need for enhanced data management strategies to maximize the value of data in the era of AI.

(Den Rise/Shutterstock)

The findings also indicate that data marketplace adoption is surging, with a 71% year-on-year increase. An overwhelming majority (95%) plan on or have already established a self-service data marketplace, with 78% expecting game-changing or significant benefits from it.

The increase in adoption of the internal data marketplace has also helped address the skills gap required for effectively leveraging data within an organization. According to an IBM report released earlier this year, the skills gap remains a major barrier to AI adoption. Internal data marketplaces can provide easier access to data and resources, potentially supporting employees in developing the skills needed for data-driven tasks.

As organizations increasingly rely on AI, the report outlines significant challenges in managing data intelligence programs, particularly in governance, data quality, and metadata management.

“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.”

If the findings from Quest are any indication, organizations that prioritize their data practices will be well-positioned to drive growth and maximize the return on their AI investments. This pivotal year has the potential to act as a catalyst for advancements in data intelligence, setting the stage for a more data-driven future.

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