The data ownership blind spots putting organizations at risk

Common data ownership oversights

Quality data has become a critical asset for organizations, especially for automation, analytics and AI. Data owners, data stewards and chief data officers are responsible for ensuring that data meets established standards and has trackable lineage.

But there are still types of data that organizations fail to capture and govern effectively, including:

• Unstructured content from communication channels. Transcripts from messaging and collaboration apps, meeting transcripts and recordings, and content from emails and social media exchanges all lack ownership but might contain critical business information.
• Data generated by shadow IT systems. About 70% of CIOs believe that the business units in their organizations deploy unsanctioned tech, according to Flexera's 2026 IT Priorities Report. That creates shadow data -- that is, data generated and stored outside the purview of the organization's IT and security controls as well as its data governance program.
• Data developed in sandboxes and for temporary projects. This spans from developmental databases and test environments to intermediate datasets to training data for AI and machine learning models.
• Siloed data. Many data types fall into this category, including data stored in individual desktop folders, temporary reports, disconnected spreadsheets and data in legacy systems. Some also put vendor and third-party data in this category. Dark data also falls into this category, even though the business no longer uses it.
• Operational and machine data. Various data types belong here, ranging from systems logs, IoT sensor data and API payloads to metadata. These datasets often lack a single owner and are overlooked in management and governance.

Unowned data governance challenges

Establishing governance and ownership over unowned data is challenging. Experts noted that these data types are hard to classify, easy to overlook and span multiple business functions. Consequently, no single business leader claims or is assigned ownership. Or it might be that data ownership lies with many or all leaders, which lacks accountability.

Regardless, unowned data puts the organization at risk of costly data breaches, regulatory violations and inaccurate outputs from analytics, automation and AI initiatives due to poor data quality.

With such consequences in mind, many executives seek to improve their organization's overall data management and governance. According to Workvia's 2026 Executive Benchmark Survey, business leaders ranked "strengthening data governance" as the second-highest priority for digital transformation projects, behind automating data collection and validation.

Similarly, Informatica's CDO Insights 2026 Report found that 86% of 600 surveyed data leaders planned to increase data management investments -- and 41% seek to boost data and AI governance. This is in response to concerns about poor data quality affecting business objectives.

Ultimately, data management and governance won't succeed if they can't be accounted for. Assigning ownership to unowned or over-shared data will help organizations strengthen data governance and boost data quality -- and their own trustworthiness.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.