MinerEye Data Tracker

Large amounts of critical corporate information live outside structured systems like ERP, CRM, and databases, ending up as “unstructured data” in documents, slide decks, spreadsheets, and PDFs stored across file servers, collaboration platforms, and cloud storage. This sprawl is intensified by routine exports from systems such as SAP, making hundreds of recurring extracts common. The resulting unstructured data often contains highly sensitive material, including R&D intellectual property with product-liability implications, pre-publication financial figures, and PII—now carrying heightened risk because organizations must both prevent leakage and be able to locate and manage such data to satisfy GDPR data-subject rights.

Most organizations lack sufficient control over these stores, and traditional Identity and Access Governance reaches its limits because it is not designed to identify sensitive content or manage entitlements at the fine-grained level needed for Windows file servers and SharePoint. Meanwhile, tools aimed at platform administrators are often too technical and confined to single environments. This gap has driven the emergence of Data Governance (also called Data Access Governance or Entitlement & Access Governance), focused on identifying/classifying unstructured data, establishing ownership, and governing access entitlements across repositories.

MinerEye DataTracker addresses the hardest part: understanding content criticality through identification, classification, and tracking. Using MinerEye’s “Interpretive AI,” it performs unsupervised similarity detection at scale (claimed up to 1TB/hour per VM), then applies supervised learning for classification via multiple training inputs such as Windows Server sources, Office 365/Azure Information Protection labels, dictionaries, and customer-defined learning sets. Results can be tagged, analyzed via strong dashboards and query/filter tools (including residency mapping), and exported through integrations and APIs to tools like DLP, DAM, IRM, and SIEM. Strengths include speed, multimodal (text and graphical) analysis, and broad data-source support; key challenges are limited application integrations, a small partner ecosystem, and a training UI that could be improved.

• Unstructured corporate data commonly exists as text documents, slide decks, spreadsheets, and PDF files.
• Data exports from SAP systems routinely produce large quantities of unstructured files, with hundreds of regular exports being common.
• R&D information can contain intellectual property and also be relevant to product liability.
• Financial data used to prepare annual statements is sensitive and must not leak before certain dates for listed companies.
• GDPR increases the need to understand where PII resides in order to support data-subject rights.
• Identity and Access Governance does not support identification of unstructured data or fine-grained entitlement management for Windows file servers or Microsoft SharePoint.
• MinerEye DataTracker applies AI and machine learning to classify data using binary, pictorial, and textual patterns.
• Interpretive AI’s first phase runs unsupervised and uses visual similarity to detect patterns at scale.
• MinerEye claims identification throughput of up to 1TB of data per hour per single virtual machine.
• MinerEye DataTracker can integrate Office 365 end-user labeling by integrating with Microsoft Azure Information Protection.

• “A very significant portion of critical corporate data resides in what commonly is named ‘unstructured data stores’.”
• “Having 100’s of regular exports from the various SAP systems in place is the norm, not the exception.”
• “Unfortunately, the vast majority of organizations still are not in control of unstructured data.”
• “The focus is on cross-system management of identities and access, not on the level of granularity required for environments such as the ones named above.”
• “We consider Data Governance as a mandatory element in a comprehensive IAM and Data Protection strategy, complementing IAG and other core components of IAM.”
• “MinerEye names this approach ‘AI Powered Information Governance’.”
• “MinerEye DataTracker focuses on the third aspect, which is the hardest to solve: Analyzing data, classifying it, and provide the information that is required to track the use of such data.”
• “It does so by a three-step approach of identification, classification, and tracking.”
• “From our perspective, the approach of MinerEye to focus on identification and classification plus tagging instead of automated actions is focused and thus straightforward.”
• “Overall, MinerEye DataTracker counts amongst the promising candidates in the still emerging field of Data Governance, taking a focused and well-thought-out approach that integrates well with a variety of solutions used for Information Management, Information Governance, and Information Security.”