In today’s data-driven world, enterprises collect and process staggering amounts of personal and sensitive data. With regulations like GDPR, CCPA, and HIPAA making data protection a top priority, organizations must maintain tight control over every byte of personal data they store. However, many companies still face difficulties uncovering “shadow data” — hidden or forgotten datasets not covered by standard data protection practices. This is where Data Protection Officers (DPOs) become crucial. One compelling example comes from several organizations that struggled to pass internal audits—until they adopted BigID, a data intelligence platform designed to help companies discover and control their data assets, including shadow data sources.
TLDR
DPOs in several organizations faced repeated failures in internal data compliance audits due to the presence of unmonitored shadow data sources. Traditional tools and manual audits fell short in identifying these hidden datasets. Once they implemented BigID, the platform’s automated discovery and classification capabilities exposed previously unknown structured and unstructured data stores. This drastically improved data visibility, compliance, and overall data governance posture.
What Are Shadow Data Sources?
Shadow data sources refer to datasets that exist outside of a company’s official IT systems or data governance policies. These may arise from:
- Old databases forgotten over time
- Data stored in cloud services by non-IT teams
- Temporary backups kept for projects that were never purged
- Spreadsheets sitting on personal or shared drives
While most businesses have robust mechanisms to manage their official data repositories, shadow data sources often remain outside of these controls. Their existence represents a serious blind spot, especially concerning data privacy laws.
When Internal Audits Aren’t Enough
Many DPOs rely on internal audits to assess their organization’s data compliance and risk exposure. However, audits are only as effective as the data they can see. In several high-profile cases, organizations found themselves failing internal audits, not because of data misuse, but due to unknown data harboring within forgotten systems.
These audits revealed that:
- Existing data governance tools had limited visibility
- Manual reporting failed to catch orphaned repositories
- IT teams were unaware of the full data inventory
As one DPO from a global retail organization phrased it, “We were constantly caught off guard—our audit reports kept flagging gaps that simply shouldn’t have existed.”
Enter BigID: A New Approach to Data Discovery
After repeated failed audit attempts, several companies turned to BigID. Designed with privacy and security at its core, BigID uses machine learning and automation to locate, classify, and manage data—across structured and unstructured sources, on-premise and in the cloud.
BigID’s key features that proved vital to DPOs included:
- Data Discovery: Automatically scans the entire data landscape for structured, unstructured, and semi-structured data sources
- Data Classification: Identifies sensitive and personal data such as names, addresses, passport numbers, and more
- Correlation & Context: Links data across systems to users based on relationships and context
- Policy Enforcement: Supports setting retention, deletion, and access policies based on regulatory or organizational needs
One of the platform’s standout capabilities was its use of AI and advanced pattern recognition. This allowed DPOs to identify shadow data in forgotten databases, legacy platforms, and random cloud buckets—sources that manual processes had missed for years.
A Case Study: From Failure to Full Compliance
Consider the case of a mid-sized healthcare provider based in Europe. The organization underwent three consecutive failed internal audits. Despite having a dedicated privacy team and standardized governance framework, hidden data sources persisted.
After implementing BigID, the company made several important discoveries:
- Three legacy SQL databases used in prior patient intake processes
- An Amazon S3 bucket owned by the marketing team with patient engagement surveys
- Multiple Excel files stored across employee desktops with patient contact lists
These shadow data sources contained highly sensitive information, yet had been forgotten and therefore never secured or accounted for. Within weeks of deploying BigID, the DPO team was able to:
- Map all personal data sources across business units
- Apply access controls and retention policies
- Conduct successful follow-up audits with zero critical findings
This transformation not only improved compliance but also boosted executive confidence in the data governance program.
Why Shadow Data Often Goes Undetected
Understanding how shadow data surfaces and remains hidden is essential to preventing it. Common reasons include:
- Lack of centralized visibility: Different teams use different tools, and data gets siloed.
- Human oversight: Employees might generate temporary reports or storage without going through IT approval.
- Outdated infrastructure: Older systems get overlooked during migrations or upgrades.
- Cloud sprawl: As companies rapidly adopt SaaS platforms, unmonitored data growth becomes inevitable.
By the time audits uncover these lapses, remediation often feels reactive and rushed. For DPOs trying to lead compliance proactively, these blind spots can be daunting.
Key Results After Deploying BigID
An analysis of 10 organizations across finance, retail, education, and healthcare sectors revealed these measurable results after BigID implementation:
- 45% Increase in discovered data assets within the first 30 days
- 60% Reduction in unidentified data sources in quarterly audits
- 70% Faster policy enforcement and data subject request fulfillment
- 100% Pass rate for privacy and compliance audits post-deployment
Moreover, DPOs reported enhanced collaboration between legal, compliance, and IT departments thanks to unified visibility and actionable insights provided by BigID dashboards.
Lessons Learned For Data Privacy Teams
The experiences of these DPOs highlight several lessons that other organizations can take to heart:
- Don’t assume your current tools cover everything. Regularly evaluate your data discovery capabilities.
- Embrace automation. Manual discovery can’t keep up with modern data proliferation.
- Prioritize visibility. You can’t protect what you don’t know exists.
- Ensure business-wide collaboration. Shadow data often originates outside of IT.
- Act preemptively with tools like BigID to identify and eliminate data risks early on.
Conclusion
In a world increasingly regulated and reliant on ethical data stewardship, shadow data is one of the most significant threats to compliance and security. DPOs play a pivotal role in preventing data breaches and false audit reporting, but only if they have the right visibility and tools at their disposal.
As the experiences of these DPOs demonstrate, even the most well-intentioned data governance programs can fall short without full-spectrum data discovery. BigID filled that gap — offering thoughtful automation, tailored data classification, and powerful insights that turned audit failures into success stories.
Ultimately, the solution to shadow data isn’t more policy — it’s better technology that enables DPOs to truly see and understand all the data their organizations hold. BigID has proven itself an indispensable ally in achieving that goal.