Saumya Gupta: “Legacy Data Must Be Treated as a Strategic Asset” — The Data Diva Talks Privacy Podcast

As enterprises race to adopt AI and scale up their digital capabilities, they’re collecting vast amounts of data. But not all of it is manifestly done. Beneath the surface of innovation lies a growing problem: legacy systems filled with outdated, untagged, and ungoverned data. This was the central theme of Episode 239 of The Data Diva Talks Privacy Podcast, where host Debbie Reynolds, also known as “The Data Diva,” sat down with Saumya Gupta, Assistant Vice President for APAC and Japan at Platform 3 Solutions. The conversation highlighted the hidden risks of legacy data, the power of metadata, and why businesses must reassess their approach to data lifecycle governance. So, how can businesses turn what’s hidden in the shadows into something purposeful and compliant? Let’s dive in.

The Invisible Threat: Why Legacy Systems Matter

Legacy systems often go unnoticed in modern discussions about privacy. Yet, as Saumya Gupta points out, these outdated repositories are home to some of the most sensitive data enterprises holding much of it retained far beyond its legal or functional lifespan.

“Legacy data isn’t just old. It’s unmanaged, unmonitored, and often outside the reach of compliance,” says Saumya.

These systems were not designed with modern privacy laws or digital risks in mind. As a result, they pose a significant threat, one that many organizations underestimate until it’s too late. Whether it’s customer information, financial data, or employee records, this “forgotten data” can become a liability in audits, breaches, or regulatory investigations.

Reimagining Governance with Data Lakes and Lakehouse’s

To confront this challenge, Saumya advocates for architectural modernization using data lakes and lake house systems. These solutions enable enterprises to:

• Centralize structured and unstructured data
• Tag datasets using consistent metadata standards
• Govern information based on sensitivity, regulation, and compliance.

By moving away from fragmented data silos and toward integrated platforms, organizations can begin to gain control over their digital footprint. Especially legacy data that has historical oversight.

“Data lakes give organizations a chance to bring order to chaos, and only if, they layer metadata and governance correctly,” Saumya notes.

The Metadata Framework: Five-Layer Model

One of the most impactful points is Saumya’s introduction of her Open Metadata Model, a five-layer system designed to help organizations classify and manage their data intelligently. Here’s how the model works:

• Technical Properties: What is the nature of the data? File type, format, location, etc.
• Business Relevance: How does it align with business objectives? Is it valuable or obsolete?
• Operational Quality: Is the data accurate, current, and reliable?
• Sensitivity: Does it include personal, confidential, or regulated information?
• Compliance Requirements: What legal, regulatory, or industry standards apply?

In this structured approach, data is transformed into a clearly defined entity, one that can be analyzed, secured, retained, or deleted based on criteria.

AI vs. Privacy: A Clash of Priorities

The conversation then dives into a modern dilemma: AI’s appetite for data vs. privacy’s principle of minimization. AI thrives on access to massive, diverse datasets to train smarter algorithms. But this creates tension with regulatory standards like GDPR, which emphasize collecting and retaining only what is necessary.

“Enterprises must resist the temptation to keep everything just in case,” warns Saumya. “AI shouldn’t be fed unmanaged, unverified, or outdated data.”

She emphasizes that without data hygiene, companies risk feeding biased, inaccurate, or even non-compliant data into their machine learning systems which leads to ethical lapses, regulatory violations, or flawed outputs. Tap into the Future of AI-Driven Insights with Smart Archiving, learn more.

Defensible Deletion: Turning Risk into Opportunity

One of the key strategies discussed is defensible deletion. The process of securely removing data that no longer holds business or legal value. Saumya urges organizations to treat deletion not as a loss, but as a gain.

• Reduced risk of data breaches or leaks
• Lower data storage and management costs
• Improved audit and regulatory posture
• Streamlined AI models based on relevant, clean data
• Greater trust with customers and partners

“Deletion, when governed properly, becomes a strategic move,” says Saumya. It helps enterprises proactively manage risk, avoid penalties, and uphold privacy commitments. Also read: Key Strategies for a Successful Data Migration

Building Privacy-First Infrastructure

Throughout the conversation, a consistent theme emerges: Privacy must be built into infrastructure. As regulatory landscapes evolve and consumers become more privacy-conscious, retrofitting compliance no longer works. Saumya calls for a privacy-by-design approach, one where data lifecycle, governance, metadata, and AI usage are aligned from the start. This means:

• Integrating metadata tagging into ingestion pipelines
• Embedding retention and deletion policies into storage systems
• Ensuring AI and analytics engines only access classified, compliant data
• Establishing audit trails for all data lifecycle actions

Reframing Legacy Data

One of Saumya’s most compelling insights is her reframing of legacy data, not just as a burden or cost center, but as a strategic reset point. By identifying, classifying, and governing this dormant data, organizations can:

• Clean up their compliance landscape
• Reduce attack surfaces for cybersecurity
• Improve decision-making with cleaner analytics
• Future-proof their digital infrastructure

“Legacy data is a signal. It tells you what went wrong, what needs control, and where you can start fresh.”

Conclusion

As enterprises lean into AI, data-driven insights, and cloud-first strategies, the temptation is to focus only on the new. But as this podcast episode reminds us, the real risk and opportunity lie in the old. Legacy systems, unclassified data, and metadata gaps represent one of the most important frontiers in data privacy today. Saumya Gupta’s insights provide a clear roadmap for organizations ready to take action to transform structured metadata, prioritize defensible deletion, and embed privacy into the core of your architecture. Start your secure data migration journey now. Talk to us; we’re here to help.     FAQ: