From Rows to Records

The Evolution and Governance of Structured Data

Executive Summary

By understanding the history and context of structured data systems, professionals can better address challenges around integration, privacy, retention, integrity, and compliance.

Evolution of Structured Data Systems: Structured data management has its roots in early record-keeping and computing. The rise of relational databases in the 1970s revolutionized how organizations store and retrieve data, introducing the table-based schema that separated logical data models from physical storage. Relational Database Management Systems (RDBMS) became dominant through the 1980s and 1990s, largely replacing earlier hierarchical and network databases. This history set the stage for today’s data-driven enterprises.

• Impact of Relational Databases: The relational model (proposed by E. F. Codd in 1970) brought unprecedented flexibility and efficiency in data storage. By storing each fact once and using Structured Query Language (SQL) to retrieve and join data, organizations could ensure consistency and answer complex queries efficiently. Commercial relational databases (like Oracle, DB2, SQL Server) proliferated from the late 1970s onward, transforming business operations, enabling transaction processing, and underpinning enterprise information systems for decades. RDBMS technology became a foundation for records and information governance as businesses relied on these systems to manage structured records (e.g. financial data, customer information).

• Emergence of New Data Paradigms: By the 2000s, the explosion of web and sensor data (“big data”) brought new challenges. Traditional SQL databases, designed for single-server environments, struggled to scale to internet-era volumes. This led to the rise of NoSQL databases in the late 2000s, which sacrificed some relational features for distributed scalability (examples include Google BigTable, Hadoop/HDFS, Cassandra). More recently, distributed SQL databases have emerged to combine the scalability of NoSQL with the guarantees of relational systems. These developments diversify the structured data landscape, creating a more complex environment for information governance.

• Key Challenges Today: Organizations now face several critical challenges in managing structured data as part of their records and information governance programs:

  • Integration with Unstructured Data: Business information exists not only in structured databases but also in unstructured forms (documents, emails, multimedia). Aligning governance for structured and unstructured information is difficult, as each has different formats and management tools. Many companies have treated them in silos – for example, applying records retention rules to documents separately from databases. This disconnect leads to gaps in oversight.

  • Data Privacy and Security: Structured data systems often contain sensitive or personally identifiable information (PII), which must be protected by data privacy regulations like GDPR, HIPAA, and others. Ensuring database security (access controls, encryption) and meeting privacy regulations (like GDPR’s “right to be forgotten”) in structured datasets is a complex task. A significant portion of over-retained records contain personal data, exposing organizations to privacy risk if not properly secured and disposed. Identifying all instances of personal data across numerous databases and guaranteeing proper deletion on request remains a major challenge.

  • Retention and Disposal: Implementing retention schedules and defensible disposal for structured data is notoriously challenging. Unlike files in a content repository, database records are interlinked and continuously updated, making it hard to decide “what constitutes a record” and when it can be deleted. Many organizations retain structured data indefinitely by default, leading to bloat and non-compliance with records retention laws. Defining retention rules for database rows or fields – and executing disposition without breaking data integrity – requires careful planning.

  • Data Integrity: Maintaining the integrity of structured data means keeping it accurate, complete, and unaltered over time. This is vital for records serving as evidence of business activities. However, data integrity can be compromised by unauthorized changes, lack of audit trails, or poorly managed migrations. Ensuring that systems log alterations and prevent tampering is essential. Integrity also involves preserving relationships between data – for instance, if data is archived or migrated, referential links must remain intact so that the information retains its context and meaning.

  • Evolving Regulatory Compliance: Regulatory expectations around data management are growing. Industries such as finance and healthcare have strict recordkeeping and retention requirements, and new privacy laws globally mandate data minimization and timely deletion. Regulators have begun enforcing penalties for failures in data retention and protection – for example, firms have been fined for not preserving business-related messages in databases and for not safeguarding personal data during disposal. Keeping structured data practices in line with the latest laws (from GDPR to sector-specific rules) is an ongoing concern for compliance and records officers.

• Forward-Looking Best Practices: To address these challenges, organizations are adopting modern information governance strategies:

  • Develop a unified governance framework that encompasses both structured and unstructured data, breaking down organizational silos. This includes clearly defining data vs. content and ensuring “records” are identified in databases as well as in document repositories.

  • Invest in data discovery and classification tools to inventory what data exists in databases, identify sensitive information, and apply the correct retention and protection policies. Understanding your data is a prerequisite to governing it.

  • Automate retention and disposal for structured data wherever possible. Emerging solutions like structured data archiving tools allow organizations to move aging data into archives and delete it according to policy while maintaining audit trails. Automating these processes (with appropriate oversight) reduces error and ensures consistency.

  • Embed privacy and security by design into structured data systems. This means enforcing role-based access, encryption, and data masking in databases holding personal data. It also means building capabilities to quickly execute deletion requests and verify that data is truly removed across all backups and systems.

  • Maintain rigorous data integrity controls – such as audit logs that track changes to records, integrity constraints in databases (to prevent orphaned or invalid data), and regular integrity audits. Ensuring that records are protected from unauthorized alteration is critical to trustworthiness.

  • Form a cross-functional information governance team (including records managers, IT/data architects, compliance officers, and legal counsel) to develop and oversee structured data policies. Collaborative governance helps balance business needs with legal requirements.

  • Stay proactive about regulatory changes: monitor new laws and update policies accordingly. For example, if retention period limits or new data deletion mandates are introduced, incorporate them into the governance program promptly. Regular training and awareness programs can keep staff informed of compliance obligations related to data.

• Conclusion: Structured data systems have evolved from simple beginnings to sophisticated, distributed platforms that are integral to business operations. With that evolution comes a responsibility for records and information professionals to tackle the unique governance issues posed by structured data. By understanding the history and context of these systems, professionals can better address current challenges around integration, privacy, retention, integrity, and compliance. Moving forward, adopting comprehensive information governance practices and leveraging modern tools will be key to ensuring that structured data remains an asset rather than a liability. Organizations that successfully govern their structured data will not only mitigate risk and meet compliance obligations but also unlock greater value from their information assets.

Historical Overview: From Early Databases to Relational Revolution