Governance Challenges in Modern Data Platforms
Modern enterprise business strategies have become increasingly data-driven. With evolving data needs, enterprises have moved on from traditional data architectures like data warehouses and disparate siloes to more unified platforms for data management, like third-generation data lakes and data lakehouses.
However, with evolving architectures, enterprises also face challenges in effectively governing and managing their data assets in a secure, compliant way. Compliance gaps often become very expensive for large enterprises, scrubbing away millions of dollars in fines and reputation losses. To avoid this, enterprises must prioritize effective data governance practices to maintain the highest levels of security and privacy for their data assets.
This blog will explore key data governance challenges that enterprises face with modern data platforms and solutions that can enable them to navigate these issues with ease.
Increasing Data Complexity
Traditionally, data warehouses and hubs dealt with structured data, which was later processed for analytics and business intelligence. However, with third-generation data lakes and data lakehouses, the nature of data ingress quickly changes.
Data teams within enterprises are tasked to deal with a mix of
- Structured data residing in conventional databases,
- Semi-structured data in formats like JSON and XML,
- Large volumes of unstructured data from social media, customer interactions, audio messages, documents, spreadsheets, videos, etc.
Each type requires different storage, processing treatments, and governance approaches, which makes data management within unified data platforms much more complex.
Metadata Management and Cataloging Challenges
Knowing what data your enterprise owns becomes very important with data complexities increasing. When data teams consolidate disparate data siloes into a unified data platform, more often than not, they lose track of the nature of the data they own.
For effective data governance, enterprises need a robust data cataloging solution. This would allow data teams to:
- Discover and classify different types of data across the organization on ingestion
- Add metadata tags that include information on data lineage, sensitivity, privacy requirements, and usage needs
- Understand the context, origin, and composition of data and its compliance requirements
With a missing metadata management strategy, organizations often risk losing track of their sensitive data, compromising regulatory compliance and reducing the overall data utility and accessibility. On a bad day, challenges like these may cost your enterprise upwards of €20 million through GDPR fines alone.
Security and Privacy Challenges
Third-generation data lakes and data lakehouses face challenges across multiple security dimensions. It is crucial to control who accesses the data stored, what types of data can be accessed, and what data must be securely protected to prevent leakage.
Data Access Control
Enterprises need to enforce more granular role-based access controls, which can dynamically adjust access permissions based on the users’ roles and the sensitivity of the data they handle. Real-time monitoring and auditing of data access patterns must also be enforced to catch any anomalies.
Data Masking and Anonymization
Privacy regulations like GDPR, CCPA, and HIPAA mandate that enterprises handling sensitive data prevent unauthorized access and distribution. However, this complicates processes in non-production environments, as data teams often need to access sensitive files. Sensitive data like PII, PHI, and banking data must be identified and masked, while anonymized datasets must be created for analysis.
Data Quality and Consistency
As data sources multiply, maintaining data quality becomes critical. Enterprises must implement automated data validation processes across standard ingest and transformation pipelines. Data teams across departments must continuously monitor data ingress and egress to detect and rectify data anomalies.
Best Practices for Effective Data Governance
Develop a Comprehensive Data Governance Strategy
The first step in developing an effective data governance strategy is establishing cross-functional governance teams to oversee governance objectives and review governance frameworks regularly.
Foster a Data-First Culture
Through the established governance teams, provide regular training on data governance while actively encouraging data literacy and incentivizing proper data management practices across business units and departments within the organization.
Invest in Advanced Technology
AI integrations into modern data platforms have brought significant upsides. AI-based intelligent data classification techniques for automatic metadata detection and classification can significantly simplify metadata management and cataloging processes. With automated compliance and security tools, you can monitor governance and compliance risks and automatically take corrective action when needed.
Closing Thoughts
Maintaining effective governance practices is no longer optional–it’s become strategically imperative. Understanding and proactively addressing inherent governance gaps within your organization’s data management framework is a key first step. Effective data governance can turn it from a compliance burden into a competitive advantage.
As data continues to grow in volume, variety, and complexity, organizations that master these governance challenges will be best positioned to unlock the true potential of their data ecosystems.
