Navigating Data Lake Challenges: Governance, Security, and GDPR Compliance

Data lakes are becoming a crucial strategy for data management, with 62% of organizations in the production, proof-of-concept or pilot stage with their data lakes. Another 25% of organizations indicate they plan to implement a data lake within three years, according to 451 Research. Data lakes enable organizations to store and analyze diverse data from multiple applications with unmatched flexibility. But that flexibility also makes it much harder to find specific data across applications and support governance and GDPR compliance. A single Right to Be Forgotten request can require searching multiple databases while using extensive compute resources. Using data effectively is even more challenging when organizations land their data in an unstructured form without creating the infrastructure to manage that data down the road. Here are a few areas to focus on to make a data lake more useful.

Data Lake Challenges

• Governance: Despite the flexibility of data lakes, governance is still crucial for efficient data management. You can’t just toss all your clothes into a closet and expect to find your favorite shirt instantly. In this sense, structured data needs proper organization. Implementing governance measures allows for features like Time Travel to compare older versions while ensuring database compliance with ACID principles.

• Security: Cloud-based data lakes alleviate some of the security concerns with older on-premises Hadoop solutions which required frequent maintenance and updates. Cloud service providers have large teams focused on data security and access controls, meaning their customers benefit from enhanced protection against data leaks and unauthorized access. Managing responsibilities like access rights, logging and data backups are shifted to the cloud service provider, reducing the burden on companies to process these requests.

• GDPR Compliance: For many companies, GDPR compliance is focused on fulfilling Right to Be Forgotten requests. Identifying all information associated with a particular individual can be a daunting task. Instead of manual searches across multiple databases, a metadata catalog provides a more efficient approach. These catalogs enable data teams to access comprehensive metadata and quickly locate relevant information, simplifying compliance with data deletion requests.

• Analytics: Data lakes have a significant advantage over older technologies with their ability to combine and analyze diverse data sets simultaneously. Combining data from sources such as Salesforce, AWS Dynamo DB and Marketo in one place offers unique insights. However, to achieve meaningful analysis, organizations require a metadata catalog, such as AWS Glue Data Catalog, or similar offerings from Google or Microsoft. These catalogs organize unstructured data within the data lake, making it much easier to combine relevant datasets in a meaningful way without extensive data processing. 

• Access Control: Access control is crucial to ensure data confidentiality and compliance with privacy regulations. While analysts may require access to customer data for specific projects, they should only access relevant information for that research, not all of a customer’s data. Metadata catalogs play a pivotal role in controlling access, allowing organizations to provide access to necessary data while protecting sensitive information. By enabling aggregated usage of personally identifiable information (PII), companies can strike a balance between data accessibility and privacy.

Leveraging Data Catalogs for Optimal Data Lake Management

I mentioned how a robust data catalog can be the most effective solution to handle GDPR, analytics and access challenges. A data catalog provides valuable metadata insights into user access history, tracking the most recent information and enabling efficient troubleshooting. I’ve seen many organizations that have recently adopted data lakes skimp when it comes to deploying a data catalog, and this just causes issues down the road. 

A data catalog will add an incremental cost in terms of logging, but when it comes time to utilize disparate data sources or identify specific records across applications, a catalog makes a data lake an order of magnitude more useful. The small incremental cost of implementing a data catalog yields significant benefits and value in terms of time saved, especially for handling individual user record retrieval after data deletion requests and managing customer data from multiple applications.

Data lakes offer significant advantages in data management, enabling organizations to flexibly store and analyze diverse data. However, organizations must prioritize key areas such as governance, security, GDPR compliance, analytics, and access control to overcome the challenges associated with data lakes. Deploying a robust data catalog provides invaluable metadata insights, simplifying compliance with data deletion requests and facilitating efficient data discovery. By embracing data catalogs, organizations can fully harness the power of their data lakes, unlocking valuable insights while ensuring security, compliance and efficient data management.

About the Author

Coral Trivedi is a Product Manager at Fivetran where she works on making access to data as simple and reliable as electricity by building out Data Lake and Cloud Data Warehouse destinations.

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