Demystifying Datalakes: Harnessing metadata for effective Data Governance and GDPR compliance

Demystifying Datalakes: Harnessing metadata for effective Data Governance and GDPR compliance

Have you ever wondered how to build an efficient datalake and how it is transforming the world of personal data management and governance? In this article I am exploring the crucial aspects of data governance and its implications for GDPR compliance. As we delve into the key components of a strong datalake foundation, we’ll uncover the importance of metadata in driving effective access management. Join me on this journey to unlock the potential of your datalake while maintaining data integrity and privacy.

So, imagine you’ve built your datalake, finalised the architecture, and made the crucial decisions choosing the platform and tech stack: AWS, Azure, or GCP; Talend or Azure Data Factory; Snowflake; Databricks. But have you thought about data governance? Are there any potential GDPR considerations you should be aware of?

The philosophy behind a datalake is to gather massive amounts of raw data into a central storage location and defer decisions on how to use that data until later. However, it’s important to align with GDPR regulations, which dictate that personal data can only be used for the purposes explicitly agreed upon during data collection. What about your confidential data; should anyone in the company have unrestricted access to confidential data within the datalake?

Even basic aspects demand attention. For instance, it’s essential to clearly define what each data field means. If a field named “Status” with values ranging from1 to 4, it is important to document precisely what a status of 1 signifies. This clarity ensures consistent interpretation and use of data throughout the datalake environment.

A successful datalake needs a solid foundation, to mix a metaphor. Let’s dive into the key components that contribute to this strong foundation:

Data Dictionary

A Data Dictionary is like the encyclopaedia of your data. It formally defines and describes each data field, including its type (like text, number, or date), size (e.g., maximum 20 characters), and whether it’s mandatory or optional. Sometimes character fields contain dates, so we need to document the format, e.g., the ISO standard YYYY-MM-DD. This documentation ensures everyone understands the data within the datalake consistently.

Data Modelling

Data modelling helps you visualize how data is structured and the relationships between different data elements. By using effective data modelling techniques, companies can gain insights into how various pieces of data are logically connected. This understanding paves the way for efficient data analysis and decision-making.

Data Lineage

Data lineage tells you where your data comes from and enables you to trace it back to its source. It plays a vital role in verifying data accuracy and identifying any potential issues like corruption or mislabelling that may have occurred during the data’s journey. Data lineage provides transparency and ensures data integrity within the datalake.

Data Quality

Data quality is crucial for reliable decision-making. It covers factors like data completeness, accuracy, and currency. Poor data quality can have a significant impact on the quality of decisions made based on that data. Implementing measures to ensure data quality guarantees that the data within your datalake is trustworthy and suitable for its intended purpose.

Security and Data Access Management

To ensure the security of data, it is essential to encrypt data at rest, stored within the database, and during transit using encrypted protocols. But how do we make sure that access to the data is restricted according to company policies? Managing data access according to company policies is equally vital. By defining access controls based on roles, department affiliations, or other criteria, organisations can enforce proper data access restrictions and maintain data privacy.

Now, let’s talk about metadata and how it can help us manage who gets access to our data. Metadata is like a magic ingredient that provides valuable information about the data we have. It’s essential to assign a “data owner” who can determine things like which fields contain confidential data, and which fields contain Personal Identifiable Information (PII) that need separate permissions. This helps us keep track of who should be able to see what.

So, picture this: we have Sandeep, a marketing manager. He should have clearance to access any data up to a confidentiality level of 3. Then we have Sally, a developer, doesn’t require access to real Personally Identifiable Information (PII) data; she can do her job with some test data. And finally, we have Claire, a finance manager. She specifically requires access to the management accounts. Here comes the big question: how can we use the metadata from our data dictionary to control who can access the data based on their department, job position, or other factors?

Here, we face a tricky challenge. Do we want to setup access rules separately for each downstream visualisation tool? Will this approach lead to inconsistencies and errors, and potentially undermine the foundation of the datalake? Or is there a way to control this centrally, with the datalake serving as the single point of control for data access management?

Also, we must consider the impact of data caching on our master plan. Does data caching jeopardise the dream of using metadata to drive access management? While caching can speed things up and make everything run smoothly, but it also brings some complications for data governance. What if the results of Sandeep’s query are cached and then served to someone who does not have the right clearance? Caching outdated or inconsistent data could compromise the accuracy of analyses and decisions made based on the data. Finding the right balance between the benefits of caching and the need for data governance in the datalake environment becomes a critical.

So, the deal is finding the balance. We have to explore innovative solutions that let us use metadata to manage access while keeping everything consistent, accurate, and secure in our datalake. Using metadata gives us fine-grained control over who can see what, based on their roles, confidentiality levels, and other factors. Let’s embrace these principles, and your datalake will become a powerful asset, empowering your company to make informed decisions and drive success in the data-driven era.