We live in a world where big data is becoming an integral part of decision-making processes, but managing a data lake is no small task. How can your organization ensure that it is properly governed to produce reliable insights?
A data lake contains a wide range of raw and unstructured datasets collected from multiple sources. In order for insights gained from this information to be trusted, there must be an effective governance process in place. This means that steps should be taken to guarantee the data's security, privacy, and accuracy.
Even though implementing sound data governance is critical for maintaining data integrity, it can be challenging. Here are five key challenges you must consider when creating and managing an effective data lake governance strategy.
Creating Robust Security & Privacy Protocols
When it comes to a data lake, security protocols must be put in place from the very beginning. Data stored in a data lake can be vulnerable if there isn't enough emphasis placed on creating secure systems for storing and accessing information. Mindex adheres to stringent privacy and security regulations as an AWS cloud service provider working with companies in the healthcare and financial services industries. We also personally know a thing or two about robust security protocols because we have to follow rigorous New York State encryption requirements with our own product, SchoolTool, a student management system that handles student data.
Establishing Proper Access Controls
With greater access to data, companies must also implement access controls so that only authorized personnel can view/edit certain parts of the data stored within a data lake. This ensures that strategic and intellectual property are protected from unauthorized access and misuse. Additionally, access controls help organizations comply with internal policies as well as regulatory requirements such as GDPR or HIPAA (Health Insurance Portability and Accountability Act).
Building an Efficient Classification System
Developing an efficient classification system is essential when dealing with increasing amounts of complex data sources. Why is this important? If you don't categorize this disparate set of structured and unstructured data sources uniformly upfront, you'll waste time manually matching up all the fields when you query it during analysis. As a result of inconsistent record formats or business definitions variability across different files, teams often face significant delays when interpreting datasets, making ingesting content for predictive analysis more difficult for analysts or machine learning algorithms. Sounds like a headache, right?
Monitoring Processes and Quality Assurance Across Data Sources
You may need to retrain or build new habits for quality assurance with your organization's data. We know it can be a burden, but we have seen the negative consequences time and again. In order to ensure quality assurance and trust in their systems' accuracy, companies must track and monitor data collection and cleansing processes. This entails reviewing a wide range of processes, such as capturing and cleansing data. By actively monitoring these processes, teams can gain confidence in their solutions and continuously optimize them to meet their expectations.
Maintaining Traceability Back To Original Name
Without robust data management systems and processes in place, organizations struggle to keep track of the source of information—which is important to ensure that data remains accurate, consistent, and easy to locate if it has been modified (such as changes to the name, format, or contents of a file) or moved over time.
Similar to supply chain management and the idea of tracking the origin of a product to ensure it is manufactured and distributed in compliance with regulations (or that the product meets quality and safety standards), maintaining traceability back to the original name is an important process in many different fields, as it helps to ensure that data is properly managed, and that its origins can be easily traced for reference or regulatory purposes.
TRYING TO ACHIEVE YOUR BUSINESS GOALS WITH DATA LAKE GOVERNANCE?
At Mindex, we understand that data lake governance is a daunting task, so we have developed a data lakehouse approach that simplifies it. We can work with you to develop a governance plan that allows you to spend more time harnessing the power of your data rather than dealing with all these governance challenges.