Self-service analytics and business intelligence (BI) are powerful tools that allow users to access and analyze data without the need for IT intervention. However, self-service platforms also introduce new risks and challenges for data governance. In this article, we'll explore how to implement data governance for self-service analytics and BI.
Common risks include data security, data quality, and data privacy. Data security is a major concern when users have direct access to data. Data quality can also suffer when users are allowed to manipulate data without proper oversight. Finally, data privacy is a concern when sensitive or personal information (PII) is stored in data warehouses or data lakes. This can be particularly important for companies like Snowflake, Redshift, Databricks, and others who need to make sure they are in compliance with HIPAA, the Financial Modernization Act, and other laws in place to protect the privacy of consumers.
To mitigate these risks, organizations need to put in place proper data governance controls. These controls should be designed to ensure that data is used appropriately and that unauthorized access is prevented. Data governance controls can be divided into three main categories:
This type of control limits who has access to data. It can be implemented through authorization mechanisms and authentication software such as Okta, Cyberark, AuthO, and Ping Identity.
This type of control ensures that data is of high quality. Quality control measures can include validation, data cleaning, and transformation processes.
This type of control tracks who accessed which data and when. Auditing and logging can help organizations identify misuse or unauthorized access to data.
In addition to these data governance controls, organizations should also consider implementing security controls. These controls can help prevent unauthorized access to data and protect data from being tampered with or stolen. Security controls can include encryption, access control lists, and firewalls.
Data governance for self-service platforms requires a balance between giving users the freedom to access and analyze data, and establishing controls to mitigate risks. By understanding the risks and challenges associated with self-service, and by implementing the proper data governance controls, organizations can enable self-service while still protecting their data.
To help with this process it’s very common for modern data stack users to use data catalog and data observability tools. Data catalogs can help organizations keep track of their data assets. Some of the most common data catalog tools in the modern data stack include Alation, Atlan, Collibra, and Metaphor. Data observability tools on the hand, can help you monitor and understand the data that flows through your system. For data observability, teams should consider Monte Carlo, Datafold, Bigeye, and Metaplane.
If you want to enable safe, reliable, self-service analytics and BI in your organization, data governance is essential. By implementing the tips we’ve outlined, you can create a foundation for users to find and trust the data they need to make better decisions. And that’s where ThoughtSpot comes in. Our AI-Powered Analytics solution provides instant answers to business questions by leveraging the power of natural language search and large language models.
Sign up for a free trial today and see how easy it is to get started on your own journey to self-service success.