Join our webinar on democratizing data in the cloud with Forrester, Snowflake and TIAA - Sign up hereStart Now
Published on September 20, 2021 AUTHOR - Privacy Research Team
Data Governance (DG) is simply a process to manage data availability, usability, integrity, and security in enterprise systems. Data Governance is driven by an organization’s internal data management standards and policies. Effective Data Governance translates into consistent, trustworthy, and secure data.
Modern organizations are increasingly relying on data analytics to guide business decisions. To quickly process data for analytics, organizations are utilizing applications like Snowflake. With its unique architecture, Snowflake can handle nearly unlimited concurrent workloads, helping organizations run complex queries quickly and securely.
However, with the massive amount of data running in Snowflake, organizations need to ensure robust data governance controls are implemented to ensure data integrity and security.
Snowflake has several data governance controls embedded in the application. For example:
Data Governance is difficult to implement because it requires people to change existing data management practices. The best way to tackle change resistance is to educate all stakeholders on why Data Governance is essential, how it benefits them, and the entire organization. Educating stakeholders helps them understand why they need to change.
This is the most challenging yet essential step to ensure effective data governance. It involves scanning all the data sets in Snowflake to discover personal and sensitive personal data. Next, personal and sensitive personal data needs to be classified into relevant categories. For example, first and last names of an individual may be classified as personal data, but an individual’s social security numbers may be considered sensitive personal data and thus requires restricted access to that data.
The next step is to identify all the roles with data access rights such as read, write, delete, or share. It is important to develop a granular-level understanding of each role’s tasks and responsibilities to develop adequate data governance controls. For example, a data architect will require different privileges from a data engineer. More on this in the next step.
This step will require the most work. The Data governance team will need to collaborate with every team that has access to the Snowflake data cloud. The primary objective is to develop policies that provide the highest level of security, and at the same time, allow each user adequate access to complete their daily tasks. Coming back to our example, Tom is an HR data analyst and Jessica is a payroll processor working in the same department.
The Snowflake Data Architect will understand Tom and Jessica’s roles and responsibilities, and set individual role-based policies. The global data access policies will be set up such that Tom can only see personal data of employees like their names, phone numbers and addresses. Jessica, on the other hand, will have access to sensitive personal data like SSNs and bank account numbers.
It is essential to measure the impact of Data governance policies by tracking a set of KPIs. Some of these KPIs might be the rate of adoption, the number of data issues/events, the overall cost of the program for data rectification and issue resolution.
Like any process, Data governance takes multiple iterations to perfect. It is a continuous process with a strong focus on feedback-based improvements. As new data streams join in, data volumes grow, and new access points emerge, the Data governance process will be used to come up with new policies to address new challenges. Therefore, it is important to keep it adaptable.
Data governance teams also need a thorough review process that constantly assesses governance policy enforcement and effectiveness. The results of the review process will help formulate improvements.
Securiti’s solution for Snowflake has a set of features that enable Data Governance in one-window. You can think of Securiti as a solution that enhances the security of the Snowflake Data Cloud. With Securiti, data architects can:
Users can also set up customized workflows to automatically fix any security misconfigurations. This helps strengthen data security as well.
PO Box 13039,
Coyote CA 95013