Data Governance and DevOps

In the age of information, "data is treasure." With trillions of datasets encapsulating the world, data is fragile. Safeguarding data is imperative, and data governance ensures data is managed, safe, and in compliance.

Data Governance

Data governance overlooks data. It lists down processes that set policies, ensure availability, security, integrity, and schedule performance metrics. Data governance is crucial as it lays down the foundation that supervises and administers data. The heart of data governance is “Data Policy and Compliance.” 

Data policy drives data in an organization, and it is a document that sets standards for the data. Data policy and compliance documents talk about the following:

1. Scope of the policy
2. Teams responsible
3. Data quality and integrity checks
4. Data security in place
5. Data usage and access

A data policy document lays down the data foundation for an organization. It describes:

• How far the range of the policy extends and what it covers.
• The teams involved in managing, working, and overlooking the data. It narrows down the people who will be dealing with the data, creating an enclosed environment for the data.
• Two of the most important aspects of data are correctness and integrity. Data correctness ensures there is no discrepancy in data, and data integrity ensures data in use does not contain any personal or sensitive information. Both aspects are fragile, and deviation in either could have a significant impact.
• Securing the data is equally important. A data policy document includes the necessary guidelines to implement security measures, mitigation plans, and encryption of data at rest and in transit. It also sets data breach guidelines and schedules, plans for data backup and recovery.
• Data usage and access can be considered as an extended part of data integrity and security. But they are an important aspect of data. What the data will be used for, and how, is important. Setting access policies can strengthen the security around data.

DevOps and Data Governance

As data governance holds significant value for a data project, a DevOps mindset can bring about an increase in efficiency to the data governance process. DevOps is big on streamlining and automation, which puts together the processes and decreases the need for manual intervention.

Data governance has two technical processes whose automation can bring remarkable benefits:

1. Data correctness and integrity involve checking the precision of the data and ensuring no sensitive information is present. It can be a part of the ETL pipeline.
   • ETL stands for Extraction, Transformation, and Loading and is an automated way of addressing data pre-processing steps. After the extraction of data, data cleaning can be implemented, which fixes inaccurate data and empty columns. Pandas library can be used to clean data.
   • A Python library such as Faker can be used to replace sensitive information with random data masking personal information.
   • An ETL pipeline using a CI/CD tool like Jenkins can cut down on manual intervention and seamlessly run on schedule to fetch data, check correctness, maintain integrity, and load the transformed data onto the data storage solution in an automated manner.
2. Data security can be broken down into two sub-processes:
   1. Access management on data storage platform: Access management automation depends on the platform the data storage resides in. For instance, a data warehouse solution such as Amazon Redshift or a data lake like Azure Data Lake Storage, since on cloud platforms can be automated with an Infrastructure as Code (IaC) solution like Terraform.
      For standalone SaaS applications, APIs can be used using a programming language like Python.
   2. Data scalability: Scaling data can be made easy by implementing a CI/CD pipeline with an IaC like Terraform, Azure Bicep, or AWS CloudFormation. The pipeline can be divided into two aspects: one that monitors when a certain threshold is hit and the second part of the pipeline that scales the storage up. This pipeline can also be configured to accommodate scaling down as needed.

Conclusion

In a world running on data, data governance is crucial as it comprises a system that oversees and manages data. So, it naturally becomes imperative to build a DevOps mindset that could bring together the governance processes and streamline them with automation.