After reading about 23andMe’s recent data breach issue this morning, I’d like to share some thoughts on data governance from my perspective as a Data Automation Engineer.
Data drives just about every aspect of our world, and increasingly so as technology continues to evolve. A growing challenge with this is the art of managing and governing this ever-growing volume of data in a way that ensures the proper maintenance of its quality and security.
In my career as a data professional I have seen how data governance can slip into an afterthought, particularly in small or mid-sized organizations. Luckily, automated data governance can be a powerful tool to ensure data quality, security, and compliance without much effort.
Let’s take a moment to explore the idea and understand how it is poised to shape the future of data management.
What is data governance?
Where data analysis is the practice of collecting, transforming, visualizing, and utilizing data to inform decision making, data governance involves the establishment of policies, procedures, and controls to ensure data quality, integrity, privacy, and security. It’s about defining who has access to what data, how the data should be used, and maintaining compliance with regulatory requirements like GDPR, HIPAA, or other industry-specific standards that apply to your organization.
Traditionally, data governance has been a manual and resource-intensive process which is prone to human error. However, automation can streamline these processes with much more efficiency and effectiveness.
Before we move on, I would like to note that, as with all things involving automation, I do recommend including some element of regular human-led oversight to ensure everything functions as intended. If erroneous automations are left to their own devices and decide to go off the rails, whatever they control will quickly suffer from a lack of regular oversight.
Why should data governance be automated?
Keep pace with scale and complexity
As a business grows, so does their data. While it may seem fine to manage data policy in a smaller organization, this task quickly becomes unfeasible as a company expands both internally and externally; internally, with respect to access control and permissions monitoring, and externally, with respect to data volume and security.
Real-time monitoring
Automation allows you to monitor your data usage in real time, which in turn ensures more immediate responses to violations or discrepancies.
Improved consistency and efficiency
By implementing automation you are simultaneously ensuring the consistent application of your governance policies, reducing the error stemming from human-led governance.
Guaranteed compliance
Automated processes can be configured to comply with the latest data privacy regulations, making sure your compliance never lapses.
How can automation enhance data governance?
Quality monitoring and cleaning
Automations can be configured to monitor data quality continuously, so flagging and remediation of inconsistencies or bad data can be carried out even if you have a constant stream of incoming data.
Access control
Perhaps an obvious place to implement automation is with access rights and permissions. As an organization grows, it should not be granting access rights to individual users as it may have been done with a smaller team. Automations should be in place to manage access with user groups and predefined roles and rules.
Managing metadata
Using automated processes to extract and manage metadata can provide valuable insights into data lineage, usage, and relevance. For example, I implemented automated metadata management on a previous team using SharePoint Document Libraries. This helped us create the versioning, rollback, and artifact creation we needed to pass a CMMI Maturity Level 3 appraisal.
Policy enforcement
Governance policies, such as data retention policies and privacy configurations, can be automated using roles and rules as mentioned before. This allows your policies to be enforced consistently across the organization.
How to get started
1. Assess the playing field.
Conduct an in-depth analysis of your existing data to identify areas that can be automated. Common examples of areas may include those mentioned in the section above.
2. Choose your tools.
Select automation tools that align with your organization’s requirements to integrate seamlessly with your existing systems.
3. Implement and test.
Use the appropriate resources to design and implement automated data governance processes. Be sure to run a sufficient amount of testing to identify bugs and defects before putting your automations into a production environment. This will help you avoid many potential hiccups during your roll-out.
4. Train and integrate.
Train your team and communicate to the organization where appropriate on your new data governance processes. Educating your team is about how it works and who to reach out to for support will help things run smoothly after launch.
Wrapping up
Automating your data governance isn’t just a new trend in tech. It’s a necessity in today’s data-centric world. By learning how to leverage automation, you can ensure your organization maintains a high degree a data quality and compliance, paving the way for improved decision-making and sustainable growth. And in doing so, you’ll watch your data management transform into a seamless, efficient, and secure process.
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