According to data governance statistics, the global market size of data governance will reach a whopping $7.42 billion mark by 2026. What’s even more amazing is the fact that the market size of data governance stood at $2.62 billion in 2021 and $3.28 billion in 2022, which translates into a compound annual growth rate of about 24.9%
All these data points clearly show the exponential increase in the demand for data governance. Despite this, almost half (46%) of businesses don’t have a data usage strategy in place. Even businesses that have a data strategy fail to implement data governance because they follow the wrong tactics and techniques. If you are one of those businesses that are struggling to achieve success with data governance, Anti-Dos will help you solve this problem by telling you about seven data governance best practices.
Table of Contents
7 Data Governance Best Practices You Should Adopt Right Now
1. Clarify Who Is Responsible For What
2. Keep An Eye On Your Sensitive Data Assets
3. Monitor Security Controls of Critical Data
4. Integrate Governance Into Access Control
5. Keep Cost Under Control
6. Test The Effectiveness of Your Governance Framework
7. Establish An Accountability Mechanism
7 Data Governance Best Practices You Should Adopt Right Now
Here are seven data governance best practices you should right now.
1. Clarify Who Is Responsible For What
Before you do anything else, it is important to lay a solid foundation for data governance. Start off by focusing on who owns which data. Next, clearly outline responsibilities so that everyone knows their role and limitations. Doing this exercise will help you get a clearer picture of your data workflow, and different stages involved in the data lifecycle and gain a better understanding of the security posture. Once the foundation is laid, your team can function more smoothly and tackle data governance challenges more easily.
2. Keep An Eye On Your Sensitive Data Assets
Organizations have a large volume of data. Not all of it is critical. That is why it is important to classify and prioritize your data assets. For instance, your trade secrets are critical and should never fall into the wrong hands. Meanwhile, public information about your business is not that critical. With a clear distinction between sensitive and nonsensitive data, your security team can easily decide where to put their resources and which data to protect first.
Don’t forget to create an inventory of data assets. This will help you maintain visibility and control over your data. As a result, you don’t lose sight of critical data assets. This makes it tough for your threat actors to target it. Cybercriminals target data assets that are unprotected or left unattended for a long time.
3. Monitor Security Controls of Critical Data
Once you have an inventory of all the data assets, it is time to decide on security controls to put in place to secure sensitive business data. You can implement security policies and controls both at a data asset level or implement it at an organizational level. The more granular you go with security control implementation, the better. Since every data is different, you can not enforce one size fits all security policy. As the security landscape continues to evolve, make changes to security policies to combat the latest threats.
4. Integrate Governance Into Access Control
Instead of implementing data governance in isolation, it is better to fully integrate it into access control. By integrating governance principles into access control, you can cut down the cybersecurity risk of account takeovers and privilege escalation. Best DDoS protection services use these techniques to prevent business disruption. This will make life tough for cyber attackers as they won’t be able to move laterally through the network despite compromising a user account.
Whenever someone requests for data access, analyze the security risk and data sensitivity. This will help you decide which security guardrails you need to have in place in order to protect your data. Additionally, this will make it much easier for security teams to decide which users to grant permission and what level of permission should be granted.
5. Keep Cost Under Control
Did you know that a data governance program can cost businesses anywhere from $20 million to $50 million depending on the size of the organization? This happens because most businesses tend to opt for a traditional governance model. A traditional governance model not only has its limitations but it can also exponentially increase your overheads.
The best way to keep costs under control is to switch to an automated data governance framework. The difference could be night and day as automated models can accelerate the implementation process and reduce the risk of human errors while providing a real-time tracking facility. All of this can slash your business expenses.
6. Test The Effectiveness of Your Governance Framework
Just because everyone in your industry is using a particular governance framework does not mean you should too. Every business is different and so are their data needs and governance requirements. You need to choose a governance framework after taking all these factors into consideration.
Even when you have chosen a data governance framework, it is important to constantly evaluate its performance. If it is falling short of your business goals, you can consider adopting a different data governance framework. There is no point in sticking to a governance framework that is not delivering desired results.
7. Establish An Accountability Mechanism
Remember the first point of the article? Clarify who is responsible for what. Why do we do that? Because it also makes it easy for you to hold everyone accountable for their actions. If your organization is adopting a generic approach to data governance, then you can opt for a domain-subdomain model for accountability. Every domain or subdomain has a supervisor who is accountable for their department. On the flip side, if you believe in going deeper with data governance, you should choose an accountability mechanism that supports that model.
How do you implement data governance at your organization?
