In today’s data-driven world, organizations are increasingly recognizing the significance of protecting their valuable data assets. With the ever-growing amount of sensitive information being generated and stored, ensuring proper data access governance has become a critical priority. Data access governance refers to the processes and policies that govern the management and control of data access within an organization, safeguarding data from unauthorized access, breaches, and misuse.
To establish effective data access governance, organizations must adopt a proactive approach, incorporating best practices that mitigate risks, promote compliance, and foster a culture of data security. In this blog post, we will explore the top 10 data access governance best practices that can help organizations strengthen their data protection strategies and ensure the confidentiality, integrity, and availability of their data.
From establishing clear roles and responsibilities to implementing robust access controls and monitoring mechanisms, these best practices encompass a holistic approach to data access governance. Whether you are an organization looking to enhance your existing data security measures or an individual seeking to understand the fundamental principles of data access governance, this blog post will provide valuable insights and actionable guidance.
By following these best practices, organizations can minimize the potential for data breaches, internal threats, and compliance violations, ultimately fostering trust among stakeholders, protecting their brand reputation, and complying with regulatory requirements.
So, let’s delve into the top 10 data access governance best practices and discover how they can empower organizations to effectively manage and secure their data assets.
Best Practices for Data Access Governance
Below are 10 best practices for good data access governance.
1. Focus on Effective Communication
Successful data governance implementations rely heavily on effective communication. It is essential to communicate the “what” and “why” of the implementation to ensure understanding and buy-in from all employees, and to help them to make better decisions. Moreover, training line-level employees in effective communication can facilitate the sharing of important information upward. Using email templates and public mailing lists can provide individual employees with an easy means of bringing data governance issues to the surface.
2. Involve Stakeholders Early in the Process
It is essential to involve key stakeholders at the early stages of implementing data governance to ensure successful implementation. Engaging stakeholders means seeking their input and feedback on critical decisions and keeping them informed about the progress of the implementation. Stakeholders bring essential business knowledge to the table, and effective communication with them can help identify potential pitfalls.
3. Establish Sources of Truth Early On
Businesses often struggle with determining which data is reliable, which causes confusion and impedes effective decision-making. Data governance helps resolve this issue by establishing a single source of truth for each type of data. For example, this could mean deciding whether sales or accounting data is the source of truth for income amounts. Once these decisions are made, distributing data becomes simpler.
4. Understand That Data Governance is Not the Responsibility of Developers Alone
Data governance should not exclusively fall upon app developers. By recognizing that data governance is a shared responsibility, organizations can prioritize this crucial component of data management.
5. Select a Data Governance Framework and Stick to it
Data governance frameworks simplify communication across an organization. It is important to carefully consider the pros and cons of different frameworks and how they align with the unique needs of the business before making a final decision. Additionally, organizational training can help kick off the adoption of a new framework. Below are some of the most commonly used data governance frameworks:
- McKinsey framework
- Eckerson framework
- PwC enterprise data governance framework
- The SAS data governance framework
- The DGI data governance framework
6. Begin with Small Steps and Expand Incrementally
Start with key departments to focus on for initial implementation. If the early implementations are successful, it can help amass political and monetary capital to fund future phases. By learning from early implementations, the overall implementation process can be greatly improved.
7. Watch out for Perverse Incentives
It is crucial to curb perverse incentives in data governance to avoid any negative consequences. For example, incentives such as bonuses which encourage the concealment of data governance problems are particularly detrimental.
8. Develop Training Plans
Comprehensive training for all employees who interact with data is crucial. Often, the training phase is the most extended aspect of data governance, as it requires careful planning and execution. Businesses can engage experts to provide specialized training and speed-up the process.
9. Establish Standards from the Outset
It is crucial to establish a data governance framework and agree on standards at the beginning of any project. Without an early agreement on such frameworks and standards, planning and rollout may be delayed.
10. Avoid Overburdening Data Stewards
The role of data stewards is critical in safeguarding data. However, overloading data stewards with responsibilities can lead to mistakes and liability issues. Thus, it is important to distribute the duties among different departments.
If you’d like to see how the Lepide Data Security Platform can help to protect your systems and better govern access to sensitive data, schedule a demo with one of our engineers or start your free trial today.
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