7 Data Governance Best Practices for Effective Data Management
Data governance best practices allow you to manage the data in your systems in a way that best serves you and your customers. When you collect data from your customers and clients, it is your responsibility to use that data strategically and ethically. You have to organize it, give access to the different departments and partners of your business, analyze it, and utilize it.
There are many components of data governance (DG), all of which aim to make sure you get the most out of your data while honoring the trust of your customers. In short, data governance refers to the decisions made about customer data.
Why is Data Governance so Important?
Following data governance best practices is a good way to make sure you get the most out of the data you collect without compromising your integrity. Proper data governance ensures that you use customer data profitably and ethically.
The first main benefit of a proper data governance strategy is the elimination of data silos. Data silos occur when separate departments have access to different types of customer data—the types of customer data that are relevant to one specific department. Data silos should be avoided as much as possible because the best data analysis happens only when customer data is unified, therefore creating a consistent customer experience. We’ll look at data architecture more below.
Increased Revenue and Profits
Once data is organized, you can begin to use it effectively. This means preventing errors, lowering data management costs, and increasing access to necessary data. With good data comes good information and the ability to make better decisions (more on data quality later). All of this gives you a competitive advantage and leads to increased revenue and profits.
Compliance and Security
Finally, data governance requires that you develop internal processes, policies, and procedures that outline who has access to the data, what data you collect, when you use it, where you store it, how long you store it, how you use it, and why you are making these decisions.
Developing and enforcing these policies is important to running your business smoothly and profitably. It allows you to use data ethically and maintain trust with your customers while also complying with the data laws and regulations specific to your industry.
Who is Involved in the Data Governance Process?
A data governance team is responsible for creating internal policies and procedures for handling customer data. This team could be made up of a chief data officer, a DG manager and team, a DG or steering committee, data stewards, data architects, data modelers, and data quality analysts and engineers.
A DG or steering committee is responsible for enforcing and monitoring the developed policies and procedures. Data stewards are the people who handle the data regularly and carry out the agreed-upon policies and procedures.
Other parties who might be responsible for implementing and enforcing internal policies and procedures are data management teams, representatives from business operations, IT department, and executives.
Who are Data Stakeholders?
Anyone who has an interest in the data governance process (collecting, managing, processing, storing data, etc.) is referred to as a data stakeholder. The number of data stakeholders involved depends on the size of your business, the volume of data, and your own delegation.
According to the Data Governance Institute, small organizations may be able to succeed through an informal system of governance. Small organizations “may not even be aware of when they are switching between making management decisions and broader governance decisions.” Larger organizations, however, usually find that they need to agree upon a more formal system due to the scope and complexity of their data.
Regardless of the number of stakeholders, however, everyone has to agree upon a set of policies and procedures for handling data efficiently, safely, and securely. Here are 7 data governance best practices that ensure all components of the strategy are met:
7 Data Governance Best Practices
Communication and Cooperation
We talked above about data stakeholders—everyone involved from data collection to data retirement. No matter how many data stakeholders there are, each party has to agree on the internal policies, processes, and procedures and agree to abide by them and enforce them. Data governance requires cooperation among stakeholders and clear communication of expectations and consequences.
Your customers need to trust you. Therefore, they need to feel confident that the data you collect from them is safe, secure, and private. To determine whether you are making ethical decisions, consider these questions:
- Do my customers know what I’m using their data for?
- Do my customers have control over what data we are able to collect?
- Do my customers have opportunities to opt out?
- Do I have procedures in place for maintaining data privacy?
- Am I securing data against outside entities?
Risk Management and Data Security
Properly securing customer data maintains your customers’ trust, but it also protects you from financial loss and legal repercussion. Refer to the who, what, when, where, and why from above. A good data governance strategy allows you to use data effectively and efficiently without compromising the data or breaking compliance rules. This means you have to regulate who has access to what data when. Consider how you use the data and how long you keep it.
Education and Training
In order to meet data governance best practices 1-3, you need to make sure all of the data stakeholders are properly educated and trained. And regularly. This is the best way to ensure everyone involved abides by the developed policies and procedures to protect the business and the customers. Poor education and training can lead to compromised data, unnecessary risk, errors, inefficiency, and ineffective processes.
Architecture and Integration
At the heart of these data governance, decisions should be your desired business outcomes. This is the “why” aspect of data governance. All of the decisions you make regarding data architecture and integration should be made with your goals in mind. Developing procedures for measuring your success will inform future architecture and integration decisions.
Data architecture refers to the organization of data. We talked earlier about unifying data and avoiding data silos. That’s because the best data analysis happens when the data is unified, consistent, and correct. Without this, data integration will be difficult and not as effective.
Data governance best practices 1-5 will not matter much if you’re attempting to integrate bad data. It’s imperative that you clean your data to avoid inconsistencies, duplication, gaps, and other errors. These errors are much more difficult to identify and resolve when data is siloed. Fortunately, data governance helps unify the data in order to resolve data errors and assist with better decision-making based on good, clean, effective data.
When you collect and organize your data, where do you keep it? Who has access to it? How long do you keep it? Data storage procedures help ensure that the data stakeholders comply with the internal processes the DG team develops as well as data privacy and protection laws.
It’s also a good idea to document where data comes from, where it’s stored, and how it’s protected. You should track the lifecycle of the data you collect as it can become outdated, break agreements with your customers, and offend external regulations.
A good data governance strategy protects you and your customers while providing value. Data governance helps put and keep all data stakeholders on the same page when it comes to handling, managing, and monitoring data. Keeping your customers’ data safe and secure helps maintain their trust, and unifying the data improves analysis and therefore implementation.
Don’t get caught breaking data and privacy laws and regulations. Develop a strong data governance strategy to keep your business running smoothly, profitably, and safely.