Data collection is one of the most important functions that businesses perform. By obtaining data about your customers, employees, finances, and more, you can ensure easy, reliable access to information that helps guide major business decisions.
However, if your data becomes unreliable or invalid, it can pose a significant threat to your organization. Your employees won't have access to vital information that guides their day-to-day workflow. And, if the data becomes completely unusable, data collection will have to re-start from scratch.
This is where data governance plays a major role in organizing and protecting your internal data. It acts as a form of insurance that ensures every piece of information you collect is properly stored and distributed within your organization.
In this post, let's discuss what data governance is and how you can implement a policy at your company.
What Is Data Governance (DG)?
Data governance is a combination of individuals, technologies, and systems that work together to protect an organization's data. This ensures that data is accurate, comprehensive, and easily discoverable for employees. Businesses use data governance to safeguard their information and distribute data to employees who can use it most.
There are many types of data governance that fall under its overarching umbrella. Let's take a look at a few of these examples in the section below.
6 Data Governance Examples
1. Data Usability
If you want your employees to use your data, it needs to be accessible and easy to understand. Data should be stored in one location and organized in a simple, logical way. Additionally, every employee in your organization should understand what each piece of data means, how it's collected, and how to use it.
Metadata is qualitative information that describes the other data you've collected at your business. It helps your team understand why certain data is collected as well as it's relevance to their short- and long-term goals. This way, if data is ever misplaced or forgotten, you'll have context clues to explain the purpose of each dataset.
3. Data Security
While most of your data should be easily accessible, some information will be extremely private and only be viewed by specific employees. In this case, data security is essential to protecting data and deciding who should have access to it. This comes in handy particularly with information pertaining to payroll or finances.
4. Data Quality
One of the most important aspects of data governance is making sure your data is reliable and consistent. If not, your team may make misinformed decisions that end up costing your business a fortune. Consistently checking data for accuracy can filter out incorrect, out-of-date, or corrupted information.
5. Data Integration
Sometimes, data coming in from a variety of sources needs to be combined. In these cases, data integration groups this information into a larger dataset that provides meaningful insights about your business. By combining data together, you can obtain a clearer picture of how different functions relate to each other within your organization.
6. Data Preservation
Your company should have a process for deciding how data is stored and preserved. After all, some data is used constantly, while other information can be archived, or even deleted. This is where it helps to have a universal storage system to ensure pertinent data is never too hard to find.
There are also several data governance models that can be adopted based on your business needs and the types of data governance you use. Let's take a look at a few of these models in the section below.
4 Data Governance Models
1. Decentralized Execution for Individual
This model is perfect for an individual business owner who manages and maintains all of their data. In this model, the same individual who creates and sets up their data is typically the only one who uses it. We can see how this model plays out using the image below.
This model is also built for business owners who manage and maintain their master data. However, in this model, data is used and shared by several employees across different teams. This way, if your business has several offices or stores, you can ensure information is categorized and distributed to every person on your team. Check out how this model works using the image below.
In this model, either a business owner or multiple business leaders are in control of the master data. The individual or team control the creation and set up of data based on requests that come in from other departments. With this system, data is centralized to team leaders who can distribute information to employees as needed. This is effective for companies that have a lot of employees and need to regulate how information is shared internally.
4. Centralized Data Governance and Decentralized Execution
The final model combines different aspects of the systems listed above. In this model, there's an individual or team that controls the master data, but each team creates their own datasets to contribute information. This means that both management and team members are responsible for collecting and sharing internal data. This is great for larger businesses who are looking to streamline data to their management teams.
Now that we're familiar with what data governance is and how you can implement it, let's talk about some best practices to consider when creating a data governance framework.
5 Data Governance Best Practices
1. Start with a small sample size.
It's best not to kick off your data governance program with a complex or long-term project. You might make errors or lose motivation from the team. Rather, begin with a smaller, more manageable project, like analyzing data for one team. Assess the state of the data, specifically its collection, storage, and usage, then decide how much of your budget will be invested in the initiative.
2. Create a team dedicated to your program.
If you want your data governance to be effective, then you'll need to create roles dedicated to your program. The leader of the program should have strong communication skills and be able to communicate its importance to the rest of your company. Each person on the team should have clear responsibilities and ensure each data governance initiative runs smoothly and quickly.
3. Be transparent with external stakeholders.
You should always be transparent with your external stakeholders -- customers, partners, investors, suppliers, etc. -- about business functions and changes. In this case, they should all be made aware of your data governance program before you set it into place. You want your stakeholders to know that the security and validity of your company's data is a main priority.
4. Set clear goals for data governance.
Once you've implemented the new governance system, setting goals for your program will ensure its long-term success. These goals can include protecting top-level data, reducing friction between teams, decreasing the costs of data management, and creating a faster data entry process. Whatever your goal is, it should be actionable and include a roadmap to success.
5. Assess projects after completion.
After each data governance project is completed, you shouldn't merely pat yourself on the back and move on. After all, if the project wasn't successful in achieving your goals, it will need to be adapted for the next initiative. Run some tests on your data to note changes and discuss with your team what processes should be streamlined and which ones need to be readjusted.