Data governance has to do with people, processes, and technology. Those three elements have a big influence on the data you capture and the quality of that data. Data governance helps you define standards and processes so you can guarantee the highest data quality.
This post will explain the benefits of data governance and provide you with seven practices you should consider when implementing your data governance strategy.
What Is Data Governance?
At its core, data governance focuses on defining standards to ensure the highest data quality and data integrity. Data is the most valuable asset for your organization, and it’s key for making decisions. If you can’t trust your data, then you can’t make reliable data-driven decisions.
Therefore, data governance helps companies set standards for people, processes, and technology. On the people side, it helps with defining roles, responsibilities, and communication paths. On the process side, data governance focuses on creating templates and procedures. And of course, data governance is also concerned with technology—especially data capture and data validation.
Let’s further explore the many advantages of data governance.
Benefits of Data Governance
At its core, data governance focuses on ensuring the highest data quality possible. However, it can offer many more advantages as well. Let’s take a look:
- Minimize the risk of data errors.
- Reduce data inconsistency and duplicate data points.
- Define clear roles and responsibilities. You and your colleagues should know who’s capturing which data.
- Define a process that increases efficiency and reduces costs.
- Establish a clear procedure about who’s allowed to use data and how they’re allowed to use it.
The above benefits should help you change your mind about implementing a data governance strategy if you were wondering whether it’s worth the effort.
Now that we’ve established why this topic is worthwhile, let’s look at seven best practices for data governance.
7 Data Governance Practices You Need to Know
Here are some of the most important data governance practices. As you read, consider how they apply to you and your organization.
1. Assign Roles and Responsibilities
First, you must define ownership over the data governance process. If nobody knows who should do what and when, then your data governance implementation will likely fail.
The data owner is responsible for updating data in his or her specific data domain. This means multiple data owners can exist in one company, each for their data domain (customer data, for example). Also, the data owner decides who can access which parts of the data. In the end, the data owner knows the meaning of every data field and what format it should hold.
In contrast, the data steward is responsible for validating the data quality. He or she can do this by performing regular data quality checks. Often, the data steward will use metrics, such as the number of missing fields or the number of data duplicates, to calculate the data quality.
2. Use Templates to Help You Standardize Your Data
Using templates contributes heavily to the standardization of data. You can use a template for simple tasks, such as filling in the data about a new customer. By using the power of templates, the data owner saves a lot of time looking for all the information or formatting the data.
To give another example, let’s assume the HR department requests employees who are sick to send a sick notice by email. Now, the HR manager has to pick the information from the email, format text, and insert the data into a specific HR tool. The organization can optimize this process by using predefined templates that ask the employee for the date, his or her name, and the reason for absence.
Using templates also allows for technological innovation. For instance, a programmer could write a script that scrapes the data from the sick notice template and inputs that data directly into the HR tool.
3. Invest in Education
The success of your data governance implementation depends heavily on your employees. When roles and responsibilities are unclear, data quality will decrease. More errors will happen, data will get lost, or employees will add invalid information.
Therefore, educate your employees properly, and explain the data governance process so they know why they’re doing what they’re doing.
4. Define Integration Steps
Many organizations define an amazing data governance process, but they don’t think about the actual implementation. If they asked, “Are we ready to implement this new data governance process?” then often the answer would be no!
As you consider how to integrate a new data governance process, consider questions such as:
- How does this process fit into our current operations?
- Can we use our current tools and technologies to support this new strategy?
- Do we need to create a new role, or can we delegate responsibilities to existing employees?
- Does this new data governance strategy fit into the current operations?
- Do we need to educate employees to successfully implement the new governance system?
These questions are vital for successfully implementing a data governance strategy.
Next, let’s explore data standards.
5. Develop Data Standards
Besides data templates, data standards also matter. A data standard includes elements such as defining nomenclature or data formatting.
Nomenclature is important to create standards for file names. For example, a file name might consist of a date, short title, and version number. Data formatting, on the other hand, refers to choosing standards for your data. To give an example, you might want to use the American date notation (month, day, year) for any date field in your databases. Or you may want to define a standard for which decimal separator to use for numbers—periods or commas.
Next, let’s discuss why data architecture is vital.
6. Plan With a Data Architecture in Mind.
It’s important to define a data architecture that includes all the data your organization holds. Often, you’ll want to use data domains to put data into logical groups. A data domain defines how your data architecture will look.
A data domain can relate to customer data, product data, and so on. It’s a common technique used to group data in meaningful domains. Besides that, it helps your organization get a better overview of the data you hold.
Let’s move on to our final step.
7. Recognize the Importance of Data Metrics
Finally, you can learn more about data metrics. As anything is measurable, you can also measure the quality of data through metrics. Data metrics help you keep track of your data governance, and they allow you to track the quality of your data.
For example, you can track the number of duplicates in your data or the number of missing fields. If this number increases every month, then you’ll know something’s wrong with your data governance strategy.
Data Governance Done Right
To conclude: Data governance helps you define how humans, processes, and technology should work together to ensure the highest quality data. The most important part of data governance is defining integration steps. Without clear integration or migration strategy, your employees won’t know how to adapt to a new governance system.
Once you’ve successfully implemented the data governance strategy, it’s important to regularly check the health of your strategy. To this end, data metrics act as a continuous feedback loop to tell you about the success or failure of your data governance strategy.
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!