Data Governance Best Practices

Unlocking the power of accurate and reliable data is no longer a daunting task. Companies that are able to do so will be well-positioned for the future, as they use data to enhance customer experience, increase customer retention, and drive innovation.   

Data Governance plays a prominent role in fulfilling the promise of doing more with your data. Companies that take advantage of revolutionary advancements in areas like automated data quality checks, paired with best practices like standardized data formats, will be well on their way to having actionable data that informs mission-critical business decisions and opens up opportunities for growth.  


Why You Should Implement Automated Data Quality Checks

Data Quality is a typical priority for most Data Governance programs. Automated data quality checks are an effective way to ensure that data quality is maintained throughout your organization without adding significant additional demands on your workforce. They provide the following benefits to organizations that are looking to elevate their Data Governance efforts:

1. Improved data accuracy: Automated data quality checks can help companies identify and correct errors in data quickly, improving data accuracy and reducing the risk of making decisions based on incorrect data.

2. Better compliance: Companies that employ automated data quality checks ensure that they are meeting regulatory requirements and industry standards, reducing the risk of penalties or fines for non-compliance.

3. Greater transparency: Automated data quality checks can help companies build trust with stakeholders by providing a clear audit trail of data changes and updates, improving transparency and accountability.

4. Cost savings and risk mitigation: By identifying and correcting errors in data early, companies can avoid the costs associated with correcting data quality issues downstream, such as lost productivity, reputational damage, or customer churn.

5. Improved decision-making: Automated data quality checks can help companies make better-informed decisions by ensuring that the data used to inform those decisions is accurate and reliable.

Automated data quality checks can be implemented to run in the background so they identify any issues with data quality in real-time. DMG recommends at minimum setting up rules that check for missing data, inconsistent data, and duplicate records. If and when these issues can be caught early, they can be flagged for troubleshooting before they’re allowed to permeate and erode data quality.


Why You Should Standardize Data Terminology, and How You Can Get Started

Standardizing data terminology is another way to ensure data quality and integrity without adding significant additional demands on your workforce. Some examples of data terminology are: product identifier, states, colors, amortization value, cost of goods, etc. These are largely based on the industry and business that you are in, and which data is considered to be of high value to the organization.

By standardizing the terminology associated with data formats (for example), you can ensure that data is consistent across different data sources and that it can be easily integrated between systems and analyzed. Standardizing data formats can also help reduce errors and inconsistencies in data by ensuring that all data is entered or captured in a consistent and uniform manner.

Here are some examples of how to standardize data formats in a Data Governance model, along with some of the benefits of each:

Use of data dictionaries: A data dictionary is a centralized repository that defines the names, definitions, and formats of data elements used in an organization. By establishing a common data dictionary, all data elements can be consistently defined and documented, reducing ambiguity and improving understanding and use of the data.

Data validation rules: Validation rules are used to check data values against predefined criteria to ensure that data is accurate and consistent. They ensure that data can be standardized to a common format and structure.

Data modeling: Data modeling is the process of creating a conceptual or logical model of data, which defines the relationships between data elements and the rules governing their use. Taking this step means data can be structured consistently across different systems and applications.

Data transformation and mapping: Data transformation and mapping tools can be used to convert data from one format to another, while preserving the meaning and integrity of the data. By standardizing data transformation and mapping processes, data can be easily moved and shared across different systems and applications.

Master data management: Master data management (MDM) is a set of processes and tools used to manage the most important data elements in an organization, such as customer, product, or location data. MDM empowers organizations to ensure that critical data is accurate, consistent, and accessible across the enterprise.

Use of data exchange standards: Data exchange standards, such as XML, JSON, or CSV, or industry data standards (such as MISMO in mortgage banking) can be used to define a common format for exchanging data between different systems and applications. This allows for data to be easily shared and integrated across different platforms, reducing data silos and improving data quality.

In addition to all of the above, standardizing data formats also makes it easier to implement the previous best practice of automated data quality checks.

And remember that data formats are just one type of data terminology. There are many others out there. That is a larger subject, however, and one that we may revisit in a future post.

Let’s Discuss Data Quality Metrics and A Data Quality Culture

Establishing or improving your Data Governance framework can feel like an overwhelming challenge, especially with the deluge of data and systems that even small to medium sized businesses have to manage. The good news is that it has never been easier to automate data quality checks, and standardizing your data terminology and data formats can go a long way toward helping you do so.

The first post in this series looked at how organizations can take a minimally invasive approach to formalizing or improving their data governance efforts. In the third and final post of the series, we’ll cover the importance of choosing the right data quality metrics and establishing a data quality culture where everyone is invested in making sure your Data Governance efforts bear fruit.

You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing data governance approach.