Gable Blog | Data Governance

What is data governance?

Data governance sets clear rules for handling and stewarding customer information throughout its lifecycle. It ensures data is accurate, used correctly, and kept safe.

One great way to imagine a data governance program is by picturing the following scenario:

Suppose you buy an RV from an estate sale. It works great and looks terrific, but the seller says you get the whole package. Everything inside from the previous owners is yours. There might be something valuable in there, but you won’t know until you look around and sift through the items (and clutter) in the vehicle.

These objects, like pieces of data, have to be organized into a few categories:

Detection: You discover and identify what’s in the RV. There might be old vinyl records in a cubby, family photographs in the closet, or valuable jewelry left behind. Some things might be useful, others worthless, while different pieces might have significance in the future.

Classification: Now, it’s time to classify each item and formalize what you’ve found. Label the value and use of each item. Organize everything so you can easily access it when it’s time to move them to the right place.

Process: Decide the policy you’ll base decisions on, like how you plan on using the items. What determines a good object or not? Where should it go? Create a strategy to follow.

Parameters: Now that you have the process on paper, you can follow specific guidelines to determine what to do with each piece—a set of clear rules.

Communication: Through documentation with metadata management, you can add the vital information you need to retrieve and understand it. This data catalog also articulates to others what they should do with the items—there’s clear data lineage.

Like the RV example, those governing data start with a central location. From there, they must decide what to do with the data, and who it should go to.

Why is it important?

Good data governance has become essential now more than ever. Thanks to innovative technology and customers living more of their lives online, there is a significant amount of information to collect, analyze, and use for decision-making and improving customer experiences.

Customers also expect you to use data efficiently, with McKinsey & Company reporting that 71% of consumers want personalized interactions.

1. Accurate, responsible, and efficient data for companies

With an effective data governance strategy, organizations can ensure high-quality data, use it responsibly, and maximize output.

2. Ensuring customer trust in today’s climate

The data industry knows the challenges it’s faced with privacy, transparency, and consumer trust. Companies should be able to uphold regulations like GDPR and perform above standards to remove themselves from avoidable pitfalls.

The four pillars of the data governance framework

1. Upholding standards and regulations

Teams must ensure that all practices, regulatory requirements, and compliances are maintained and upheld.

2. Data quality

Teams are then expected to supervise data and ensure it is authentic and reliable. Quality can be measured by preciseness, thoroughness, authenticity, timeliness, and consistency.

3. Transparency and privacy

Data should be ethically managed—every involved party should know what’s collected, as well as how and what it’s used for.

4. Data management

Management refers to the overall components of governance—the “governing” portion.

Data governance key roles

While there are many roles and responsibilities within data governance, there are a few hands-on positions.

Steward: The data steward cares for the data to ensure it’s accurate and up to policy standards.
Manager: The data manager helps put the strategic plan into action.
User: Data users involve the team and how they perform daily tasks, interact with the data, and study sets.

What are the challenges of maintaining data governance?

Each pillar of data governance faces obstacles.

Is it accurate and timely? Data teams must optimize and review how they collect and analyze data consistently.

Is it going to the right people, and are they using the data as they should? Data teams must decide who has access to the data, how much of it, and what they can use it for.

What happens when there is an inefficiency? How is it handled? If data tools are not working right, teams need to quickly identify and solve the issue.

Is the organization communicating its data policy? Teams must ensure that what data they collect and how they use it is communicated to customers, employees, and essential stakeholders.

5 best practices to consider when managing data governance

1. Choose the right team

Governance is about the right people.

2. Understand it’s a process

Data governance is a continuous process that should be treated with care and dedication.

3. Set high standards and communicate expectations

Ensure your team is consistently communicating.

4. Know the vision

The strategy should be crystal clear to create organized and efficient data management.

5. Invest in the right technology and data governance tools

Choosing the right platforms and tools to help govern your data is critical for creating a smooth process.

Gable: From reactive to preventative solutions

Gable’s proprietary technology provides data teams with data contracts to eliminate human error and prevent issues.

By bridging the communication gap between data producers and consumers, our platform creates a more accurate, efficient solution for data.

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