Regarding data, knowing how, who, and what are the most critical questions to govern it efficiently and responsibly. Data governance gives organizations the strategy to manage their information.
In this article, we’ll define data governance. Then, we’ll dive into why it’s essential, critical pillars of governance, its benefits, and its challenges. By the end, you’ll learn about one of the most innovative ways to govern your data assets: combining the common reactive approach with a more efficient, preventative strategy.
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 (for example, maybe you don’t need the power drill you found in an old toolbox, but you might need it when you want to make minor improvements 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. In the context of the RV, it might look like labels, a notebook explaining everything, or another system entirely. 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.
For example, different teams within an organization require different data sets, instructions on how to use them, and permissions for data access. Marketing teams, accounts receivable, customer service teams, and tech/product support would look different in the data governance context—especially as it may include social security numbers and sensitive data.
The data team can enact the process for governance that would create a way to protect everyone, provide accurate information at the right time, and provide clear guidelines on how to use it.
Why is it important?
Good data governance has become essential now more than ever. Thanks to innovative technology (like artificial intelligence and machine learning) 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 and 76% get frustrated when companies fall short of it.
The importance of data governance initiatives is two-fold:
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.
Companies should ensure they have the right compliance, procedures, and tools to get the most out of data and use it to improve the customer experience.
One significant challenge is dealing with reactive strategies. While a quick response is ideal when something goes wrong with your data collection and processing, it’s not enough to ensure quality data governance. Companies need a preventative solution.
Gable's innovative technology helps organizations improve data quality at scale by fostering communication between data producers and consumers via data contracts. These contracts eliminate the human error component and stop mistakes before they happen.
2. Ensuring customer trust in today’s climate
The data industry knows the challenges it’s faced with privacy, transparency, and consumer trust—in short, the world is paying attention to how companies handle data.
Companies should be able to uphold regulations like GDPR and perform above standards to remove themselves from avoidable pitfalls. They can position themselves as one of the industry's safest, most trustworthy businesses.
Since 2019, data privacy search inquiries have doubled, signaling the need for more responsible data management. It’s no longer enough to handle data ethically, but consumers want full transparency and clarity on what’s collected and how it’s used.
Source: Google Trends | Search inquiries for data privacy have doubled in only a few years.
Effective data governance addresses these concerns. Companies can use the tools necessary to handle big data responsibly and enable access to the right individuals. Organizations can also provide clear and transparent policies to customers, communicating data practices and what to expect.
As mentioned, customers want personalized experiences. This starts with quality data. And if we can combine that with the qualifications expected from us when it comes to building trust—like data protection and transparency—then we can create a productive data strategy.
When we fail to govern data
When we can no longer trust data, it becomes unreliable. We no longer have a picture of consumers, the world, and where the market is heading. Management is stuck making business decisions no longer based on information but on best guesses.
But that doesn’t have to be the case. By following foundational principles, we can avoid situations that compromise our data.
Data governance in the cloud era
In recent years, cloud technology has changed the data governance landscape. Depending on the infrastructure you use, data is available anywhere and everywhere. This has brought more concerns about safety, security, and management protocols.
The changes have led to more federated data governance. Unlike previous systems that centralized data, federated governance addresses the distributed nature of data in the cloud. It enables companies to manage and govern across different teams and platforms, providing flexibility, scalability, and efficiency regarding governance policies.
Another issue to consider is the involvement of non-technical teams in data governance. Non-technical staff will inevitably influence how data is managed as it becomes more accessible. Without the right system, code vulnerabilities can spark major concern.
That’s why a user-friendly no-code solution that allows users to manage data can help safeguard and empower non-technical teams to govern effectively. Clear guidelines that help govern data will also accelerate your governance strategy for better and safer results.
These fast-paced changes in modern cloud data governance require agile and adaptive systems. Unlike traditional governance, which might move slowly, contemporary governance must work quickly to adapt to changing tech and consumer expectations. Companies can adopt automated governance tools, implement continuous monitoring, and have a tighter feedback loop to adjust swiftly and respond to evolving requirements.
The four pillars of the data governance framework
Data governance comprises a few foundational principles, often summarized in four pillars. As a data governance team works towards efficient and secure information, they have the job of ensuring that data is organized and ready for an organization to grow and achieve its top-level priorities.
1. Upholding standards and regulations
Teams must ensure that all practices, regulatory requirements, and compliances are maintained and upheld.
For example, the GDPR, or the General Data Protection Regulation, is one of the strictest laws globally for data security and privacy. The EU law ensures that consumers are protected and that there is transparency on what data is being collected and what it’s being used for.
When companies become GDPR compliant, they meet EU standards and build one of the strongest, safest foundations for their data governance journey.
GDPR and other relevant compliances and regulations are essential to ensuring companies meet legal standards. Afterward, they can continue to create an above-and-beyond approach to create a safer and more transparent system for consumers. As trends and privacy expectations evolve, businesses can lead the way.
2. Data quality
Teams are then expected to supervise data and ensure it is authentic and reliable. Data is only as good as its quality, and companies need it to make the right decisions. Quality can be measured by preciseness, thoroughness, authenticity, timeliness, and consistency.
- Preciseness: Is the data correct? Can it be validated and trusted?
- Thoroughness: Is the data complete, and does it make sense as it is? Can it be used? Is there missing information that prevents the data from telling a complete story? Is the data organized?
- Authenticity: Does each piece of information accurately represent the subject or action?
- Timeliness: Is this data relevant to understanding present situations? Does older information add context, or is it misleading? Using this data, can we predict growing trends?
- Consistency: Are we gathering enough data through regular intervals, touchpoints, and actions? Are all tools in sync and integrated? Is manual labor getting in the way of collection, metrics, and analytics?
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. Managers should also make sure that these guidelines and promises are upheld.
4. Data management
Management refers to the overall components of governance—the “governing” portion. While stewardship identifies who is accountable and shapes the program, data management refers to all the moving pieces of making governing possible—like how efficient, secure, and cost-effective the process is. Additionally, it includes what tools are being used and how the data is stored.
Data governance key roles
While there are many roles and responsibilities within data governance (like strategic level oversight), 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. Additionally, they need parameters to evaluate expired data. If the data is no longer accurate, the team loses trust in the information, leadership makes the wrong decisions, and the organization builds data debt.
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. If there are inconsistencies in the policy or if circumstances change and leadership does not follow up, companies risk irresponsible governance and vulnerabilities.
What happens when there is an inefficiency? How is it handled? If data tools are not working right, if data isn’t organized, or if something is throwing a wrench into collection and analysis, 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. There should be transparency as data policies evolve.
5 best practices to consider when managing data governance
1. Choose the right team
Governance is about the right people. You need team members who are passionate about quality data and have the never-ending desire to ensure the best and most accurate information possible. Additionally, that information serves the organization and its initiatives.
2. Understand it’s a process
Data governance is a lot like gardening. You don’t plant seedlings and then revisit them in a few months. Most of them would die. Instead, you visit, water, and care for them often. When you spot weeds, you pull them out. 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. They should question how enterprise data is collected, presented, and analyzed. Team members should be able to correct issues quickly and prevent the same ones in the future.
4. Know the vision
The strategy should be crystal clear. If data teams know what the business goals are, they can manage data so that it’s organized and efficient to use for the needs of the organization. If team members aren’t sure what the vision is, it creates confusion, messy data, and more difficulty in making strategic decisions.
5. Invest in the right technology and data governance tools
In the data world, inefficient technology and human error can wreak havoc. That’s why choosing the right platforms and tools to help govern your data through contracts, automation, and communication is critical for creating a smooth process, reducing issues, and providing the best, most relevant information possible.
Gable: From reactive to preventative solutions
Reactive problem-solving is important. We need to be able to identify an issue and fix it to preserve data integrity and security. But it shouldn’t be the only solution.
Many reactive situations can be avoided from the start.
Gable’s proprietary technology provides data teams with data contracts to eliminate human error and prevent issues. Instead of waiting to identify a problem once it’s already happening (and causing damage), you can stop it from happening altogether.
Gable’s data contracts enable you to establish data owners, set requirements for the data, and codify these nuances as version-controlled code.
We know reactive data monitoring isn’t enough. Gable ensures that alerts trigger from notifications within the CI/CD workflow. Our bot provides context to developers where they are and tags data consumers after detecting breaking changes.
By bridging the communication gap between data producers and consumers, our platform creates a more accurate, efficient solution for data.
Reserve your spot now to learn more about Gable and how you can create a preventative strategy with data contracts.
