High-quality data and effective data governance are essential for companies adopting a data-driven approach.
Although different, these two aspects are closely interconnected.
Organizations often acquire data quality solutions with the expectation of resolving problems related to data accuracy and reliability problems.
Yet, before that, data governance is crucial to establish the foundation for comprehensive data quality at an enterprise level.
Although data quality and governance are different aspects of managing a company’s data, they can bring valuable benefits.
By using them both, one can get a clearer view of one’s company’s resources, make operations more efficient, and increase one’s business’s ability to adapt and succeed.
In this article, we will delve into what data quality and governance are, the differences between the two, their importance, how they overlap, and how to incorporate them into your organization.
What is Data Quality?
Data quality refers to how good and reliable data is, based on factors like how complete, accurate, relevant, timely, and consistent it is.
When data meets all these criteria, it’s considered high quality.
Companies that have high-quality data can make smarter choices about their business path, strategies, and how to use their data effectively for success.
To maintain data quality, utilizing top-notch data quality software is crucial.
Any issues with data quality can result in making bad decisions.
The better the quality of your data, the more useful and valuable it becomes.
Importance of Data Quality
Ensuring data quality isn’t just a new addition to your company; it’s a vital element of any data-driven approach or business strategy. Managing data quality can result in the following:
1. Precise Decision-Making: High-quality data enhances decision-making by facilitating performance tracking, predicting future outcomes, and recognizing potential issues.
2. Efficient Resource Usage: Companies can avoid wasting resources on incorrect data and optimize resource allocation by upholding data quality.
3. Enhanced Customer Experience: Accurate and up-to-date data helps companies comprehend their customers and preferences, leading to better customer experiences.
4. Cost Reduction: Poor data quality can lead to costly errors and rework. Investing in data quality can minimize mistakes and the associated expenses.
Data quality isn’t merely a short-term consideration; it profoundly influences an organization’s long-term success and expansion.
By maintaining rigorous data quality standards, organizations can be well-equipped to tackle future challenges and capitalize on opportunities.
What is Data Governance?
Data governance encompasses the strategies and procedures necessary to effectively handle enterprise data, utilizing it for informed business decisions.
It establishes a framework for mitigating the risks tied to businesses operating within an unpredictable regulatory landscape.
In essence, data governance involves the management of all the assets related to an organization’s information—not just data but also documents, applications, networks, configurations, and metadata.
Numerous data governance software solutions offer control over data accessibility, usability, accuracy, and security.
Importance of Data Governance
Data governance holds importance for several reasons:
1. Compliance: It ensures that companies conform to laws and regulations, such as GDPR, thereby preventing substantial fines and penalties.
2. Consistency: Data governance establishes a uniform approach to data management throughout an organization, reducing discrepancies.
3. Security: It plays a pivotal role in safeguarding sensitive data against potential breaches, lowering the risk of unauthorized access.
4. Trust: By ensuring proper management, currency, and data accuracy, data governance instills trust among stakeholders in the reliability of the data.
5. Increased Efficiency: It enhances operational efficiency by eliminating redundant data duplication and refining data-related workflows.
6. Enhanced Decision-Making: High-quality and dependable data improves strategic planning, decision-making, and overall performance metrics across all business sectors.
Main Differences between data governance and data quality
Data governance focuses on the broader tasks of managing data involving people, processes, and technology.
Its uses encompass creating effective methods for storing data, overseeing its entire lifespan, pinpointing data that requires correction or removal, assigning a responsible data steward, and using technology to support these data management efforts.
In contrast, data quality deals with accuracy problems in more detail.
This involves spotting inaccuracies or discrepancies in individual data points, like names or addresses.
It also includes developing and implementing specific processes to ensure data is precise, uniform, appropriate, and comprehensive.
How do data quality and governance overlap?
Put simply, you can’t achieve data quality without first having effective data governance in place.
Before even thinking about using a separate tool for enterprise-level data quality, organizations must establish proper data governance.
Organizations adopt data governance for various reasons, as it impacts multiple aspects like security, privacy, accuracy, compliance, roles, management, integration, and more.
They use data governance to:
1. Enhance transparency regarding data, usage, availability, and management.
2. Establish uniformity across data systems, policies, and procedures.
3. Address data-related issues.
4. Ensure adherence to regulations and organizational standards.
All of these tasks are crucial for enhancing and monitoring data quality.
Strong data governance brings together those who create data and those who use it, promoting communication and shared understanding about data quality.
While existing data might require significant improvements to enhance its quality, the insights gained from this process can be used to refine data governance strategies and protocols for incorporating new data.
Considering this interconnection, developing data governance and data quality strategies in collaboration is logical.
Incorporating data quality and data governance in your organization
Where should a data team begin if they want to enhance data quality and governance?
Organizations deal with a plethora of data components.
Which ones get their attention? Which ones can be overlooked? Which elements have the most significant impact on business and should be tackled first?
Data teams can approach these inquiries from two angles:
1. Essential data components: Identify what holds utmost importance for the business, whether it’s a regulatory report, a data cube, or a key performance indicator (KPI).
2. Data value: Evaluate how long poor data quality can exist or the risk tied to insufficient quality data. Start by addressing areas with the highest stakes.
Once these focal points are identified and prioritized, data governance can be employed to establish a collaborative framework for managing and outlining policies, business rules, and assets that ensure the required level of data quality control.
Alignment between IT and business users is vital, as they work together to define and enhance data quality and utilization.
For instance, data owners can pinpoint critical systems and processes involved, while business users can voice the standards that data must adhere to during its journey through these systems. This stage is where policies, prerequisites, and business rules are made and agreed upon.
With a clear understanding of how data flows within the organization and the established standards, the data quality team can more seamlessly turn these standards into data quality rules.
Subsequently, they can apply these rules to the data within those systems.
Conclusion
Even though data quality and data governance are different concepts, they operate in collaboration.
A robust data governance foundation is crucial for achieving adequate data quality.
With proper data governance, organizations can have confidence in their data, which, in turn, increases the reliability of that data.
Data governance is the initial step in ensuring access to the top-quality data possible by providing the necessary context and knowledge about the data.
When data quality measures are built upon a foundation of data governance, it offers more than just a basic assessment of quality.
It furnishes an appraisal of quality within how the business employs the data.
By establishing data governance as the foundation and integrating data quality, organizations can ensure that their data is accurate, reliable, and safeguarded.
This assurance paves the way for comprehensive digital transformation throughout the entire enterprise.
