Every year, modern organizations deal with more data types from more data sources that require more advanced methods of data quality management to meet their needs. The size and complexity of this data in aggregate also tend to hog the headlines. For data stewards, however, while adapting and accounting for volume and complexity is vital, quality remains key.
These rising tides of data complexity increase the dangers of data quality issues. As organizations rely on data quality more holistically, sub-optimal data streaming through complex systems can lead to a widening range of problems, including inaccurate analytics, faulty business decisions, decreased customer satisfaction, and non-compliance with regulatory standards.
Therefore, the rules that govern data quality are taking on greater and greater importance, if not prominence. This article delves into the essence of data quality rules, breaks down their critical importance, and explores how data contracts emerge as pivotal instruments in safeguarding and elevating data quality across the board.
Data quality rules are a subset of business rules, predefined criteria that establish how to measure and monitor the accuracy, completeness, consistency, timeliness, uniqueness, and validity of data sets (i.e., data quality). As such, they play an essential role in effective data governance.
Without such rules, it would be exceedingly difficult (if not impossible) to identify and resolve data quality issues, improve the quality of data over time, and ensure that data assets, data elements, and data models are compliant with all standards and policies.
On the other hand, having data quality rules in place helps ensure an organization gets the promised quality of data. These rules can also validate data values or data records, and they are accessible to data users, unambiguous, and maintainable. By implementing data quality rules, organizations can automate the analysis and cleansing of data, saving resources and ensuring the data is "fit for use" for its intended purpose.
In general, the process of creating data quality rules is structured and involves several key steps:
In most cases, data quality rules begin with a team conducting a data quality assessment and defining the dimensions that are relevant to their organization’s business requirements. Typically, these dimensions include the foundation aspects of data quality itself—accuracy, completeness, consistency, timeliness, and validity. By identifying these dimensions as they relate to specific business goals, the criteria established will measure quality as it matters most to the organization.
With the organization’s particular dimensions established, the team should then identify all data quality issues that could be problematic over time. This step involves understanding what good quality data looks like for your organization—what is required and what is acceptable. To do so, teams should be sure to engage with data consumers and stakeholders who can provide valuable insights into where potential problems may exist or areas that need improvement.
Once a team flags dimensions and potential issues, they can specify and document the data quality rules. As much as possible, the rules should be clear and align with the data quality dimensions. Once documented, they can serve as an ongoing reference for the organization’s data management practices.
Once rules are specified and documented, data quality checks should then be implemented. These checks take the form of processes or algorithms designed to evaluate and enforce the data quality rules when put into practice. Quality checks can be applied at various stages of data handling to make sure the data in use meets the quality standards that are now established.
Never make the mistake of thinking rules related to data quality are static. They must evolve as well as the organization’s data and business needs. This requires a consistent schedule of reviewing and making updates as needed. Despite the whipsawing of today’s data-driven businesses, this step ensures all rules remain relevant, efficient, and effective. Regular reviews and updates also facilitate communication and collaboration with the organization’s key data stakeholders.
Along with maintaining open lines of communication with data stakeholders, ongoing staff education and training is essential. Ultimately, data quality is everyone’s responsibility in an organization. Therefore, all employees involved in data handling need a tangible understanding of the standards they need to meet and the role they play in maintaining optimal data quality.
Finally, it's important to empower some employees to monitor data compliance and enforce data quality rules consistently. As a complement to data governance within an organization, the chosen employee (or employees) should establish systems that can automatically check data against established quality rules.
Ideally, these systems also automatically alert the proper staff to potential issues as they arise. Moreover, regular audits and reviews help make sure that data quality rules are being followed (and, subsequently, that data quality remains high).
Like many aspects of data engineering, the specific benefits of establishing data quality rules can vary across organizations and industries. However, some benefits of ensuring consistently high-quality data are more common than others:
More informed decision-making: High-quality data is accurate, complete, consistent, and reliable, which is vital for making informed business decisions. Poor data quality can lead to incorrect conclusions, resulting in misguided strategies and actions.
Enhanced operational efficiency: Accurate and consistent data helps streamline operations, reducing the time and resources spent on correcting errors and reconciling discrepancies. This efficiency can lead to cost savings and improved productivity.
Ongoing regulatory compliance: Many industries have regulations that require businesses to maintain high-quality data. Failure to comply can result in fines and penalties, making data quality rules critical for legal compliance.
Customer satisfaction: Good data quality enables better customer relationship management by ensuring accurate customer information, which can lead to increased loyalty and sales.
Creating competitive advantages: Organizations with high-quality data can gain insights that provide a competitive edge in the market. They can identify opportunities and trends more effectively than those with substandard data quality.
Operational agility: As businesses grow, the volume of data increases. High-quality data ensures that systems can scale effectively without being compromised by data-related issues.
Savvier risk management: Good data quality reduces the risk of errors that could have significant negative impacts on a company's operations and reputation.
Consistent data governance: Data quality rules are a key component of data governance, which involves setting standards and protocols for data collection, processing, and use. This ensures that data is managed properly across the organization.
Data contracts can support data quality rules by formalizing the expectations and requirements for data quality between data producers and consumers. Here are six crucial ways they work to make data quality rules impactful:
Data contracts explicitly define the structure, format, and quality expectations for data exchange. They can include data validation rules to ensure data integrity and guidelines for handling errors or exceptions. By doing so, they provide a clear understanding of the data quality requirements that must be met.
Data contracts can capture integrity constraints or data quality rules, which are then enforced by the upstream component. For example, a data contract might include a rule that a certain numerical field must be a positive number, and this rule would be enforced as part of the contract.
Data contracts serve as a single source of truth for understanding data in motion. They help ensure the consistency, reliability, and quality of data by providing transparency over dependencies and data usage within an organization.
Data contracts are fundamental tools for orchestrating efficient and responsible data handling within a data governance framework. They establish clear guidelines, ownership, and accountability for data through agreed-upon standards and practices, which are vital for maintaining data integrity and compliance with regulatory requirements.
Data contracts can be enhanced with metadata properties and rules that specify integrity constraints or data policies. These can be used to automatically enforce data quality through systems like Confluent Schema Registry, which supports data contracts with tags, metadata, and rules.
Data contracts may include SLAs that specify standards for data quality and availability. These SLAs help ensure that data producers maintain the agreed-upon level of data quality, and they provide a basis for accountability and enforcement.
So, in our world of increasing digital transformation, it’s clear that data quality rules the realm of decisions, operations, and innovations. Therefore, establishing rules that help ensure data quality in an organization forms part of the foundational bedrock of robust data governance and integrity. It’s also clear that data contracts do more than fortify this foundation. They elevate the operational, legal, and strategic facets of managing data assets across the spectrum.
As we navigate the complexities of data-driven ecosystems, the integration of comprehensive data contracts emerges as a pivotal strategy to harness the full potential of our data resources, ensuring that they are not just abundant but accurately aligned with our ambitions and regulatory mandates. Make sure you’re educating and equipping yourself to handle these issues within your own organization. Join our product wait list at Gable.ai and discover for yourself what’s possible when data reliability and integrity are never in question.
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