What are the best ways to define data quality requirements for different data applications?

Prioritize data quality dimensions based on application needs (e.g., accuracy/validity for ML, timeliness/completeness for reporting). Establish clear, measurable criteria for each dimension to guide evaluation. Address data quality issues early in ETL for third-party/public data. Utilize transformation layers in the data warehouse for continual enhancement and refinement of data quality by analysts/scientists.

Spoiler alert. If you prioritise only certain data quality dimensions for certain use cases you are doomed to failure. Focussing on accuracy and validity for ML and you ignore the fact the data is incomplete and Inconsistent. Your model will produce garbage. Data quality dimensions should be assessed by design, within your ingest/integration layers of your platform and published so your business areas/users can determine their own risk appetite in terms of consuming that data. Assess as many data quality dimensions as you can across all of your data that is being consumed. (Typically with a focus on Critical Data Elements) Do not split them as you need them all to be great for your consumers to have trust in your data.

Defining clear and measurable criteria for data quality can be difficult if the data is used by multiple parties for different reasons. My suggestion is to have a meeting or a collaborative document where it lists uses for the data, measures of data quality, and any know anomalies. This way, everything is laid out and true anomalies can be easily identified.

Data also has a historical quality. Independent of specific applications, data may be rare or common, difficult or costly to replicate, or representative of a smaller or wider population in the real world or across time.

Data quality dimensions are critical metrics that guide the integrity and usefulness of data in specific contexts. Accuracy, completeness, consistency, timeliness, validity, and uniqueness must be tailored to the data's end-use. For instance, accuracy is vital for machine learning, while timeliness is crucial for real-time analytics. Identifying and focusing on relevant dimensions ensures that data serves its intended purpose effectively. Collaboration between data engineers and business stakeholders is essential to define measurable criteria for each dimension, ensuring data is not just abundant but aligned with business objectives.

Setting data quality levels is about balancing desired outcomes with practical constraints. High, medium, or low tiers—or numerical scores—provide a clear framework for assessing data and setting improvement targets. It's crucial to align these levels with the purpose of the data: High accuracy is critical for operational systems where errors have significant consequences, while lower quality may be acceptable for preliminary analysis. Understanding the cost-benefit ratio of enhancing data quality is essential; investing in top-tier quality for non-critical applications may not yield proportional value. Instead, focus resources on elevating data quality where it most impacts decision-making and operational efficiency.

DQ is the linchpin of successful data applications across various domains For financial & healthcare sectors, where accuracy is paramount, high data quality ensures regulatory compliance, reliable risk assessment, and patient safety. In e-commerce & recommendation systems, it underpins personalized user experiences and boosts sales. In manufacturing, precise data enables predictive maintenance and operational efficiency. In marketing, it refines customer targeting & campaign effectiveness. Low-quality data can lead to poor decisions, lost revenue, reputational damage.

The concept of data quality levels is essential in determining data management strategies. It is important to emphasize that while striving for the highest quality level is ideal, it is often more practical to pursue a level that balances quality with resource expenditure. It is essential to achieve realistic goals, as you mentioned. Additionally, I would suggest incorporating continuous quality assessments to ensure that the chosen level remains aligned with the evolving needs of the business and the data environment. This approach does not only enhance resource usage but also maintains data quality in conjunction with organizational objectives.

Data Quality Levels are subjective to the tasks and stages of the project one might be working on you are just collecting data using surveying, then the focus might be to get as many responses as possible, and a low quality might be acceptable. However, even then, if one wishes to reduce time during data clean up (which takes up at least 70 - 80 % of the time during a data project pipeline), or the project has a unique requirement, then what type of data is getting populated in the database needs to be of High quality. Perhaps a framework can be followed to ensure all the stakeholders are aware of it and comply with it.

Level 0 - Unknown Quality: Data at this level lacks any information about its quality. It has not been assessed or validated yet. Level 1 - Low Quality: Data at this level has known quality issues, such as missing values, inconsistencies, or inaccuracies. Level 2 - Moderate Quality: Data at this level has been partially cleaned and validated. It may contain some errors, but efforts have been made to improve its quality. Level 3 - High Quality: Data at this level has undergone thorough cleaning and validation processes. It is accurate, complete, consistent, and reliable for most use cases. Level 4 - Trusted Quality: Data at this level is of the highest quality. It has been extensively validated, verified, and audited.

The criteria for data quality is something that needs to be set by the data users as it can vary from person to person or application to application. Standardization is however needed in order for users to understand what good quality data looks like.

To assess data quality effectively, a mix of subjective and objective methods is often necessary. Subjective methods, like stakeholder surveys and expert reviews, gauge the perceived quality and relevance of data in a business context. Objective methods, on the other hand, involve automated checks for accuracy, consistency, and duplication, often using predefined rules or algorithms. Combining these approaches provides a multifaceted view of data quality, capturing both its technical adequacy and its fit-for-purpose from a user perspective. This dual approach ensures a comprehensive understanding of data quality, informing better decisions about where and how to invest in improvements.

Whether for business intelligence, healthcare, or scientific research, reliable & accurate data is the foundation of informed decision-making Data collection methods are scrutinized to ensure they meet established standards and best practices. Then, data profiling & cleaning procedures are employed to identify and rectify anomalies, inconsistencies, and missing values. Data transformation & enrichment may be necessary to improve data quality. Finally, data quality metrics are employed to evaluate the data's accuracy, completeness, consistency, and timeliness. These assessments ensure that the data is fit for its intended purpose, enabling organizations and researchers to derive meaningful insights and make well-informed decisions.

I particularly appreciate the emphasis on the distinction between subjective and objective methods. It is crucial to recognize that objective methods provide reliable and consistent results, while subjective methods provide valuable insights into user satisfaction and business relevance. The integration of both approaches allows for a holistic understanding of data quality. In practice, utilizing technology for objective analysis while maintaining open channels for user feedback can create a robust data quality framework that is both technically sound and compatible with user needs.

To get comprehensive view of data quality: Combine subjective (surveys, reviews) and objective (automated checks) methods. Subjective for business context, objective for technical accuracy.

Balancing proactive and reactive strategies is key to maintaining data quality. Proactive measures like robust data governance, precise data modeling, and stringent validation protocols preempt data quality issues, establishing a strong foundation. Reactive measures, including data cleaning, correction, and enrichment, address issues as they arise, ensuring ongoing data integrity. Ideally, proactive actions are prioritized to minimize the need for reactive fixes. For instance, ensuring high-quality data at the source reduces the downstream burden, while reactive actions refine data further, particularly in transformation or analysis phases. This balanced approach optimizes both data utility and system efficiency.

High-quality data ensures accurate and reliable results, enhances decision-making, and boosts user trust. The process typically involves data profiling, cleansing, transformation, and validation. Profiling helps identify anomalies and inconsistencies in the data, cleansing removes duplicate or erroneous entries, while transformation ensures data compatibility. Validation checks for accuracy and completeness. Tailored approaches are necessary for different applications. In business analytics, it leads to more precise insights. In healthcare, it improves patient care. For e-commerce, it enhances personalization. Ultimately, data quality improvement is the foundation upon which data applications can truly deliver their intended value.

It is essential to have a combination of both in any robust data management strategy. Proactive measures, such as establishing a strong data governance framework, provide the foundation for high-quality data from the beginning. Furthermore, reactive measures are essential for addressing unforeseen issues and maintaining data integrity over time. A dynamic approach that adjusts the balance between these two, based on real-time data flow and application requirements, can significantly enhance the quality of data.

The key is understanding what the use case for the data as well as the constraints for obtaining the data will be. This will define the quality guidelines that need to be set in order to get the best of the given data sets.

Standardization: Establish consistent data formats, naming conventions, and coding schemes to ensure uniformity across your dataset. This practice facilitates data integration and improves the accuracy of analysis. Data governance: Establish clear data governance policies and procedures to define roles, responsibilities, and accountability for data quality. Regularly monitor and enforce these policies to maintain data integrity and consistency. Data documentation: Maintain comprehensive documentation describing the structure, definitions, and lineage of your data. This helps users understand the data better and ensures transparency and consistency in data interpretation.

Data quality documentation acts as a blueprint and a report card for an organization's data management efforts. It sets out the quality criteria, outlines the methods used for assessing quality, and records the actions taken to improve it. This documentation serves to align different stakeholders on what constitutes 'quality data', with specifications detailing the standards and metrics to be met. Data quality reports and dashboards provide transparency, allowing stakeholders to track the ongoing status of data quality and to quickly identify and address issues. Logs offer granular details on data incidents, fostering accountability and continuous learning.

DQ documentation serves as the foundation for understanding, managing, and leveraging data effectively. The process involves clearly defining data sources, transformations, and business rules, alongside metadata that describes data attributes and their relationships. Documenting data lineage, quality checks, and potential issues helps in troubleshooting and maintaining data integrity. This documentation is essential for regulatory compliance, fostering data collaboration, and facilitating seamless integration into analytical tools. It ultimately empowers organizations to make informed, data-driven decisions and extract maximum value from their data assets.

Data quality documentation is a crucial element in managing and maintaining data integrity among various stakeholders. It is a blueprint for what is expected, achieved, and needs improvement. It is also essential to ensure that this documentation is accessible and accessible to all stakeholders, regardless of their technical background. Incorporating visual elements such as dashboards can make data quality metrics more accessible. Regular updates and transparent communication of this documentation can further enhance stakeholder engagement and increase the quality of data. #DataQualityManagement

Data quality is contextualized by the application is it used on. To allow and support potential unknown future applications, it is important to maintain documentation of transformations applied and the provenance of the data. Data provenance supports validity and application geographic mobility. It also supports potential use cases which require different transformations or different scale of data quality.

Data Quality Objectives: This section defines the goals and objectives for data quality within the organization. It outlines the expectations and requirements for various aspects of data quality, such as accuracy, timeliness, completeness, and consistency. Data Quality Metrics: This part outlines the specific measures used to assess data quality. It includes the definition of key performance indicators (KPIs) and metrics that are used to evaluate and monitor the quality of data. Examples of metrics could include data completeness %, error rates. Data Quality Processes: This describes the processes and methodologies for ensuring quality. It outlines the steps in data profiling, data cleansing, data validation, and data enrichment.

Data Quality Review and Feedback are key, as we handle data from multiple sources. Using Great Expectations, we conduct thorough data quality checks, which automatically generate detailed documentation. If discrepancies are found, we send alerts to the data sources. Results from these checks are stored in a table, allowing for trend analysis and identification of recurring data quality issues. This analysis is vital for giving focused feedback to data source owners and stakeholders. Our approach not only upholds high data quality standards but also promotes continuous improvement and accountability in our data management practices.

To assess the quality of your data, you need to perform data profiling. This process will reveal any problems with your data, such as missing values, duplicates, and inconsistencies. For example, you might find that some records have missing names, phone numbers or addresses. Or you might discover that some records are repeated with slight variations. Or you might notice that some records have values that do not match the expected format or range. Based on this analysis, you can set realistic and achievable data quality goals.

Data quality review and feedback are the chief mechanism for subject-matter experts, operational support, and end-users to validate data quality and the applications built on that data.

Tailor data quality metrics to the unique context of each data application. Acknowledge that not all data elements are equal; some may wield more influence on critical business processes. By assigning contextual importance to metrics like accuracy, completeness, and consistency, you create a nuanced framework that aligns with the intricacies of diverse data landscapes. For example, managing a financial portfolio where not all data carries equal weight, contextual metrics address this. Real-time market data demands stringent accuracy, while historical transaction records might allow for a bit more flexibility. By tailoring metrics to the unique significance of each data type, an effective approach to ensuring data quality is achieved.

Evaluating data quality through a comprehensive data profiling approach, which extends beyond mere outlier identification, is highly advisable. This approach harnesses advanced profiling techniques like semantic analysis and sequential pattern analysis. Unlike traditional methods focused on outlier detection, these techniques delve into data semantics, revealing hidden relationships and dependencies.