What are the most effective data quality workflows?

This is often a very difficult step for organizations that are not digital-native (meaning they were formed in the late 90s/2000s and were data-focused from the start). Sometimes just getting approval to buy a data profiling tool is a challenge. If your organization is at this step, I recommend starting with a license <$1000/yr for something like Toad DataPoint. Assign a Data Analyst to fully understand the data. Build a spreadsheet of fields that need attention. Prioritize why - what impact will that make on the business? Work with source systems owners to mass update fields that can be fixed in one pass. Work with data stewards on the hard problems. Expect that record-by-record updates will be necessary. Be extraordinarily organized!

Create a role off Data Steward as a person who will be responsible for ensuring the quality and usability of the data in an organization. Few guiding points on the role of data steward can be: - A data steward should participate in creating and enforcing the policies and rules for data usage and security. - Collaborate with different data departments, and stakeholders to resolve data-related issues and optimize data usage for business objectives. - Oversee the entire data lifecycle, from collection to deletion, and ensures that the data is accurate, consistent, reliable, accessible, and secure. - Create a culture of data excellence and accountability within the organization. - Bridge the gap between technical expertise and business needs.

In my experienced, Assessing data quality involves evaluating the accuracy, completeness, consistency, reliability, and timeliness of the data. Keeping in mind that data quality assessment is an ongoing process, and regular evaluations are necessary to ensure that data remains accurate, reliable, and fit for its intended purpose.

The first step in any data quality workflow is to assess the current state of the data. You need to identify the sources, formats, types, and volumes of data you have and evaluate how well they align with your business goals and requirements. Various methods and metrics, such as data profiling, data auditing, data validation, and data quality scorecards, can be employed to measure the quality of data. These methods assist in uncovering any data issues, such as missing values, duplicates, outliers, inconsistencies, or errors.

In my experience, data quality is an ongoing process. It should be integrated into your data management and governance practices to ensure that data quality is maintained over time and that corrective actions are taken when issues are identified. Regular data quality assessments and continuous improvement efforts are key to maintaining high-quality data.

The next step is to establish data quality rules that will guide your data cleaning and transformation processes. Data quality rules are the standards and criteria that specify how your data should appear and behave. They can be based on your business logic, industry best practices, regulatory compliance, or user expectations. Different types of data quality rules, such as data accuracy rules, data integrity rules, data consistency rules, and data timeliness rules, can be utilized. These rules assist in defining clear expectations and objectives for your data quality improvement efforts.

Defining and implementing data quality rules is an ongoing process. Regularly review and adapt your rules to ensure that they remain relevant and effective in maintaining high-quality data.

These predefined guidelines and criteria used to assess and ensure the accuracy, completeness, consistency, reliability, and other attributes of data. These rules serve as a set of standards against which data is evaluated, helping organizations maintain high-quality data for effective decision-making. Data quality rules can be applied during data entry, integration, transformation, and ongoing monitoring processes. Establishing and enforcing data quality rules is crucial for maintaining the trustworthiness of data. These rules should align with organizational objectives and regulatory requirements, and they need to be regularly reviewed and updated to adapt to changing data needs and business processes

For me, implementing the data accuracy, consistency, and reliability components within a comprehensive data quality workflow can help organizations maintain high standards for their data, resulting in more reliable insights and better decision-making. Keep in mind that data quality is an ongoing process, and workflows should be regularly reviewed and updated to adapt to changing data requirements and business needs

The third step is to implement data quality actions that will correct or prevent any data issues. Data quality actions are the tasks and operations you perform on your data to bring them into compliance with your data quality rules. They may include data cleaning, data enrichment, data standardization, data deduplication, data integration, and data monitoring. Various tools and techniques, such as data quality software, scripts, workflows, pipelines, or ETL processes, can be utilized to execute data quality actions. These tools help automate and streamline your data quality workflows, ensuring consistency and reliability.

In many cases, raw data from diverse sources contains errors and inconsistencies. From my perspective, prioritizing data cleansing is crucial as it allows the analyst to address foundational issues, ensuring the reliability and accuracy of the dataset before moving on to other data quality actions. The reason is that data cleansing focuses on rectifying inaccuracies, inconsistencies, and errors present in the raw data. Without clean data, efforts to enrich, standardize, or integrate information may lead to compounding errors and misleading insights.

Implementing data quality actions involves putting into practice the steps necessary to improve and maintain the quality of your data. These actions are designed to address data quality issues and ensure that your data meets predefined standards.

"The fourth phase involves evaluating results post-implementation of data quality enhancement strategies. We measure and compare states before and after intervention, ensuring adherence to established data quality rules and goals. We use methods like dedicated dashboards, detailed reports, graphs, and specific KPI monitoring for clear visualization and effective communication of data quality impacts. Aligning results with broader organizational goals identifies gaps and areas for improvement, validating actions and contributing to a continuous cycle of data quality enhancement, strategically aligned with organizational objectives."

The fifth step involves reviewing data quality feedback received from users and stakeholders. Collecting and analyzing opinions, comments, suggestions or complaints is essential to understanding the needs, expectations and satisfaction of data consumers. Methods such as surveys, interviews and focus groups help identify problems and opportunities for improvement. For a more comprehensive approach, implementing continuous feedback systems can be considered as a way to further strengthen data quality. According to an IBM study, poor data quality costs companies around 3.1 trillion dollars per year in the United States.

Effective data quality workflows involve defining standards, profiling, cleaning, validation, enrichment, monitoring, governance, metrics tracking, user training, automated testing, auditing, feedback loops, archiving, and using dashboards to ensure ongoing data accuracy and reliability. Regular reviews and updates are essential for adapting workflows to changing business needs. The most important aspect is that users know what exactly they are looking, namely the goal!

One of the key considerations organisations have to lookout for while implementing any data quality or standardization program is to create the role of a Data Steward. Depending on the organisation's maturity this can be a standalone role or can be a culmination of roles rolling right upto a 'Chief Data Officer'