How do you use data quality tools to monitor policies and rules?

Data Quality tools provide options for Data Profiling and Validation. Business rules are needed for data profiling. The business rules can be converted into technical rules and used as part of profiling. For an example, If there is a organization-wide policy to have upper case in names, a rule can be generated and applied in the profile. If there is a business rule, not to have missing column on specific data element, it can be part of the profiling.

Leveraging data quality tools is pivotal for ensuring adherence to policies and rules. These tools can automate the process of validating, cleansing, and monitoring data against predefined standards. In my recent consultancy project, I utilized a data quality tool to manage a client's extensive customer database. This database, structured in nature, contained fields like customer ID, name, address, and transaction history. We set up a rule in the tool to regularly check for duplicate entries and inconsistencies, like mismatched postal codes or incomplete addresses. This practical application underscores the importance of data quality tools in maintaining the integrity of structured data, ultimately contributing to enhanced business outcomes

Tackling unstructured data, which includes text, images, and videos, presents unique challenges. Data quality tools are crucial for extracting meaningful information and ensuring compliance with relevant policies and rules. I worked on a project where we dealt with a large volume of customer feedback collected via emails and social media. The data was diverse, ranging from text to emojis. By employing a sophisticated data quality tool equipped with scripting capabilities, we were able to categorize sentiments, identify key themes, and flag inappropriate content effectively. This demonstrates the power of data quality tools in transforming unstructured data into actionable intelligence.

Loaded correctly with data quality rules, a data quality tool can identify violations. You then need to take action on the violations. Typically, multiple people are involved in identifying the problem and solution, which can be led through data governance. Notifying people using the data is key so they understand the risk of using data with quality issues. Often, these tools are complex and have limited access, so it would be helpful to find a way to directly and succinctly notify people.

Data Quality tools can be used to create data quality test cases which are executed during the extraction and transformation stages of the data pipeline. Some common checks that can be performed are as follows : - Null Checks - Duplicate Checks - Validity Checks - Format Checks These checks can be performed in the Staging Layer of DWH. After this in transformation layer - Bussiness logic checks - Data profiling checks - Referential Integrity checks can be performed. The reports generated from Data Quality tools should be monitored to ascertain most common issues and then a proper RCA process should be done followed by change in process during data generation so that these issues are minimized.

In reality, most of the business rules can be converted into technical rule and applied on profiling, which will get the results. In some situations, the data needs to be prepared prior-hand, before profiling. For example, if you are identifying duplicates using fuzzy mechanisms, that effort needs to be completed before profiling.

Here are some tips on using the data quality tools: 1) Set up your data quality tools to send alerts and notifications whenever they detect any violations. These alerts can be directed to the relevant team or parties for immediate action based on pre-defined metrics. 2) Consider creating data quality scorecards to get a comprehensive overview of the quality of your data across different sources and dimensions (like completeness, consistency, integrity, and timeliness). This will help you to prioritize areas that need attention and improvement. 3) Generate reports and send them through various channels to track compliance and gain a better understanding of the sensitivities involved.