Data quality checklist helps you quickly measure data that is considered important. Data quality metrics such as reliability, consistency, accuracy, and completeness all contribute to the data quality of a product or project.
High levels of quality in data are achieved when the data is relevant and accessible. A data quality checklist template is used to fix and locate errors in data entry. Dealing with data including review, entry, and validation has a high potential for waste of time and errors.
Data Quality vs Data Integrity
Data quality relates to whether the data is accurate and reliable. On the other hand, data integrity goes one step beyond quality. It requires the data to be accurate, complete, and consistent.
Benefits of Data Quality
Below are some of the benefits of data quality
- Raised confidence in data
Confidence is important in any organization but difficult to achieve, especially when people who use data for decisions are less involved in preparation and collection for consumption.
- Better decision-making
Data quality has a direct impact on the bottom line of organizations as it affects decision-making accuracy. When data is accurate, complete, and timely, organizations can make decisions that lead to positive outcomes.
- Higher scalability
Data needs to evolve and change in growing organizations. Good quality of data is important to ensure that data scales in an organization for new business opportunities and cases.
- Higher consistency
High data quality ensures consistency across the procedures and processes of an organization. In many organizations, different stakeholders may need to analyze the same number of sales but they might be getting the data from different sources for those sales numbers. Incongruency in reporting and systems can negatively affect cross-departmental initiatives and decision-making. A consistent strategy ensures that data is harmonious.
- Decreased costs
Higher quality of data helps reduce costs. When data is complete and accurate, organizations spend less money and time on things like running reports again after initial errors and reprinting documents related to products. Additionally, a higher data quality helps avoid penalties and regulatory fines for non-compliance.
- Time savings
Inaccuracy in data leads to wasted resources and time and inefficient operations, especially when a certain team member spends a lot of time testing data quality.
Data Quality Checklist Template Word
Data Quality Checklist Template PDF
Data Quality Checklist Best Practices
List your Business Cases and Data Quality Requirements
Combine your business discussion and data strategies and develop business requirements and use cases. Centre examples for dimensions of data quality and their KPIs. Make your requirements measurable, specific, relevant, achievable, and time-bound.
Prioritize your Requirements
After you list your business cases and requirements, prioritize them. Your focus depends on your business strategy and goals.
Write and Run a Test Case
Create test cases and check if your critical data inputs match the highest quality requirements for data. Do quality checks and verify expected KPIs and quality attributes.
Think of Boundaries
When you test the six main dimensions, consider the endpoints of the data sets that you are testing. These boundary conditions usually have several quality issues.
Consider negative testing. It describes situations where you have unexpected or opposite behaviours.
Profile your Data
At times you may want to profile the data, cleanse, and monitor it. This reveals unexpected issues in data quality through exploration.
Develop a Data Quality Improvement Plan
After you get the results from your profiling and planned testing activities, you will better understand issues in data quality and their impacts. Develop a fixing plan for critical data issues and work on data that has lesser problems.
Data Quality Checklist Template Excel
Data Quality Checklist FAQS
What data quality checks to look out for?
Some of the common data quality checks are: identifying overlaps or duplicates for uniqueness, checking for null values, mandatory fields, and missing values to fix completeness, and applying format checks for consistency.
How would you evaluate data quality?
A data quality evaluation is executed that determines how well a product complies with the objectives of the activity, specifically in terms of timeliness, accuracy, reliability, and coherence.
What are the most important dimensions of data quality?
The most important dimensions of data quality are completeness, accuracy, validity, timeliness, and uniqueness.
Who manages data quality?
A data manager undertakes data quality management practices to maintain the quality of information. These practices are undertaken from acquiring it to, handling, distribution, implementation, and analysis.