What are the most common misconceptions about data quality assessment and how can you avoid them?

Data evolves over time.New data is continuously added ;existing data is updated ;& data may become obsolete. -->As the business evolves, the data requirements also gets changed. Regular assessments help ensure that data remains aligned with the evolving needs of the organization -->Organizations often integrate data from various sources. These sources may change, new sources may emerge, or old sources may become unreliable. Continuous assessment is necessary to adapt to these changes. In short,doing regular assessments enable organizations to identify data quality issues proactively and address them before they cause significant problems. This,in turn, reduces the likelihood of costly data-related errors.

I agree, but being a doer myself, I suggest it is crucial to start showcasing Data Quality status on a report/ DB/ Domain. It is easy to be caught in the perfectionist trap and to try to set up the perfect framework. Showing is an excellent way to raise awareness and ask for a budget/effort to create a sustainable quality improvement over time

It is typically an easy assumption to do the assessment on data quality, only once or with less frequency due to various reasons. Data grows everyday in every line of business in every organization. So there is a compelling need to profile the data every day or with more often. Continuous and regular interval profiling identifies repeating data quality issues on which the organization can focus on fixing.

Data Quality assessments are not simply finding and fixing data quality issues. It also helps to identify the gaps between the systems, helps to identify the standards not followed by, helps to focus on finding the root causes, helps to identify inconsistencies, helps to identify the gaps in the business processes, etc. It also helps setting new policies and new standards. It also helps formalizing the data governance program.

While there's a technical component to assessing data quality, it starts with the business. The business knows what quality means to them and what to look for. If you leave it up to technology, you will get technical quality checks. There are common things you can check, such as a US address has to have a valid zip code, or a Canadian phone number is structured a certain way. The quality checks you might miss are things like the business really needs to have a phone number for each customer because they believe in a more personal touch. Without that knowledge, the average person would probably think that a phone number is optional.

It is essential to view data quality as an ongoing process and implement data governance practices to ensure quality maintenance and improvement. It should not be treated solely as a business or technical issue. The business can help evaluate the data based on its completeness and timeliness. For instance, determining when the data is required for reporting and whether batch jobs can handle it can help prioritize tasks. Ultimately, the prerequisites depend on the intended purpose.

Data quality is generally perceived as technical work, but it is an organizational task, while profiling activity is technical. The business rules need to come from business team. The data, in general, owns by the business and the details for profiling, such as the business rule, context, criticality, etc. needs to come from the business. Thus the data quality assessment falls beyond the technical arena.

The common myth about data quality efforts is costly in buying tools and services. Everyday software SQL client tools provide profiling options and Database client tools provide various profiling options. It is up to the organization to develop framework, profile and assess their data, and prioritize and focus on critical ones.

A proof of concept, on how data quality fix helps the organization (in processes and in reports that help decision making by executives and other leaders) would nullify the misconceptions and would help move forward with data quality program. Educate the data stakeholders on the importance of having quality data would remove the misconceptions.

1. Key is to understand data quality assessment is point in time analysis 2. Always have built in proactive and reactive control to identify data issues 3. Review the control periodically