What Is Data Quality Assessment? It is the process of finding and exposing all the business and technical issues related to data in an organization so that data cleansing and data enrichment processes can be executed across the organizational data using appropriate data quality tools. Here are some technical issues which can be identified by data quality assessment tools.
1. Inconsistency in the data structure, values, formats
2. Missing data and default values
3. Data with incorrect fields
4. Spelling and typing errors
Recommended Data Quality Assessment Tools
The recommended data quality assessment tools should be able to do the following:
1. Identifying data that requires data quality assessment – data that is critical to business operations and reporting
2. Understanding which data quality dimensions are to be assessed and what is the associated importance
3. Defining ranges for every data quality dimension, categorizing data as being high or low quality
4. Applying all the assessment criteria to data items
5. Reviewing the results of data quality initiatives and determining if the data quality is acceptable
6. Whenever possible, the tool should take a data quality improvement approach and other initiatives including Salesforce data management.
7. Performing data quality assessment checks on a periodic basis to ensure the quality of data and data initiatives in the organizations.
Data Quality Dimensions Assessment By Data Quality Assessment Tools
All of the recommended data quality assessment tools will be able to assess and monitor the following six quality dimensions of data at periodic intervals.
1. Data Completeness – the percentage of complete data stored in the system
2. Data Uniqueness – no data is recorded more than once throughout the system and each data entry is unique based on multiple indicators in the system. For example, there are two names of the same person in a school. The names are Frederick and Fred, but the person is the same and therefore this data is not unique.
3. Data Timeliness – the degree of reality shown by the data and relevance to the time. For example, in a school Monica updated her contact details on July 12th but it was only updated on July 15th. This indicates a delay of 3 days in the system.
4. Data Validity – If the data conforms to the syntax, format, type and range according to business specifications, then data is considered to be valid.
5. Data Accuracy – the degree by which the data is able to describe the real world associated with it. For example, a student records his actual date of birth in non US format like DD/MM/YYYY and the date changed to month by the school. This is non-accuracy of the data.
6. Data Consistency – the absence of variations when we compare two or more representations of the same data. For example, a student’s date of birth should be similar in the school’s register as well as in the school’s database.
A data quality assessment in combination with a data quality audit tool can help in ensuring a higher level of data quality in the organization, thus allowing the organization to perform better decision making and achieve growth.