The 6 Dimensions of Data Quality
Data Quality is not a one-time process, in-fact, it is a dedicated and continuous process that includes considering a plethora of aspects that are involved in the collection process of data. Sometimes decisions have to be made fast and the existence of the data should be of high-quality where the executives across different hierarchies can utilize it to make decisions smoothly.
Globally, as more and more companies are going through digital transformations, data management and its complexity is growing and at the same time, the Impact of bad quality of data is also on the rise. Often companies come across data quality issues, such as, when data is entered, edited, manipulated, maintained, and reported. To ensure data is trustworthy, it is vital to understand the key dimensions of data quality in the first place.
The dimensions represent the criteria, views of measurement attributes for the problems of the data quality that can be interpreted, assessed, and improved independently. The comprehensive data quality can be assessed as a collection value of independent dimensions that are relevant in the application context.
Data quality ensures that information is completely accurate and consistent in the enterprise which ultimately contributes to the successful Implementation and execution of Data Governance.
A single Data Quality Dimension may require multiple data quality rules to be created for a measure to be processed. Bad quality data leads to slow and inaccurate decision making. It is also vital to measure the data quality if you want to use enterprise data confidently in analytical and operational applications. Undoubtedly, only good and high-quality data can provide you with accurate analysis that drives business decisions with trust.
There are many results of bad data.
- Expensive and ineffective processes
- Less productivity and growth
- Poor customer relationships
- Poor decision making
Importance of Measuring Data Quality
It is a challenge to determine the data quality precisely. It all depends on considering multiple data attributes to get the measurement approach and correct context to the data quality. For example, the customer data in the retail business must be accurate, complete, and available when required. To run a marketing campaign the customer data needs to be consistently accurate across all the channels.
Measuring data on multiple dimensions by following six key dimensions of data quality that are to be used.
- Data Quality Dimension #1: Completeness – Data can be called complete even if the optional data is missing. The data shall not only serve the given purpose but also meet the expectations to be considered as complete.
- Data Quality Dimension #2: Consistency – Data across all the respective systems should reflect consistency and be in sync with each other across the company
- Data Quality Dimension #3: Accuracy – Accuracy is the level to which data correctly reflects the real-world objects
- Data Quality Dimension #4: Conformity – Conformity is all about the data following a set of standard data definitions such as data size, format, and type.
- Data Quality Dimension #5: Integrity – Integrity here means, validity of the data across the organization which ensures that all the data in the database is capable of tracing and connecting to the other data
- Data Quality Dimension #6: Timeliness – Timeliness is all about whether the required information is accessible whenever it is expected and needed
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