Data Quality

 What is Data Quality?

·       In general, Data quality is the assessment of how much the data is usable and fits and process of converting source data into high quality data.

 

        The below factors help measuring data quality:

 

 

1.Data Consistency

2.Data Conformity

3.Data Completeness

4.Data Accuracy

5.Data Integrity

6.Data Timeliness  

 

Many factors helps measuring data quality such as:

·         Data Consistency: Violation of semantic rules defined over the dataset.

·         Data Accuracy: Data are accurate when data values stored in the database correspond to real-world values.

·         Data Uniqueness: A measure of unwanted duplication existing within or across systems for a particular field, record, or data set.

·         Data Completeness: The degree to which values are present in a data collection.

·         Data Timeliness: The extent to which age of the data is appropriated for the task at hand.

·         Data Integrity: Data integrity is the overall accuracy, completeness, and consistency of data. It is maintained by a collection of processes, rules, and standards implemented during the design phase.

The following chart illustrates the main architectural functional blocks.

 

 

7.Data Profiling

8.Data Explorer

 

The different types of functional blocks are:

·         A Profiling perspective where you can use predefined or customized patterns and indicators to analyze data stored in different data sources.

·         A Data Explorer perspective where you can browse and query the results of the profiling analyses done on data.

 

 

 

Importance and Uses of data quality:

·        Data quality is an open source tool

·        Improved data quality leads to better decision-making across an organization.

·        The more high-quality data you have, the more confidence you can have in your decisions.

·        Good data decreases risk and failures.

·        It gives the better and best results.