Top 5 Data Quality tools

Unlock Success with Data Quality Dimensions using Data Quality tools

In today’s world where data is the ultimate source of power, the quality of data cannot be ignored or taken for granted. If your data is of Good quality, you’ll always be ahead of your competitors in making informed business decisions, customer trust and satisfaction, reduced operational cost, etc. Let’s understand what is data quality and how you measure the quality of your data.

Data quality is measured with the help of the Data Quality Dimension. The 6 common data Quality dimensions that can help you measure the quality of your data and also give you the issue with your data.

Top 5 Data Quality tools:

Informatica Data Quality: Provides comprehensive data profiling, cleansing, and monitoring capabilities.

IBM InfoSphere Information Analyzer: Offers data profiling, quality assessment, and metadata management.

SAS Data Quality: Includes data cleansing, matching, and monitoring features with integration capabilities.

Oracle Data Quality: Provides data profiling, cleansing, and enrichment tools integrated with Oracle’s broader data management solutions.

Talend Data Quality: Offers data profiling, cleansing, and monitoring features as part of its broader data integration platform.

There are more tool in the market, you can choose the one that suit you best

Data Quality Dimensions:

Data Quality DimensionsCOMPLETENESS:
This basically checks for the null values in your data set(column).
The question you ask yourself: Is all the required information available

CONSISTENCY:
 This checks for consistency of your data across your organization.
Example: if an employee has left the organization then his status in the HR department and Payroll must be INACTIVE.
It cannot happen that in HR it’s ACTIVE and in Payroll it’s showing INACTIVE.

CONFORMITY: This ensures data follows a set of standard data definitions like data types, size and format.
Example: Date of birth of employees must be in ‘DD-MM-YYYY’ format only.
The question you ask yourself:  Do data values comply with the Business specified format? If so, do all data values comply with these formats?

ACCURACY: The degree to which the data correctly reflects the real-world object.
Example: 1. Sales of a Business unit must be a real value.
2. The address in the employee table must be a real address.
Question to ask: Does the data object in question accurately model the real-world object?

INTEGRITY: It means the validity of data across the relationships and ensures that the data can be traced back to the source and is connected to other data.
Example: In a customer database, there must be a Valid Customer, Address and relationship between them, else it’s an orphan record.
Question to ask yourself: Is there any data missing important relationships, or links?

TIMELINESS: Timeliness references whether the information is available when it is expected and needed. timeliness is very important and is reflected in
– Customer service providing up to date information.
– Credit system checking in real-time on credit card account activity.

Steps to analyze the quality of your data using Data Quality tools:

steps implemented by data Quality toolsAssuming you have flat file(.csv) for perusal.
 
1. import the file in tool and do the Profiling.
Profiling will give you the overview of your data set. Number of null values in you column, format of phone number, all possible values for city/state, date patterns used, etc.
2. Once you have the profiling results with you, you need to discuss with the business and explain them about your observations and try to get the business rules that they want to implement. For example, there cannot be null values for phone number for a valid customer.
3. Then you need to code the buisness rules in the tool that you are using. Run the rules on the avaialble data set and generate the output showcasing the quality of client data.
4. Create scorecards and trend charts for the result set.
5. Suggest fixes to the business and once business confirm the fix is implemnted, repeat the step 3, 4 to see whether the quality of data has improved or not.
 
This is a simple brief on how we go about checking the quality of client’s data.

Top 5 Data Quality tools in the market:
1. Informatica Data Quality
2. IBM InfoSphere Information Analyzer
3. SAS Data Quality
4. Oracle Data Quality
5. Talend Data Quality

  • Completeness
  • Conformity
  • Consistency
  • Accuracy
  • Integrity
  • Timeliness
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