Data Quality Validation

Enterprises receive voluminous data running into several billions and even trillions of bytes. While they can help businesses with developing focused strategies, they remain underutilised due to their raw and varying formats they are available in that needs cleansing for being meaningful and support decision making.


Indium Software offers Data Quality Validation (DQV) services to systematize and enrich the data, however large and complex they may be. Our customers have achieved next level of technology transformation with the historical data and primed for effective decision making.

Some of the challenges to DQV include

ETL not being mapped or executed properly

Data received from various sources lack cohesion

Indium Software's Approach

Identify and eliminate the data errors that occur during big data testing, for reporting. To solve the business needs, we have developed a comprehensive solution that will make sure that the process is accurate and the results are delivered as promised.

Indium Software's Best Practices For DQV

Right from requirements gathering to the delivery and maintenance, Indium Software has a process-oriented approach that covers the following:

Understand the business requisites
Test case design and preparation of test data
Final/summary report and result analysis
Test planning and estimation/assessment
Execution of tests with bug reports and closure
Test closure

We offer Data Quality Validation services irrespective of ETL tools or technology used. Our services include:

Navigation/GUI testing is equally important to ensure all aspects of front-end reports are covered, and corrective measures are applied.

Post the data transformation process, data correctness test is performed. Apart from validation of end-to-end data, our Software testing procedure also outlines remediation which ensures future data corruption does not take place.

Following the validation testing, accurate loading verification into the warehouse is done by comparing validation counts, aggregates and spot checks. This is done on a timely basis between random actual and target data.

We have automated metadata testing procedures that include close checks on data type, data length, restrictions/index etc.

We provide incremental ETL testing services to check the reliability of new and old data after the addition of new data. We will also verify that the updates and inserts are being processed as expected during the incremental ETL process.

Data transformation testing can get complicated at times since multiple SQL queries may need to be run to verify all transformation rules conform to the business rules. Our techniques ensure saving time on this tedious task.

Our services help prevent:

  • Syntax issues
  • Incorrect reference type errors
  • Bad data (invalid characters, invalid patterns) or bad data models (data types, precision, Null) affect the quality of data.

In order to ensure accurate, reliable and consistent business information, our ETL Testing and Validation techniques ensure production reconciliation.

Adapting your data warehouse to technological changes to be compliant and embracing new/advanced security and performance upgrades is a necessity. Our systematic testing approach with respect to migrating previous data into the new repository helps reduce substantial effort in the pre and post upgrade stage.

Data Quality Validation Tools Expertise

We have vast experience in automated, semi-automated and manual ETL testing. Our expertise with automated testing tools guarantees that only valid data will be delivered. Some of the popular tools in which we have our expertise are:

apache logo

Test your Big Data system for robustness & performance

Suggested Content

Big Data For Real Time Customer Engagement

Case Study

read more
Data Management Brochure
Big Data Security Using Kerberos For A Leading Mobile Engagement Provider

Success Story

read more
How To Load Test Micro Services-Based Applications Deployed In Dockers?


read more