The adoption of digital technologies is changing the way businesses run their operations, product/service development, and serve customers to enhance their digital experiences. At the center and front of this transformation is data, which provides businesses with insights needed to make critical decisions, understand business trends, identify opportunities, and build on strengths.
However, though businesses are flooded with internal and external data, there is no guarantee for data quality. The data is fragmented and often duplicated across the organization. In some cases, it is also incomplete, inaccurate, and unreliable. A McKinsey report on data in the pharma industry shows that increasing data complexity will throw up several challenges such as lack of quality and inherent bias, limiting the application and usefulness of real-world evidence.
This is a challenge, as data needs to be reliable for making meaningful decisions and determining its effective usage. This makes testing of data for quality critical and imperative for data handling at every stage of the process- right from collecting data and analyzing it to prepare it for presenting the data to the stakeholders. The quality of data will influence the quality of decision-making, making data verification and validation crucial.
Data validation, however, is often skipped due to the common perception that it delays the pace of transformation. While in the short term, this approach may seem beneficial, in the long run, it leads to inaccuracies, repetitions, and other data-related issues, which will impact decision-making and render the transformation ineffective or of limited use.
Instead, businesses can adopt automated validation processes through which the pace of data validation can be quickened to test the data quality for faster digital adoption.
The Need for and Types of Data Validation
Data validation for accuracy, clarity, and integrity mitigates the risk of potential defects in the digital transformation project. But along with testing the quality of data, validating the data model is also important as any poorly structured data model can throw up unforeseen issues when software and apps access data files.
To prevent this, businesses must run a variety of tests to validate the data and the data model. Some of the foremost aspects to be validated are data consistency, integrity, format standardization, data type, uniqueness, and ensuring there are no null values.
Format standardization will help to improve the structure of the data models and ensure compatibility with the applications for which the data is being used. Another standardization is with reference to the file formats for easy mapping and access.
Data validation can be done using scripts where codes are written to compare data values and structure with defined rules to ensure that the information meets the quality parameters. However, this requires a knowledge of coding and can be time-consuming for large volumes of data with an element of complexity. Open-source and proprietary tools are available and can be selected based on the business objectives and how the features of the tools serve those needs.
The End-to-End Data Validation Process
McKinsey recommends a framework-based approach to data validation. Data quality testing must be incorporated at every stage of the data workflow and must include the three steps of determining the sample data, validating the database, and validating the data format.
Selecting the sample data is a critical step for large volumes of data. Getting the size and the error rate right will impact the project’s success. For validation of the database, determine the unique IDs and the number of records needed for comparing source and target data fields. Data format validation should check for incongruent and duplicate data, null field values, and mismatched formats.
A clearly defined set of rules will ensure that only the correct type of data, in the right format or a specified range is accepted. Where applicable, one should use a valid list of standardized values such as postal codes and industry codes for data validation. Businesses should also check for consistency and uniqueness to avoid duplication and ensure no fields are left blank.
A batch control audit is a commonly used in-built data quality testing process to check the correctness of batches of input data, especially in the data-preparation stage. Batch control can be of two types:
Sequence Control: The records in a batch are numbered sequentially to ensure the presence of each record during data validation.
Hash Totals: Selected field values in each record are totaled or the total number of records counted and checked at the time of data validation. These are also called hash totals.
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Indium Digital Assurance with Data Quality Testing
Indium Software is a cutting-edge technology company that integrates digital engineering with quality assurance to provide businesses with end-to-end solutions for improving their business outcomes quickly and efficiently. We work closely with our customers to understand their business objectives, data sources, and the current infrastructure to create bespoke digital transformation solutions and meet the desired end-state.
Our digital assurance stack integrates quality assurance best practices with quality engineering, future tech testing, and advisory services. Data assurance and ETL testing form a crucial part of the overall solution with automation to accelerate the process and deliver quick and effective results.
Indium brings to the table more than 20 years of experience in QA and Testing and a team of more than 400 SDETs (Software Development Engineers for Testing).
Indium has a well-established digital and data engineering practice that is growing fast and has helped us be counted as a trusted partner for the world’s fastest-growing companies in digital assurance. Through increased automation, productivity, and quality, we offer a value-driven transformation from QA to QE.
To know more about our digital assurance capabilities, visit:
Our data assurance capabilities include:
- Ensuring that the data is fit for purpose and the qualified data is extracted, transformed, and loaded from the source into each layer with data lake/data warehouse testing
- Ensuring data completeness for data validation including app modernization/digital transformation
- Ensuring data consistency, appearance, and performance
- Performing data validation of the underlying data models/data when migrating from legacy infrastructure to a cloud-based platform
There are some open-source and proprietary tools available that can be customized to meet your business needs. If you have programming skills, you can also write your own code.
With automated data validation, large datasets can be verified. It reduces the dependence on human resources and the associated errors. Logical rules can be run automatically and efficiently.