Creating A Data Validation Testing Strategy

We truly are living in a data-centric world. Companies, small and large, are realizing the benefits of big data and data analysis.

Today, most clientele demand leads to vast amounts of data, giving them a competitive edge based on accuracy and correct interpretation of the data. Every second, the volume of this data increases. Thus, it gets tougher to manage data in traditional means. This data is used for business reporting, Forecasting, analysis.

Research reports indicate that poor quality data has been responsible for annual losses of $15 million and over. With all these bad data issues, a lot of companies out there are asking themselves some questions. They’re facing some challenges. They’re asking how much of this data needs to be validated? How do I ensure that I am using the proper data permutation? Am I exercising that ETL logic properly? What are the critical endpoints that need to be tested? How do I verify the data from the various source systems?


A successful continuous testing journey starts with a solid business case and a clear roadmap. Every testing transformation also includes scaling.

Usually, there are four different phases in testing transformation. They are listed below.

  1. Focus on risk-based test automation and quick wins
  2. Build success stories around wins and promote them to get positive attention and highlight
  3. Scaling to serve more teams across the enterprise
  4. Right metrics in place

New techniques, business rules, and intelligence need to be added to the current systems to deal with the complexities of the ever-growing data stack. This process is demanding and tedious. Thus, data validation is vital to ensure the data dealt with is accurate, complete, and of quality.


Data Validation is a process of verifying how the provided data is entirely and accurately moved through the systems according to the business requirements without any loss and whether it is correct and complete. It also checks that the database stays with specific and incorrect data properly. It also ensures that accurate data is available at the target. This testing is carried out on databases after the implementation of transformations.

Data Validation is a sort of database testing that ensures data integrity will not be affected when data extraction, application of logical rules, transformation, and loading into the target system is performed. (ETL- Extract, Transform, Load process in short & ELT – Extract Load & Transform).


There are categories of bad data that we may see in our data validation process, the first being

  • Missing Data (Incomplete Data): Sometimes, data does not make it from source to target. This may be due to an incorrect lookup table or an invalid join in the transformation process.
  • Truncation Of Data is also a vital issue to keep an eye on.
  • Data Type mismatch is another crucial and critical issue that we do find.


The goals of the data validation assessment are to receive an expert evaluation of the current process. Once done, provide recommendations on improving strategy and ultimately a proposal for successful implementation of goals. Let us look at some of the components we’ll deal with and look at our business analysis.

Moving on to the requirements you have on-site, what are the expectations of the business, data architecture that you have implemented? Which technologies are involved? Which jobs schedules for the details? Are you running the ETL testing process itself? How is the testing team looking at requirements and developing their tests? How does that integrate with data or DevOps systems that you have in place? Is there enough resource coverage for the goals that you’re trying to achieve? Metrics evaluation? How are these metrics being reported out? Finally, risk assessment what sort of risks are acceptable or unacceptable based on your current situation?


Types of Software Testing

Software testing encompasses diverse testing activities – test strategy, test deliverables, a defined test objective, etc.

Software testing has two parts:

Manual Testing – Manual Testing is testing any software or an application without using any automation tool. It is a method of verification and validation of an application or software in contradiction of requirements specifications.

Types of Manual Testing:

In software testing, manual testing is of three kinds.

Automation Testing is used in some specific cases to carry out the test scripts without any human intervention. It enhances the efficiency, productivity, and test coverage of Software testing in the most productive way.


Information gathering is very vital for data testing strategy. To gather this information, you’d start with an interview of key players.

  • First, beginning with the business and data analysts responsible for creating the requirements,
  • Then talking with the QA testers to find out how they develop and execute test plans.
  • The developers who are the ones that are coding this ETL process, how they’re performing unit tests, how is their approach for developing this code?
  • The DBA is also a critical factor in the performance and the stress of each of these environments.
  • Ultimately, the end-users are the beneficiaries of the data.
  • Once you’ve completed the interviews Additionally, we’ll look at the process documentation.
  • The requirements in the mapping documentation, your testing process design, how is that implemented?
  • Analysis of the tools being used for your DevOps for data ops. How that’s all integrated?
  • Then look at how everything’s being reported out. And is everyone sort of satisfied with those reporting metrics?

Some of the deliverables you should expect are

  • A detailed analysis report,
  • Recommendations for improvement presentation to your team on those findings, ultimately, a proposal for a path forward? Or how do you successfully supplement goals?

Now, one will understand where you are at in the data validation process.

Challenges prop up when testing unstructured data, especially while dealing with tools in big data scenarios.

Disparate and Incomplete Data

Problem: Businesses today are storing exabytes of data as part of their daily routine. This voluminous data has to be confirmed for its accuracy and relevance for the company. It is practically impossible to test this level of data manually.

Solution: Automation is crucial to big data testing strategy. QA engineers with the requisite skills to create and execute automated tests for big data applications hold the key to high success rates.

Abnormal Scalability

Problem: A fluctuating workload volume, especially on the higher side, can significantly impact database accessibility, networking, and processing for any application. Though big data applications are devised to handle enormous data, they may not handle immense workload demands.

Solution:  Data testing processes must embed these testing methods:

  • Clustering: Spread out huge data chunks equally between all the cluster nodes to the minutest level. By replicating file chunks and storing them within different nodes, machine dependency is reduced.
  • Data Partitioning: This type of automation is less complicated and is simpler to carry out.

Test Data Management

Problem: It isn’t easy to manage test data when QA testers do not understand the components within the big data system.

Solution: First, the QA team should coordinate with marketing and development teams to understand data extraction from different resources, data filtering, and pre-and post-processing algorithms. Provide proper training to QA engineers designated to run test cases through big data automation tools to manage test data properly.


Usually, you have the source data from multiple sources, going through an ETL process into a big data lake. After that, the data warehouse pulls information from that lake via another ETL process. that ultimately ends up in a data mart where the BI and Analytics reports will pull information.

There are a lot of layers as the data moves through the system. The ETL developers involved are responsible for driving that data from one source to another.

Mapping Document

The mapping document lays out the most fundamental pieces of how ETL processes work and stare to capture business rules to map out the data flow.

At its basic level, the ETL process should contain the source input definition, the target output definitions, and any logic applied between the two. Once you have a data mapping in place, there are a couple of different or several different testing methods that you can work with to test them out like: –

Database Schema Testing

Data Schema Testing involves testing each object in the Schema, including databases, devices, tables, columns, keys, and indexes.

– Stored Procedure Tests

Stored Procedure Tests involve checking whether a stored procedure is defined and the output results are compared.

– Trigger Tests

In a Trigger test, the tester must see whether the trigger name is correct in addition to other values in rows and columns.

– Server Setup Scripts

One should perform two types of tests −

  • Setting up the database from scratch, and
  • To set up an existing database.

– Integration Tests of SQL Server

Integration tests follow up component testing. Stored procedures need to be spruced up to select, insert, update, and delete records in different tables to find conflicts and incompatibility.

– Functional Method

Functional testing can be worked out by dividing the database into modules as per the functionality. The functionalities are of two types:

  • Type 1− We try and find out the features of the project in Type 1 testing. For each significant component, please find out the Schema, triggers, and stored procedures responsible for implementing that function and stack them into a functional group to test each group at once.
  • Type 2− In this type of testing, the border of functional groups in a back-end is not apparent. One can check the data flow and see where you can check the data. Start from the front-end.

– Stress Testing

Stress Testing involves getting a list of significant database functions and corresponding stored procedures.

– Benchmark Testing

If the database is devoid of any data problems or bugs, system performance can be checked. Benchmark testing provides inputs should there be any bad system performance.

– Front-end Testing of a Database

Front-end testing gives us insights into back-end bugs.

Market Leading Automation Tools

In larger sets of data with many data testing points, automation plays a critical factor in giving you the ability to handle all of that.

Automation testing tools can be described in two categories, which are as follows:

  • Functional Testing Tools
  • Non-Functional Testing Tools

A functional automation testing tool is used to implement the functional test cases. This type of tool is of two types:

  • Commercial Tool
  • Open-source Tool

Commercial Functional Testing Tools

These tools are not available freely in the market. They are also known as licensed tools. The licensed tools include various features and approaches as compared to the open-source tools.

Some of the essential commercial tools are as follows:

Open-source Functional Testing Tools

Open-source functional testing tools are those tools, which are available freely in the market. These tools have less functionality and features than commercial/licensed ones, but sometimes working on the commercial tool becomes expensive.

The non-functional automation testing tools are also divided into two different categories, which are as below:

  • Commercial Non-Functional Automation Testing Tools

These tools cannot be used freely, as they require a proper license. Unlike the open-source testing tools, the commercial tools have additional functionality and features.

These tools help us to enhance the efficiency of the software product.

  • Open-Source Non-functional Automation Testing Tools

These tools are available freely and hence can be used easily. However, they have less functionality as compared to commercial testing tools.


The ideal goal for any data validation process is to reach a level of maturity where you’re tracking ROI. There are predictive data issues, and you’re achieving auditable results. This is usually done by implementing a successful automated solution. We find that most companies are between levels one and four and striving to get to level five and ultimately achieve the most mature level of testing that one can hope for an organization.


The quality of decision-making in the IT lifecycle delivers sustainable results when test activities produce valuable information.

In addition to the core value from testing, such as getting an insight into product health, detailed bug reports, quality metrics, ROI on automation, Test Activities can indeed be re-engineered to deliver critical information that helps all the stakeholders.

Going beyond the aspects covered by a Test Process Audit, our test experts have put together a Test Value Assurance Framework (TVAF) that guides our customers to study and redesign their organizational Test Activities. We recommend the best practices to produce information that helps decision-making (intelligence). Several unnecessary testing tasks are usually eliminated while bringing accountability in innovative ways.

Test History (test plans, scripts, data, reports, and so on) is a growing asset in many of our enterprise customers, and we apply several techniques such as Data Science in extracting untapped value from revenue, optimization, and {digital} market leadership perspective.

To deliver more value from testing/QA investments, we inspect the current outcome of each essential testing dimension in the Test Infrastructure/Architecture in an enterprise. This includes brainstorming on cross-functional values between testing, operations, customers, and leadership. The assured benefit is to derive valuable information from testing activities and serve each stakeholder personally.

Founded in 2003 and based out of Irving, Texas, Kairos Technologies is a US-based customer-first technology company.

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