Published On: May 5th, 2021Categories: Data GuideBy 6.1 min read

What is BI testing and why is it important?

In software development and maintenance, testing is a regular practice that delivers huge benefits to the development process, allows for better communication between teams, increases the speed of development, improves user experience and results in less re-work.

However, when it comes to BI environments and their maintenance, we usually see:

  • An abundance of manual testing (which is costly, time-consuming, not much fun and also leaves a lot of room for error)
  • A lot of rework time (both for BI teams and for end-users)
  • Ad hoc fixes
  • Stress and lack of trust in data
  • Costs of having bad data

So why isn’t it common practice to test BI data when the benefits of a proactive approach include increased credibility and a more data-driven organization? Well, BI and reporting tools are historically driven from the business side, where a testing mindset is not necessarily present. But modern BI is not just about data presentation and visualization — there is a lot of business logic and data transformations that take place at this level, too.

As BI environments are shifting towards being built by IT (with a more intricate software-based mindset and processes), testing is beginning to appear on the agenda. But how exactly does testing work?

What test types to include in your BI testing framework

All types of testing fall under two categories – functional and non-functional. Functional testing tests the behavior of the software while non-functional tests aspects of a software that doesn’t relate to its function, such as performance, usability and reliability.

This article focuses on functional testing, specifically related to baseline testing, regression testing and unit testing — all to ensure that the outcome of your BI data is consistent and accurate.

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Baseline Testing

Baseline testing involves making sure that all object-type or other data assets are still present after the application has been reloaded — ensuring that the UI of the application has not changed.

Unit testing

Unit testing describes testing individual units of software in order to ensure that the unit itself is performing correctly.

Regression Testing

Whenever developers change or modify BI functionalities and/or features, there’s a huge possibility that these updates may cause unexpected behavior. Regression testing is performed to make sure that changes or additions haven’t broken any of the existing functionalities. Its purpose is to find bugs that may have been accidentally introduced into existing builds and to ensure that previously removed bugs continue to stay away.

Comparison testing

Comparison tests allow you to compare elements across different environments, ensuring that certain assets remain consistent across different solutions and software. For example, this lets you test and make sure that the same KPI in your Qlik and Power BI environments has the same value.

System Testing

System testing is testing conducted on a complete, integrated system to evaluate its compliance with the specified requirements. System Testing is very important because it verifies that the application meets the technical, functional, and business requirements that were set by the stakeholder.

Just so you know

NodeGraph can help with automated BI testing. By using Data Quality Manager, you can identify issues before they reach the end-user, establish a single version of the truth and ensure that your solution is always running smoothly.

To find out more about Data Quality Manager, click here.

When to start automating and how to select the right tool for automated BI testing

Why test data after it enters your BI solution

There is a misconception that a visualization tool is only used to visualize data. Traditionally, all the transformations used to happen in the database or data warehouse (or anywhere else outside the BI solution) and then you used a BI tool to visualize that data. But this is not the case with modern BI tools. Today, there are so many things you can do within your business intelligence tool — including data transformation. Instead of going to the IT department, people responsible for visualizations make changes themselves.

It’s also worth noting that modern business intelligence tools (like Qlik, Power BI and Tableau) are extremely good at connecting to other data sources. We have a customer that just by looking at the automatically generated data landscape map in NodeGraph realized that only 50% of their data was actually travelling through their data warehouse. This is not a unique case.

The importance of data trust when it comes to BI adoption

Questioning your data is definitely better than blindly trusting it. However, the ideal situation involves implementing a data quality framework that will allow you to trust your data without any doubts. For this, testing is key.

Without such a framework, users often find themselves questioning their data without getting accurate and quick answers — having to spend 20-30% of their working time searching for the right data and/or validating said data. Testing can help companies achieve a single source of the truth, ensuring that business data I always fulfilling data quality requirements.

Automated alerts to help enforce data quality framework

Even when your data quality is high and business teams trust their data, your business and your data landscape is constantly evolving. This makes automated alerts crucial — ensuring that you are immediately informed if something unexpected happens in your data solution. Data Quality Manager offers automated alerts about the status of your tests via email and/or Slack.

Data and analytics governance that actually works

Once you’ve consolidated your data and analytics governance program with its policies, processes, roles and responsibilities, the challenge is to make it is easy for everyone to follow and maintain this. This means you should stay away from implementing processes that involve lots of manual work. Automation is key.

Find out more about NodeGraph’s data and analytics governance solution.

A continuous and iterative data testing framework is the only way to go

Automated data testing in BI solutions solves a long-standing and widespread problem of poor data quality, incorrect data and low data trust. But like with cleaning or documentation it’s a routine and not that exciting task.

Fixing the problem once and for all with the help of a continuous and iterative data testing framework largely supersedes sorting issue by issue in real-time without guaranteeing that they won’t return.

Find out how you can implement BI testing using NodeGraph Data Quality Manager.

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