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“Good data quality provides better leads, better-understanding customers, and better relationships.”
And:
“Data quality is a competitive advantage that data and analytics leaders need to improve continuously.”
Gartner feels there's a big spotlight on data and data quality.
The World Quality Report has a survey population of 7,150 senior executives across multiple sectors from different countries.
Quite interesting, I think, is that for the first time in 14 years, the World Quality Report mentioned data validation.
They said that 89% of those polled agreed that a robust data validation capability would not only improve efficiencies in terms of time and resources but more importantly, will help to improve business decision-making.
They also said 88% of respondents agree that a robust data validation capability directly impacts customer satisfaction and the accuracy of insights that will help boost business profitability.
One tool to take a closer look at in this space is QuerySurge.
QuerySurge is an intelligent data testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, and Business Intelligence Reports. QuerySurge ensures that the data extracted from data sources remains intact in the target data store by quickly analyzing and pinpointing any differences.
This is one of the few tools that do DevOps for data, automating your pipeline with a tool.
I’m pretty sure that based on this trend, more will follow.
It might also explain the next trend; testers need to know SQL.
6. SQL for testers
According to the IEEE Spectrum Top Programming Languages 2022, the popularity of SQL is on the rise.
It’s Number One in their Jobs ranking, which looks solely at metrics from the IEEE Job Site and CareerBuilder.
They also mentioned that they looked through hundreds of job listings while compiling their programming rankings and found that the strength of the SQL signal is not because there are a lot of employers looking for just SQL coders, in the way that they advertise for Java Testing experts. They want a given language plus SQL. And lots of them want that “plus SQL.”
This also holds for testers.
As I did with programming languages for testers, I also searched test automation and SQL and found multiple job postings looking for automaton engineers who also know SQL.
Speaking of data, get ready for the rise of Synthetic Data in testing.
7. Synthetic Data (AI-driven synthetic data)
Synthetic data is artificially-generated data that is designed to mimic real-world data. It can be used in various contexts, including software development and testing.
In software development, synthetic data can be used to test and debug code without needing real-world data. This can be especially useful in cases where it is difficult or even impossible to obtain real-world data or where using real-world data would pose a risk (e.g., due to privacy concerns). Synthetic data can also simulate various scenarios or edge cases that may be difficult to test with real-world data.
In software testing, synthetic data can be used to test the functionality and performance of a software application. It can help to ensure the application is working correctly and as intended and can also be used to stress-test the application to see how it performs under heavy load.
Overall, synthetic data can be a valuable tool in software development and testing, as it allows developers and testers to work with data similar to real-world data but without the limitations or risks associated with using real data.
And, as you know, data is the livelihood of modern artificial intelligence.
Getting the data right, especially in testing, is one of the most critical and challenging parts of building a robust test suite that leverages AI.
That’s probably why I’ve heard from multiple companies focusing on this space.
Access to quality “fake” data is helpful across the software development life cycle, from your sandbox environments to your development environments, to staging, to testing, to QA throughout the CI/CD pipeline.
