Big Data

The Impact of AI in Big data testing

Big data

The digital world is forever evolving. And throughout the past 60 decades, we’ve seen massive innovations in this domain. Those innovations took the world from the use of paper and pen, and into the world of electronics. 

Plus, we’ve witnessed the development of daily devices with a never-ending new feature, such as smartphones, the internet of things, and AI-driven analytics.

But it Doesn’t End There.

With the COVID-19 outbreak, the world economy is changing. Businesses, jobs, and the everyday routines followed by people are transforming just to adapt to the new environment. And with that comes the need for new technologies, which means a new digital transformation.

In fact, the majority of those organizations are willing to consider new technologies, regardless of their level of technological sophistication!

This is Where Big Data & AI Comes In.

With COVID-19, the preferences and habits are likely to change. And there’s a lot of analytical data that businesses need process just to adapt. That data will be too much for regular analytics software (and human effort) to sift through.

And this is where artificial intelligence comes in. Artificial intelligence and big data testing go hand-in-hand, and will likely be a key tool in the digital evolution of businesses!

How So?

Digital transformation isn’t just about improving gadgets, their performance, and getting new software. It has to have a set of end-goals. And one of those is delivering an excellent experience to end-user of a service.

User experience is a difficult form of data to collect. Lots of surveys, monitoring, and feedback needs to be collected and sifted through. So having the latest performance technologies isn’t enough.

Secure and robust platforms are also needed, and they should provide an experience that caters to the needs of individuals.

AI is Needed to Analyze User Experience Data.

Testing methods need to improve in a way that considers how users perceive a product. This isn’t easy to perform. After all, the ever-changing standards and need of the market are difficult to record. Hence there is a need of software testing experts. That is, most human testing processes look at the functionality of a digital product. They simply ensure that the product works. But, they don’t look at how the market will perceive it.

This is where Artificial Intelligence Testing comes in. AI software can learn and upgrade their algorithms constantly, based on user experience data. This allows AI-testers to become important pillars for an organization when testing a new product!

The Link Between Artificial Intelligence and Big Data

For artificial intelligence testing to be successful, a constant influx of data is required. This influx comes from big data analytics, creating a co-dependent loop between both systems. It creates a self-sustaining system that’s constantly in evolution.

Some Stats.

Each day, 2.5 quintillion data bytes are created by consumers and businesses. That amounts to 1.7MB per second of data created (and per person). That’s a lot of data. And much of that can be integrated by AIs to better analyze human beings and how they think, and the best approaches to software design that’ll change the world.


Not all data is equal. Each datum piece needs to be analyzed for consistency and reliability, especially if they come from faulty sources. So a sense of critical insight is required to analyze thoroughly the influx data. And this is necessary so that organizations and enterprises don’t get an incorrect idea on what their market needs.

AIs can assist in this process. They can manage the testing of end-to-end integrators and sources, ensuring data that is reliable and clean that can be safely integrated.

Other Challenges Faced by Enterprises.

While many organizations have a desire to advance their technologies and equipment, they still need to go through a cost barrier. Upgrading technologies are expensive. This means changing IT infrastructure, getting new hardware, in addition to training employees in the use of new systems.

It also means migrating entire databases into new mediums, such as implementing cloud systems or relying on the Internet of Things (in addition to other technologies). This can lead to issues of integration. Also, disruptions (and if done incorrectly) the losses of data in the midst of the migration process can happen. As a result, data migration testing is a necessity here. It helps with the verification of non-functional and functional aspects of an application after migration.

Information Utility.

That’s another problem to consider, which is solvable by AIs. Enterprises can’t use all the data they receive in the collection process. Additionally, there are always new data to integrate, which means that old data gets redundant. This requires many organizations to collect data real-time (while also processing it real-time).

AIs can assist with that process. Artificial Intelligence can process minute levels of data immediately while updating the analytics you see in real-time. They can test and certify big data feeds, and they do so while providing better sampling, catalogue techniques, and excellent big data performance tests.

But Why is Artificial Intelligence an Excellent Approach for Big Data?

Artificial Intelligence software packs the processing power that allows for large amounts of data processing, especially when testing software. In terms of scale and speed, the human brain cannot match the power of an AI. And this makes Artificial Intelligence faster and more accurate.

Speaking of accuracy, artificial intelligence has a better capacity for memory and storage than a human being. This ensures that they never miss out on critical components in a system, especially when testing a product.

Additionally, Artificial Intelligence can be automated. This reduces the overhead costs of testing, and it allows for frequent testing in shorter timeframes, which in the end lead to high-quality products. It allows companies to fine-tune their products before release. And this raises the standard that each industry needs to follow on what’s suitable for consumer markets.

The Next Generation’s Digital Transformation: AI is the Answer

Organizations can create smart assets using AI. And those smart assets are key in helping businesses make the right long-term decisions. Those smart assets include data repositories, better databases that are constantly updated, and analytics that never fails in market analysis.

Artificial intelligence can also help businesses predict possible problems in the market, better analyze risks, while finding better approaches to tackling product development issues. Many organizations can offer a business the big data testing they need. And they’re slowly integrating AI to make that a reality.

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