4 Ways Data Science Could Revolutionize the Testing Phase in Nearly Every Industry
Tags: Data Science, Big Data, Towards Data Science, Data Scientist, Testing Tools

The most successful companies in all industries typically have testing phases that help them develop new products, test new materials, guide marketing campaigns and more.

Data science and big data platforms could collectively upend the testing phase in almost every industry, helping companies save money and better assess their results. Here are four ways that may happen.

1. Improving the Efficiency of Human Testers

Data science won’t remove human testers from the equation, but when used properly, data analytics could help those people more quickly extract valuable insights from collections of data.

For example, people on testing teams get feedback from various sources ranging from comments on a social media feed to the remarks provided during a focus group.

A data analytics platform can help testers make sense of the compiled data and spot trends within it. With the help of data science, people can pick up on patterns that inform the next steps in a product’s development.

2. Assessing Product Safety

One of the reasons the testing phrase is so crucial is because it can help product manufacturers identify issues that could lead to product recalls. IBM has a content analysis platform that screens for indicators that could relate to severe issues with products such as cars.

Although manufacturers can and should use data analytics after the initial testing phase, it’s also smart to do so before a product has an extensive reach in the market, too. By taking that approach, they can save time, money and headaches by identifying possible safety flaws before they can harm the general public or people involved in small tests.

Data analytics platforms examine enormous quantities of data much faster than humans could without help. That means that those tools can identify warning signs that may indicate there are problems with a product going through testing. Testing simulations created with big data could also project what may happen when people use products in particular ways.

For example, they might indicate that the material for the lid on a travel-friendly coffee mug becomes overly flexible after less than 250 uses and that the problem results in an inadequate seal that’s not immediately evident to a user. Then, a person might get burned if the lid falls off when they take a sip. In that case, product engineers would know it’s time to go back to the drawing board and find a more suitable material.

3. Streamlining Regulatory Requirements

Construction, aerospace and several other industries must put their products through fire and flammability tests. They reveal what happens to a product following exposure to an open flame, or how long a product can resist fire. In the case of some fire-resistant doors, for example, they should tolerate flame exposure for at least 90 minutes.

Data science can help companies take care of any required flammability tests in systematic ways, ensuring that their products meet or exceed what the regulations dictate. Also, even when an enterprise’s industry does not make fire testing mandatory, having it carried out can create a selling point for customers who want to avoid unnecessary risks.

Big data can also help in another way by finding at-risk buildings during fire inspections.

City officials in New York utilized this method with a data analytics platform that looked for more than 7,500 risk factors, making inspections nearly 20 percent more accurate. Public buildings have to undergo periodic checks, and this is an example of how data-driven tests have value even after initial testing happens.

4. Decreasing the Time to Market

The world’s most competitive companies know how essential it is to release market-ready products faster than other entities. Succeeding in that feat leads to long-term profitability and dominance in the sector. Many companies rely on big data in the testing phase to get products ready for release faster.

Besides helping enterprises feel confident that products function as intended during tests, big data analytics can help companies assess the most pressing unmet needs in their sectors.

Proctor and Gamble has a presence in more than 175 countries, and the people in those nations have varying needs. The company discovered that big data helped them uncover insights and nimbly make changes to their products or introduce new ones to boost success.

Reducing the time it takes to bring a product to the market pays off when companies have access to reliable insights, too. It’s useless to release a product as fast as possible and realize later that not enough people want or need it to make the new product launch worthwhile.

So, a company may first deploy big data to make sure the market desires the product enough to justify its creation by performing tests on segments of the market to see how they respond to the product.

Next, it could use big data to cut down on redundancy or errors that could make the testing process take longer than it should. Both of these things help products enter the market sooner than they otherwise might.

Causing Meaningful Enhancements

Consumers probably don’t devote a lot of thought to the testing that their favorite products, or the buildings they love to visit, went through during development. But, most of them realize that the tests tend to cause overall improvements.

Big data could help product developers reach their goals with fewer delays.