- November 12, 2018
- Posted by: admin
- Category: Uncategorized
Big data quality- Quality control and testing plays a crucial part in product development and manufacturing. Without those aspects, faulty products could hit the marketplace and cause reputational damage, excessive costs and can even risk lives due to potentially dangerous consequences.
However, big data is proving to be an essential technology in making quality control and testing more efficient and effective.
By speeding up the time to market
Examining the findings of big data interfaces can enable companies to significantly reduce the amount of time needed to handle validation testing before placing a product on the market. One instance involved Intel combining big data with artificial intelligence (AI) to capture tremendous amounts of data and process it more quickly than humans.
In an instance of validating the new features of computer chips, Intel’s validation teams collect up to 250 GB of new data each week while working with tests that could have more than 1,000 parameters. Intel’s AI solution checks through historical data and finds patterns. Then it uses that information to create tests. This process only takes a few hours, but if humans tried, the feat would require thousands of hours.
Moreover, depending on AI for test execution allows Intel to locate bugs more efficiently while eliminating tests that are not relevant. This approach, according to Intel, reduces the number of tests performed by 70%, helping products reach the market faster without sacrificing quality.
Giving compiled insights that inform improved product design and testing
Some companies may have product tests being carried out all over the world and plan to use the results from those experiments to inform new, enhanced designs. Before big data became prominent, collecting the information from those tests was time-consuming and often required locating users to gather feedback about the product.
However, today’s big data platforms can quickly look at opinions broadcasted on social media or, in the case of an internet-connected device, keep tabs on how people use products in development without explicitly reaching out to them to get their feedback.
For example, big data could find out which features within fitness trackers that a tester uses more frequent and the steps they go through to do so.
Collecting data throughout a period of time and extracting the meaningful sentiments from it could also increase the likelihood of a new product’s later success. Predictive analytics can examinevarious aspects of the product development process and find the factors within it that highlight the things people like the most, as well as the things that frustrate them.
Big data is also capable of predictive models, allowing brands to create thousands of versions of a product in seconds. Proctor & Gamble took that approach when designing diapers, and they used a similar approach to determine when a dishwashing liquid would release particular fragrance notes.
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Article Credit: ie.
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