Big Data has the potential to answer many of life’s biggest questions, bring new products to market, and reduce costs for already existing inventions. However, without the right testing, this is just an unmanageable amount of data with nowhere to apply it. This episode welcomes Mike Santori from NI and Jennifer Stirrup of Data Relish to talk about best practices in data testing, how big data can help us, and why clean and accurate data is important to business.  Learn More About: <ul><li>Why traditional data testing methods don’t always perfectly apply to testing big data.</li><li>Big data can be in any format and it’s structure and format aren’t the same for every test. </li><li>How companies like Amazon have been leveraging big data and analyzing mountains of information in just fractions of a second. </li><li>What type of testing is required for analyzing data at such a high level of speed? </li><li>The elements behind testing - performance, functionality, and quality. </li><li>What does it mean to process good quality data? </li><li>How do we make sure that the data is accurately representing the behavior in question before we use it to make decisions or inferences? </li><li>How did Siri and iPhones use Big Data to give us our very own handheld computer and personal assistant in one? </li><li>More about the concerns that arise with Big Data in privacy and sharing very specific details about its users. </li><li>How big data can help us cope with crises like disease and poverty.</li><li>What responsibilities do engineers and data scientists have? </li><li>What was the accident that ultimately led to creating Play-Doh? </li></ul> Resources Mentioned: <a href="https://www.ni.com/en-us.html">NI</a><a href="https://datarelish.net/">Data Relish</a>

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