Say this much for the "reproducibility crisis" in science: It's poorly timed. At the same instant that a significant chunk of elected and appointed policymakers seem to disbelieve the science behind global warming, and a significant chunk of parents seem to disbelieve the science behind vaccines … a bunch of actual scientists come along and point out that vast swaths of the social sciences don't stand up to scrutiny. They don't replicate--which is to say, if someone else does the same experiment, they get different (often contradictory) results. The scientific term for that is bad. What's good, though, is that the scientific method is built for self-correction.

Manufacturing operations depend on getting the right information at precisely the right moment, ensuring that products get built on time, to quality specs. With the latest enterprise resource management (ERP) software, this critical data flow is often coming via the cloud, as more manufacturers become comfortable with it as a repository for key manufacturing information. With ERP software delivered via the cloud, Big Data is also more easily leveraged for Industrial Internet of Things (IIoT) applications. In this application, advanced analytics do the data crunching required for processing the flow of data, including operational metrics and inventory information. Along with offering more mobile apps that funnel factory data directly to users' fingertips, many ERP software developers are also testing newer technologies like artificial intelligence, augmented reality/virtual reality (AR/VR) capabilities, and machine learning and advanced analytics that can handle the Big Data inherent with many IIoT/IoT manufacturing data scenarios.

Every year the US government spends $5 billion on cancer research. And yet more than 8 million people still die every year from the disease--despite the frequent refrain that a cure is just around the corner. Scientists today are exploring more promising new technologies than ever before: whole-genome sequencing, liquid biopsies, mRNA vaccines, AI-powered imaging analysis. But that doesn't necessarily mean they're more promising. No number of flashy new disruptors can fix cancer research's real problem: much of its data can't be trusted, because it was never validated.