Summary: <p>It seems most large labs have a go-to data person. You know, the one who had to upgrade his PC so it could handle insanely complex Excel pivot tables? In large electrical engineering R&D labs, measurement data can often be inaccessible and unreliable.</p> <p>In today’s <a href="http://www.eestalktech.com" target="_blank" rel="noopener">electrical engineering podcast</a>, Daniel Bogdanoff (<a href="https://www.twitter.com/Keysight_Daniel">@Keysight_Daniel</a>) sits down with Ailee Grumbine and Brad Doerr to talk about techniques for managing test & measurement data for large engineering projects.</p> <div class="jetpack-video-wrapper"></div> <p> </p> <audio class="wp-audio-shortcode" id="audio-1820-12" style="width: 100%;"><a href="https://eestalktech.com/wp-content/uploads/2018/02/data-analytics-for-engineering-projects-23-ees-talk-tech-electrical-engineering-podcast.mp3">https://eestalktech.com/wp-content/uploads/2018/02/data-analytics-for-engineering-projects-23-ees-talk-tech-electrical-engineering-podcast.mp3</a></audio> <h2>Agenda:</h2> <p>1:10 – Who is using <a href="https://www.keysight.com/en/pd-2747984-pn-N8844A/data-analytics-web-service-software?cc=US&lc=eng">data analytics</a>?</p> <p>2:00 – for a hobbyist in the garage, they may still have a lot of data. But, because it’s a one-person team, it’s much easier to handle the data.</p> <p>Medium and large size teams generate a lot of data. There are a lot of prototypes, tests, etc.</p> <p>3:25 – The best teams manage their data efficiently. They are able to make quick, informed decisions.</p> <p>4:25 – A manager told Brad, “I would rather re-make the measurements because I don’t trust the data that we have.”</p> <p>6:00 – Separate the properties from the measurements. Separate the data from the metadata. Separating data from production lines, prototype units, etc. helps us at Keysight make good engineering decisions.</p> <p>9:30 – Data analytics helps for analyzing simulation data before <a href="http://eestalktech.com/2017/07/13/fast-adc/">tape out</a> of a chip.</p> <p>10:30 – It’s common to have multiple IT people managing a specific project.</p> <p>11:00 – Engineering companies should use a data analytics tool that is data and domain agnostic.</p> <p>11:45 – Many teams have an engineer or two that manage data for their teams. Often, it’s the team lead. They often get buried in data analytics instead of engineering and analysis work. It’s a bad investment to have engineers doing IT work.</p> <p>14:00 – A lot of high speed serial standards have workshops and plugfests. They test their products to make sure they are interoperable and how they stack up against their competitors.</p> <p>15:30 – We plan to capture industry-wide data and let people see how their project stacks up against the industry as a whole.</p> <p>16:45 – On the design side, it’s important to see how the design team’s simulation results stack up against the validation team’s empirical results.</p> <p>18:00 – Data analytics is crucial for manufacturing. About 10% of our R&D tests make it to manufacturing. And, manufacturing has a different set of data and metrics.</p> <p>19:00 – Do people get hired/fired based on data? In one situation, there was a lack of data being shared that ended up costing the company over $1M and 6 months of time-to-market.</p> <p> </p> <p> </p> <p> </p>
