A recent article in Digital Engineering posits that companies need a framework and tools to gain actionable insights from data to see operational improvements. As they describe it:

“The value of the analytics capability comes into play, however, only when the insights derived from the data are actionable. This occurs when companies convert raw data into information that can be used to enhance decision making and process optimization.”

The article discusses the workflow for collecting, preparing, modeling, and providing value through data analysis. Data analytics may start off relatively simply in an organization but can eventually expand to include tool sets for handling descriptive, diagnostic, predictive, and prescriptive analytics. In all of these cases, it is essential to have context built into your workflow, particularly to develop a complete and robust digital thread.

As Rob says, context is necessary to assess the real value of analytics. Raw data in itself has no value. Putting the data in context allows an organization to make that data actionable. One example cited in the article is about assessing the various meanings of vibration data from a machine. If you don’t have context, you can’t determine what vibration levels indicate a failure might be coming. But with that context, engineers can use the same or another tool to alert an operator when abnormal vibrations are found.

According to Rob, when you combine data streams with actual performance, and tie it to the contextual data, that’s when it begins to make sense. Data in context provides actionable insights that can be used in future iterations of the product, designing a new product, or providing updated software to improve performance. Although basic analytics tools should be part of any PLM platform, that shouldn’t be where they stop. Companies can use multiple sources for analytics with a solid PLM platform that provides the foundation for working with these tools to adapt for the future. Even more importantly, products should be designed with rapid feedback loops to deliver performance insights that will inform the next generation of products.

The article concludes by acknowledging that a robust digital thread can enable systems that deliver actionable insights. To read the full article in Digital Engineering, you can find it here. We would love to hear your point-of-view on this topic.

To learn more about Aras and how we support a robust digital thread, visit us at www.aras.com.