What is just enough industrial data analysis?
Supply chain constrictions, pandemic and labor shortages have raised the need for more effective industrial data analytics. Get guidance from case studies and audience benchmarking in an April 21 webcast, archived for a year.
- Determine the right amount of industrial data analysis to do.
- Learn the obstacles to implementing industrial data analytics.
- Ask questions, compare benchmarks during an associated webcast.
What if your just-in-time (JIT) supply chain strategy limits were exposed in a global pandemic. Is just-enough industrial data analysis working for operations? Is the right data getting to the right people to optimize operations in time? Where are the bottlenecks and how are they being addressed? Where’s data going to become information and who’s seeing it? In the cloud or on premise or both? Are your knowledge brokers seeing the right information quickly enough to make the right decisions, or are your analytics too much, too late to be effective?
In an RCEP webcast, “Just enough industrial data analysis?” live on April 21 and archived for a year, Laurie Cavanaugh, director of business development, E Tech Group, and Matt Ruth, president, Avanceon:
- Determine if just-enough data analytics provides enough benefits to operations.
- Identify if enough data intelligence (results of analytics) is getting to people who matter.
- Examine bottlenecks in data analysis and how to address them.
- Review tools and architectures for eliminating bottlenecks.
- See lessons learned in applying data analytics (too little too late or just enough in time).
Poll questions during the webcast will benchmark participants industrial data analytics maturity level and will look at obstacles to more effective application and use of data analytics. Webcast is moderated by Mark T. Hoske, content manager, Control Engineering.
Accelerated demand for industrial data analytics
What was appropriate data analytics for an organization in the past, may no longer be appropriate, given the disruptions and drivers accelerating demand.
Capabilities of modern industrial data analytics have changed, as software can work inline and in real time, often integrating with other software platforms in use in plants or facilities.
Barriers to adoption include technical and non-technical.
Looking at case studies where non-technical and technical barriers are addressed can help others accelerate use of effective data analytics.
While figuring out what’s “just enough,” identify key data, ensure it’s in context with a historian or data warehouse and create reports and dashboards. A team can refine and define use case needs and expand as needed, making data analytics more descriptive, diagnostic, prescriptive, predictive and integrated.
The 1-hour webcast has more information, and those participating live will have an opportunity to question the expert presenters. Date and time are April 21, 11 a.m. PT, 1 p.m. CT and 2 p.m. ET. Those registering ahead of time will receive a reminder email with login information.
Laurie Cavanaugh is director of business development, E Tech Group; and Matt Ruth is president, Avanceon. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, firstname.lastname@example.org.
KEYWORDS: Industrial data analytics
Have you re-assessed what industrial data analytics are being used where recently?