Case study: Infrastructure costs fell 66%: Examine how unlocking operational data in real time benefits industrial operations. September 5 webcast includes a live demo and case study discussion with an end user and other application experts. Ask presenters how your applications could benefit.

Learning Objectives
- Explore why real-time data analytics is important for industrial operations and the benefits.
- Understand application involved and webcast learning objectives, which include more visibility, real-time analytics.
- Get questions answered, if attending live, about real-time data analytics after application discussion and demo.
Real-time data analytics insights
- Real-time data analytics is important for industrial operations and large benefits can result, such as a 66% decrease in infrastructure costs in one application.
- Application involved and webcast learning objectives explain more, which include more visibility, real-time analytics.
- If attending live to the related webcast, ask questions about real-time data analytics after application discussion and demo.
End-user provides information about application benefits of real-time data analytics, including reduction of 66% in infrastructure costs and decrease in physical space. Single-source of truth helps operations and engineers respond to critical events and improve reliability. Self-service tool helps data users meet metrics in real time. These are among topics in a September 5 (1 p.m. CDT) Control Engineering webcast, “Unlock real-time operations data, make smarter decisions.” Those listening live will have opportunity to question the end user about the application; other experts help present benefits and help answer questions. Attendees are eligible for a certificate of completion.
Why real-time data analytics is important for industrial operations
SNOLAB, a research facility, has challenges that mirror mission-critical industrial applications. In the application, multiple data lakes and resulting lack of visibility was hiding problems with maintenance, ability to reconstruct events and improve maintenance. The application discussed in the webcast looks at how to unlock real-time operations data to improve decision-making.
Smarter, faster decisions are part of digital transformation and Industry 4.0 as the importance of real-time operations data grows. Many organizations are challenged to quickly extract available operational data from automation, controls and other plant-floor systems and create actionable insights to drive operational improvements and business value.
Tools used empower operations engineers with intuitive, no-code, self-service visualization to present data in context. Those involved can swiftly share insights with colleagues and collaborators to meet goals and unlock innovations. Applying user-friendly visualization tools can identify emerging issues and resolve them before adverse impacts to productivity, quality or costs.
Webcast learning objectives: More visibility, real-time analytics, live demo
Attend this webcast to hear how enterprises across various industries gain visibility into their operations. Learning objectives are to:
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Discover how industrial organizations monitor real-time operations and rapidly respond.
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Understand how to easily build visualizations that meet the specific needs of operations.
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Understand how to securely share insights with colleagues in any location, using any connected device.
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Explore a live demonstration of Aveva PI Vision, the data visualization tool used in the application discussed and by organizations across multiple industries.
Presenters are Steven Back, technical services manager, SNOLAB; Kanyin Ogunrinde, presales consultant, Aveva, and Brandon Ney, product marketing manager, Aveva (see photo).
Mark T. Hoske is editor-in-chief, Control Engineering, WTWH Media, [email protected], and moderator for this webcast.
KEYWORDS: Industrial data analytics, real-time-data analysis, industrial process improvement
CONSIDER THIS
During the live webcast, ask presenters how your applications could benefit from real-time data analytics.