- Present their stories
- Network with peers and partners
- Hear about industry trends
- See the latest from OSIsoft
This year our regional seminars focus on industrial analytics. Analytics is promising to deliver new efficiencies within critical operations. Join us to gain insights into:
- The role of IoT, Edge, Cloud and Data Lakes within Industrial Analytics
- Different analytics approaches for critical operations
- Capabilities of the PI System to support your analytics
- Best practices for industrial analytics
- Analytics case studies
Analytics with the PI System
|Wednesday, October 2, 2019|
|7:30 AM - 9:00 AM|
|8:00 AM - 8:45 AM|
Attendees to the OSIsoft Regional Seminar are also welcome to join an optional pre-event OSIsoft Introductory session. This session will cover the fundamentals of the Modern PI System and is tailored for those that are either new to the PI System and would like an introduction to the technology or wishing to update and refresh their knowledge.
|9:00 AM - 9:10 AM|
|9:10 AM - 9:40 AM|
Today, analytics is promising to deliver new efficiencies within industrial operations. To maximize the value of industrial analytics, you need a strategic approach that addresses edge, enterprise and cloud tools for operations data while minimizing delays and failures from data inaccessibility, intensive data processing, and poor data quality. A successful industrial analytics strategy requires a data infrastructure approach that fosters trust and is optimized for enterprise-wide operational analytics.
This overview session introduces the latest trends within industrial analytics, highlights best practices for success, and shows how the OSIsoft PI System, edge, and cloud technology support different types of analytics for varying audiences – from engineers and managers to data scientists within IT and OT groups.
|9:40 AM - 10:10 AM|
Camso, a Canadian company that is a manufacturer and service supplier of products for off-the-road vehicle, will provide privilege access and insight to their R&D center and how they use the PI System as a data core system in order to leverage test data in a different way than normally seen in manufacturing. The small validation team implemented the PI System through a proof of concept for a year with the audacious bet that the system would double the test output while providing additional data correlation to the engineering community through PI vision. With the work done through the year, the validation team goal was reached beyond their testing field and change the Camso Global IT/OT mindsets and provided stakeholders with a clear vision of the capabilities and requirements for the future.
|10:10 AM - 10:40 AM|
To take advantage of operational data across the enterprise, you need to enable more people to consume the data: maintenance engineers, general managers, data scientists, and executives. From real-time access and exploration to operational reporting and analytics, users have different data, technology, and application needs. Managers need to know if targets are being achieved. Analysts need operational data integrated with business data. Data scientists need a cleaned operational data set to train machine learning algorithms for predictive capabilities. But raw operational data can be hard to understand, difficult to access in real time, and hard to consume without extensive processing, which leads to trust and quality issues. In this session, we’ll demonstrate how the capabilities of the PI System can be used to gather, structure, process and deliver the analytics needs of different audiences throughout the enterprise, ensuring that any person can access, understand, and trust the data.
|10:40 AM - 11:00 AM|
|11:00 AM - 11:30 AM|
Many of today’s visual analytics tools have been created for business and financial applications and users. Operations managers and engineers need analytics tools specifically designed for industrial operations, which can deliver real-time insights into process and equipment performance. In this session, we'll explore visualization approaches and discover the options best suited for your operations and business needs. We’ll demonstrate how industrial engineers can easily build operational views with PI Vision, use tools like PI DataLink for ad hoc data exploration in Excel, and leverage Power BI from Microsoft for reporting business and operations data together – all with minimal technical skills.
|11:30 AM - 12:00 PM|
Enterprise analytics involves data from a complex environment of systems, devices, networks, and departments that span operation technology (OT) and business IT groups. IT groups are rapidly investing in new edge, cloud and analytics technologies: new sensors, edge devices, Microsoft, AWS, and SAP analytics tools, and open source data lakes in the cloud. The result is an even more complex ecosystem of existing and new sources of data and technology that spans IT and OT, all while requiring utmost security.
This session focuses on the best practices for leveraging existing and new sources of data from both OT and IT investments. Learn how a data infrastructure approach can securely enable OT and IT to share and deliver analytics-ready data for both operational analytics and enterprise analytics. We’ll demonstrate how the PI System can enable enterprise agility to adopt new technologies and analytics methods that leverage edge, cloud, machine learning and artificial intelligence.
|12:00 PM - 1:30 PM|
|1:30 PM - 2:00 PM|
Advanced analytics tools and platforms allow businesses to ask more from their operational data and obtain new insights. Yet 60% of Big Data projects fail. Simply ingesting raw operational data into your favorite business intelligence tool or data lake creates significant challenges and barriers to success. Problems with data access, data quality, data structure, and outputs from “black boxes” typically lead to unreliable conclusions or predictive algorithms that are never adopted.
In this session, we’ll cover the challenges of using new analytics tools with operational data; share how the PI System and the PI Integrators deliver analytics-ready data; show how subject matter experts in operations can easily curate data that can be understood by business and IT operations; and demonstrate how the 3rd party advanced analytics tools from Amazon, SAP, and Microsoft, can consume the data for trusted and quality analytics.
|2:00 PM - 2:30 PM|
McMaster University : Experiential Learning of Data Acquisition and Processing with a Cloud Computing Platform
This presentation covers how PI is used in real world projects in the Photonics curriculum at McMaster University. In these projects PI is used as an IoT data acquisition platform to demonstrate, apply and test error propagation, distribution and test of distribution, correlation and cross-validation, data rejection and signal processing. The Photonics program changed delivery of statistical analysis instruction from lecture format to the ‘experiential learning module’ using PI to cover data acquisition and statistical analysis. Projects include physical experimental setup to continuously measure environmental parameters (temperature, humidity, light, imaging, etc.) with a set of multi-modality sensors in an Internet-of-things (IoT) big data platform. As a platform, PI also supplies the visualization tools to deliver a multi-dimension view of complex data streams (e.g. time-lapse of statistical distribution) that accelerates students’ mastery of quantitative attribute measurements and improves the qualitative feedback from their projects.
|2:30 PM - 3:00 PM|
|3:00 PM - 4:30 PM|