[Michael Fourman]

Hi Sarah.  Georgia Transmission funds EPRI’s Asset Management Analytics program (Program 34) and we will be using their model for projecting wood pole reject rates.  Their models are based on an industry-wide data set.  I hope this is helpful.

——————————
Michael Fourman
Georgia Transmission
——————————
——————————————-
Original Message:
Sent: 07-20-2022 09:56
From: Sarah Green
Subject: Pole Replacement Analytics

We are currently exploring the idea of moving to a more data driven pole replacement strategy and plan to address electric reliability and resiliency. As we start this exploration we were curious if there are any other utilities that currently utilize an analytic model to inform which of their poles they replace and when.

We would be interested in having further conversations with any one that has this strategy already in place to understand the type of data that is utilized for your model, how often it is ran, where it is ran (on-premise or cloud technology), and how long it took to develop.

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
248-974-0126
——————————

[Kevin Praet (Adm)]

Hello Enterprise Analytics Community Members,

I hope all is well! We are one week away from our July (2022) Enterprise Analytics Community Conversation and want to get you excited about the session taking place. On Thursday, July 28 at 1:00 PM CT, we’ll be joined by @Andy, Vice President, Business Data & Insight at Entergy and @Yong, Manager of Performance Center at DTE Energy who will be leading a discussion around, “Analytic Fails.” As this discussion will be interactive, we encourage you to come prepared with questions and your insight.

Learn more about this session and our speakers below!

Session Description:

Want to Really Help Others? Talk About Your Failures, Not Your Successes. Even though most people don’t realize it, the latest research shows that mistakes, errors, and disastrous decisions are more useful for helping people perform better. Experience is often the best teacher: Lessons learned are often the best lessons; the more painful the lesson, the better the learning. But you can learn some of those lessons from other people–and just as importantly, help them learn a few lessons from your mistakes. That’s easier said than done, though, especially since science shows we’re naturally reluctant to share our failures with other people.” We would like to thank our roundtable participants for sharing their stories of “analytics fails” and we hope you learn from their lessons learned. We also encourage you to get involved in this roundtable discussion by sharing your “fails” and lessons learned.

Speaker: Andy Quick, Vice President, Business Data & Insight (Entergy)

Andy Quick has been with Entergy for 26 years and is currently Vice President, Business Data & Insights, an enterprise function focused on leveraging advanced analytics to help Entergy be the premier utility. Andy has had held numerous leadership positions in innovation, finance, shared services, and IT. Prior to his current role, Andy was a product owner at KeyString Labs, Entergy’s innovation incubator for new products and services where he led the development of a new digital platform business. Andy has led many departments including robotic process automation, business transformation, enterprise architecture, telecommunications, data center operations, outsourcing relationship management, and two business unit CIO organizations.

Prior to joining Entergy, Andy worked for Andersen Consulting (Accenture) where he was an IT consultant for global, multi-industry companies.
Andy has served as an adjunct instructor at Tulane University and University of New Orleans where he taught robotic process automation courses. Andy also volunteers as an executive mentor to business school students at Loyola University.

Andy holds a B.S. in computer science from Louisiana State University and an MBA from Tulane University. Andy is a certified Automation Anywhere RPA trainer and the winner of the inaugural Automation Anywhere Bot Games competition in 2018.

Outside of work, Andy enjoys spending time with his wife and four daughters. 

Speaker: Yong Li, Manager of Performance Center (DTE Energy)

Yong Li is currently a data science manager at DTE. He has over 30 years of professional experience in academic research and business intelligence.  His data science team at DTE Electric has analyzed AMI and other business data (outage restoration, work management, engineering, vegetation management, financials, and weather) to support operational efficiency, system reliability strategy and customer satisfaction. 
_____________________________________________________________________________________________________________________________________________

A calendar placeholder for this community conversation was sent out via my alternate email address (kpraet@sg.utilityanalytics.com). I’ve noticed these invites can sometimes be flagged as spam, so please check your junk folders if you’re not seeing right away. For your convenience I have also attached the placeholder to this message as an easy download. If you are not seeing a placeholder on your calendar for Thursday, July 28 at 1:00 PM CT, or are having trouble downloading the placeholder from this message, please reach back out to me so I can be sure you are forwarded the invite an alternate way.

Thanks and I look forward to connecting next week!

——————————
Kevin Praet
Membership Coordinator
Utility Analytics Institute (UAI)
Boulder CO
315-440-3033
——————————

[Rob Malowney]

Good morning Sarah,

SDG&E has been working on this concept for a couple years. At first, we utilized SAP Hana for not only the data integration/lake aspect but also to run our predictive models. We have since shifted focus to the cloud and have replicated the data to AWS and migrated the models there as well. Main data sources are GIS, nameplate attributes, weather/topology, inspection history, and outages. Would be happy to touch base with you on this topic. 

Thanks!

——————————
Rob Malowney
Asset Data Systems & Records Manager
SDG&E
san diego CA
6196196196
——————————
——————————————-
Original Message:
Sent: 07-20-2022 09:56
From: Sarah Green
Subject: Pole Replacement Analytics

We are currently exploring the idea of moving to a more data driven pole replacement strategy and plan to address electric reliability and resiliency. As we start this exploration we were curious if there are any other utilities that currently utilize an analytic model to inform which of their poles they replace and when.

We would be interested in having further conversations with any one that has this strategy already in place to understand the type of data that is utilized for your model, how often it is ran, where it is ran (on-premise or cloud technology), and how long it took to develop.

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
248-974-0126
——————————

[Sarah Green]

Hi Mike!

Thanks for sharing! Would you or a team member be able to have a further discussion with our internal teams to learn more about the data set and the model that is being created?

Thanks!

Sarah

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
5175391954
——————————
——————————————-
Original Message:
Sent: 07-20-2022 11:27
From: Michael Fourman
Subject: Pole Replacement Analytics

Hi Sarah.  Georgia Transmission funds EPRI’s Asset Management Analytics program (Program 34) and we will be using their model for projecting wood pole reject rates.  Their models are based on an industry-wide data set.  I hope this is helpful.

——————————
Michael Fourman
Georgia Transmission
——————————

Original Message:
Sent: 07-20-2022 09:56
From: Sarah Green
Subject: Pole Replacement Analytics

We are currently exploring the idea of moving to a more data driven pole replacement strategy and plan to address electric reliability and resiliency. As we start this exploration we were curious if there are any other utilities that currently utilize an analytic model to inform which of their poles they replace and when.

We would be interested in having further conversations with any one that has this strategy already in place to understand the type of data that is utilized for your model, how often it is ran, where it is ran (on-premise or cloud technology), and how long it took to develop.

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
248-974-0126
——————————

[Michael Fourman]

Sarah,

Yes, I would be happy to meet with you and your team.  Additionally, I can invite EPRI to join us to share with you the work they are doing.  Do you know if your utility is a member of EPRI?  Feel free to reach out directly to coordinate, michael.fourman@gatrans.com.

Mike

——————————
Michael Fourman
Georgia Transmission
——————————
——————————————-
Original Message:
Sent: 07-26-2022 11:32
From: Sarah Green
Subject: Pole Replacement Analytics

Hi Mike!

Thanks for sharing! Would you or a team member be able to have a further discussion with our internal teams to learn more about the data set and the model that is being created?

Thanks!

Sarah

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
5175391954
——————————

Original Message:
Sent: 07-20-2022 11:27
From: Michael Fourman
Subject: Pole Replacement Analytics

Hi Sarah.  Georgia Transmission funds EPRI’s Asset Management Analytics program (Program 34) and we will be using their model for projecting wood pole reject rates.  Their models are based on an industry-wide data set.  I hope this is helpful.

——————————
Michael Fourman
Georgia Transmission

Original Message:
Sent: 07-20-2022 09:56
From: Sarah Green
Subject: Pole Replacement Analytics

We are currently exploring the idea of moving to a more data driven pole replacement strategy and plan to address electric reliability and resiliency. As we start this exploration we were curious if there are any other utilities that currently utilize an analytic model to inform which of their poles they replace and when.

We would be interested in having further conversations with any one that has this strategy already in place to understand the type of data that is utilized for your model, how often it is ran, where it is ran (on-premise or cloud technology), and how long it took to develop.

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
248-974-0126
——————————

[Ben Ettlinger]

Is your utility a member of CEATI? Currently there is a CEATI program as described here. The results of this study may help you in your development of a strategic plan.
“The objective of this project is to prepare a state of the art report for evaluating in-service wood pole strength data based on non-destructive tests (NDE). The study should evaluate all NDE tools available in the market place and their effectiveness, in the use of in-service pole conditions particularly, validating field assessments and measurements by actual pole bending test data that are available in open literature or manufacturer’s own database. In addition, the study should address the assessment of wood pole life based on non-destructive test data and what factors needs to be considered for the assessment and a clear methodology to achieve this specific objective. A review of the current use of in-service fumigants and treatment types  and their effectiveness should be addressed. An example problem will be included for a 230kV AC line to demonstrate the methodology in condition assessment of in-service poles. “

——————————
Ben Ettlinger
Data Analytics Architect
New York Power Authority
White Plains, NY
914 681 6496
——————————
——————————————-
Original Message:
Sent: 07-20-2022 09:56
From: Sarah Green
Subject: Pole Replacement Analytics

We are currently exploring the idea of moving to a more data driven pole replacement strategy and plan to address electric reliability and resiliency. As we start this exploration we were curious if there are any other utilities that currently utilize an analytic model to inform which of their poles they replace and when.

We would be interested in having further conversations with any one that has this strategy already in place to understand the type of data that is utilized for your model, how often it is ran, where it is ran (on-premise or cloud technology), and how long it took to develop.

——————————
Sarah Green
Data & Analytic Services Manager
Consumers Energy
Jackson MI
248-974-0126
——————————