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Upcoming September (2021) Community Conversation Schedule
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Upcoming September (2021) Community Conversation Schedule
Hello UAI Members,
I hope all is well! This month we have an action-packed Community Conversation schedule planned and cannot wait to connect with you across our eight communities. To learn more about the Community Conversations taking place in September (2021), please visit our Events Calendar where we’ve included session descriptions and more info on each of our speakers.
For your convenience we’ve also highlighted below:
- September 14 (2021) – Customer Analytics Community Conversation – “Manitoba Hydro Credit and Collections Department’s Analytics Journey”
- Speakers:
- Lindusia Chmieluk, System Developer, Business Data & Analytics Platform (Manitoba Hydro)
- Katia Bernstein, Staff Officer (Manitoba Hydro)
- Speakers:
- September 15 (2021) – Natural Gas Analytics Community Conversation – “Comparing Quantitative Risk Modeling Approaches for Transmission vs. Distribution Pipelines”
- Speaker:
- Miaad Safari,Supervisor Integrity Assessments (Enbridge Gas Inc.)
- Speaker:
- September 15 (2021) – Grid Analytics Community Conversation – “Storm Outage Modeling: Applying Analytics to Inform Electrical Utilities”
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Speaker:
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Steven Quiring, Professor, Atmospheric Sciences Program, Department of Geography (Ohio State University)
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- September 16 (2021) – AAT Community Conversation – “Consumers Energy’s Analytics Architecture Maturity Journey”
- Speakers:
- Ashish Garg, Chief Enterprise Architect (Consumers Energy)
- Gautham Pingali, Enterprise Data Architect (Consumers Energy)
- BJ Walraven, Enterprise Architect (Consumers Energy)
- Speakers:
- September 21 (2021) – Data Science Community Conversation – “Data Science Tools & ML Operations: Azure Data Bricks vs. AWS”
- Speakers:
- Chad Tucker, Data Engineer (The Energy Authority)
- Jason Pegg, Domain Architect, Enterprise Technology (Avista Corp)
- Max Wick, Data Scientist and Juan Vazquez (PPL Utilities)
- Speakers:
- September 22 (2021) – Asset Health Analytics Community Conversation – “Part 2: Asset Health Analytics Use Case Identification & Prioritization”
- More information on our speaker coming soon!
- September 23 (2021) – Enterprise Analytics Community Conversation – “Power BI Rollouts and Successes”
- Speakers:
- Shawn Timbers (Rochester Public Utilities)
- Idan Enright (SoCalGas)
- Speakers:
- September 28 (2021) – Safety Analytics Community Conversation – “Gas Dig-in Analytics Use Case: Can you find a broken needle in an Everest-sized haystack?”
- Speakers:
- Thomas Kautzman, BI Analyst, Data Science (Avista Corp)
- Kim Boynton, Senior Analyst, Data Science (Avista Corp)
- Robert Hughes, Damage Prevention Analyst (Avista Corp)
- Speakers:
If you are not part of the UAI Communities, you can learn more about them on our Current Analytics Communities page on UAI Connect. All UAI Utility Members receive unlimited access to UAI Communities and the monthly Community Conversations. If you would like to join one or all of the UAI Communities, please submit a “Request to Join“.Please let me know if you have any questions. We look forward to seeing you online this month!——————————
Kevin Praet
Membership Coordinator
Utility Analytics Institute (UAI)
Boulder CO
315-440-3033
—————————— - September 14 (2021) – Customer Analytics Community Conversation – “Manitoba Hydro Credit and Collections Department’s Analytics Journey”
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2 Replies
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Thanks for this article. But it still leaves me trying to understand what that semantic layer is all about. Is it a set of indices pointing to data attributes defined with meta data? When the rubber hits the road what is being requested on the front end of the semantic layer? data fields?, meta data? What connects the data requestor to the semantic layer, a data fabric application? What’s the difference in having a semantic layer or technologies used by products like Databricks, Snowflake or Redshift?
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Ben Ettlinger
Emerging Technologies
Enterprise Architecture & Engineering Group
New York Power Authority
White Plains, NY
914 681 6496
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Original Message:
Sent: 09-01-2021 15:01
From: Erin Hardick
Subject: Bi-Weekly Feature — What is a Data Fabric and How Does it Complement my Data Warehouse?Hello members,
This week’s feature shows how data fabric is critical to connecting a traditional data warehouse (DWH) to advanced analytics at scale. “What is a Data Fabric and How Does it Complement my Data Warehouse?”, written by @Gabe, defines data fabric and the two main pillars of data fabric: context and discovery.
“Data context is the sum of meaningful use, case supportive relationships within and across different data types and data artifacts. It is the result of data relationship mining and curation in a so-called contextualization pipeline.
Data discovery is about making data effortlessly available to the right user in the right format. This always has been the goal of data and information architects. Discovery in B2C technology is instantaneous, autonomous and continuously self-learning.”Read the full article and let us know what you think! What is a Data Fabric and How Does it Complement my Data Warehouse? – Utility Analytics Institute
Utility Analytics Institute 
What is a Data Fabric and How Does it Complement my Data Warehouse? – Utility Analytics Institute Connecting DWH to advanced analytics at scale requires a data fabric, not just data availability. For industrial companies, the path to ultimate value from data liberation requires three crucial steps. Many organizations have already achieved step one: liberating data from siloed source systems and aggregating it in a traditional data warehouse (DWH). View this on Utility Analytics Institute >
Happy reading!——————————
Erin Hardick
Senior Research Analyst
Utility Analytics Institute (UAI)
CO
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I’ll answer my own question based on investigation into some Gartner research, after the previous post. The semantic layer can be a knowledge graph. “The main advantage of using a knowledge graph for this type data integration scenario is that it not only solves the actual data integration task, but it’s also bridging the gap between disparate corporate datasets and the actual business requirements. It allows business users to model, explore and discover how datasets are connected using a human-readable conceptual knowledge graph model where there is little to no difference between the conceptual and the physical, machine-readable data models.” (Demystifying the Data Fabric- Gartner). At first glance, it would appear to be a huge effort to set up a knowledge graph, requiring both IT and Business subject matter experts. And even before reaching that phase a dedicated effort in data cleaning, data quality, and data governance would be required. Otherwise the data fabric would have a lot of warped weft.
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Ben Ettlinger
Emerging Technologies
Enterprise Architecture & Engineering Group
New York Power Authority
White Plains, NY
914 681 6496
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Original Message:
Sent: 09-03-2021 08:51
From: Ben Ettlinger
Subject: Bi-Weekly Feature — What is a Data Fabric and How Does it Complement my Data Warehouse?Thanks for this article. But it still leaves me trying to understand what that semantic layer is all about. Is it a set of indices pointing to data attributes defined with meta data? When the rubber hits the road what is being requested on the front end of the semantic layer? data fields?, meta data? What connects the data requestor to the semantic layer, a data fabric application? What’s the difference in having a semantic layer or technologies used by products like Databricks, Snowflake or Redshift?
——————————
Ben Ettlinger
Emerging Technologies
Enterprise Architecture & Engineering Group
New York Power Authority
White Plains, NY
914 681 6496
——————————Original Message:
Sent: 09-01-2021 15:01
From: Erin Hardick
Subject: Bi-Weekly Feature — What is a Data Fabric and How Does it Complement my Data Warehouse?Hello members,
This week’s feature shows how data fabric is critical to connecting a traditional data warehouse (DWH) to advanced analytics at scale. “What is a Data Fabric and How Does it Complement my Data Warehouse?”, written by @Gabe, defines data fabric and the two main pillars of data fabric: context and discovery.
“Data context is the sum of meaningful use, case supportive relationships within and across different data types and data artifacts. It is the result of data relationship mining and curation in a so-called contextualization pipeline.
Data discovery is about making data effortlessly available to the right user in the right format. This always has been the goal of data and information architects. Discovery in B2C technology is instantaneous, autonomous and continuously self-learning.”Read the full article and let us know what you think! What is a Data Fabric and How Does it Complement my Data Warehouse? – Utility Analytics Institute
Utility Analytics Institute What is a Data Fabric and How Does it Complement my Data Warehouse? – Utility Analytics Institute Connecting DWH to advanced analytics at scale requires a data fabric, not just data availability. For industrial companies, the path to ultimate value from data liberation requires three crucial steps. Many organizations have already achieved step one: liberating data from siloed source systems and aggregating it in a traditional data warehouse (DWH). View this on Utility Analytics Institute >
Happy reading!——————————
Erin Hardick
Senior Research Analyst
Utility Analytics Institute (UAI)
CO
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