Data Con LA 2020 Description Often models are built for proof of concept or exploratory purposes. Why do models get stuck in the lab? What are the ingredients necessary to create demand for your model? The presentation will focus on the following: *Building executive support for your models *Identifying and focusing on use cases that impact the business *Discovering why change management is so important *Discussing the importance of communication - from the data scientist *Sharing the light - sharing the podium in the name of progress *Impacting the business vs. impacting the plumbing Speaker Jeff Moore, SoCal Edison, Data Scientist

2. 1
AGENDA
Agenda Item Topics Presenter(s) Time Allotment
Welcome • Introduction • Jeff Moore • 5 mins
Top ML
Challenges
• Data
• Culture
• Problem Statement / ROI
• Tech
• Change Management
• 15 mins
Mitigations • Marketing
• Agile
• OCM
• Strategic Roadmap
• 5 mins
Use Case • Problem Statement
• Plan of Attack
• Results
• 5 mins
QA • Summary • 5 mins

3. 2
SCE OVERVIEW

4. 3
− Data Scientist – AI/ML Lead within Data
and Process Transformation
− Former Management Consultant
− Former IT executive
− Graduate School – Northwestern, NYU
Stern
− SCE email: jeff.moore@sce.com
− Linkedin:
linkedin.com/in/moorebigdata
INTRODUCTION

5. 4
− Sources
• https://medium.com/datadriveninvestor/data-science-challenges-b7622b85b807
• https://www.statista.com/statistics/1111249/machine-learning-challenges/
TOP ML CHALLENGES (2018 VS. 2020)

6. 5
− Lack of clear problem statement
− Integrating the model into critical processes
− Results used by decision makers
− Meeting expectations and aligning the
organization
− Talk with stakeholders
− Perform basic EDA, data visualizations
− Formulate alternative problem statements
− Embed the model into legacy applications
− Make the model callable via API
− Create meaningful metrics
− Educate management on metric importance
− Integrate metric into current reporting
− Leverage change management
− Link model to corporate goals or strategies
− Educate, Communicate
POTENTIAL MITIGATIONS

7. 6
− Problem Statement
• When will the next injury event occur?
− Data
• We have all the data in the world
− Advanced analytics is cool! Everyone will use it
just because!
− Everyone wants a new iphone!
− Reframe Problem Statement
• Can a risk framework be built that identifies safety patterns
where risk is elevated?
− Data – Actually we don’t!
• EDA is your friend. Use it to show where there is plenty of data,
but also where the gaps are.
− Find suitable use cases for ML/AI
• Educate, Market, Communicate
• Be open to feedback from SMEs
• Build model credibility
− New iphone? No thanks!
• Create value by integrating the model into existing workflow
• Create new digital solutions to gain more, more frequent and
accurate data
USE CASE
Context: Company that is new to predictive analytics. New data science community of practice.
Numerous legacy systems and databases. Several, independent data science teams.

8. 7
SEEING ALIGNMENT IN PRACTICE…
Assess crew and
work risk – day of
work
Assess safety
risk prior to
executing work
• Integrating model in other areas of
interest:
 Public safety
 Data capture initiatives
 Metric reporting
 Work method training

9. 8
− Moving beyond the lab is more than just a model
− Data is important but don’t forget to communicate, market and build
alignment
− A good model will have legs beyond the initial use case
− Starting with a good problem statement can increase the likelihood
of moving to production
− Look to link your model to a strategic problem or corporate goal
IN SUMMARY…

10. THANKS
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