Overview
The electric power industry is experiencing a data-driven transformation. Rapid growth in the deployment of sensors, distributed assets, and “intelligent systems” means data is easier to access than ever before. To reap the benefits, utilities will need to master new analytics competencies — including managing and combining multiple sources and types of data, building analytic models, and interpreting findings to make better decisions.
To make it easier for you to start developing new capabilities, EPRI has developed 10 briefs on the theme Leveraging Data Science for Electric Power Industry Transformation. Each paper offers tips and tactical advice to accelerate your progress in data science and machine learning.
The briefs are organized into two series. Series 1 (4 briefs) describes major milestones in the journey toward data science mastery. Series 2 (6 briefs) addresses the Data Science Lifecycle.
Series 1: Key Milestones in the Data Science Journey
Blueprint for Data Science Success
What key enablers does your company need to use data smartly?
Learn how to successfully engage stakeholders
to guide your data science strategy. Brief
includes key steps in the process, a sample
interview guide, and a presentation template
you can customize and share internally.
Increasing Data Science Adoption
How can you accelerate data science practices and knowledge-sharing across your organization?
Drive your data science journey with practical
use case scenarios based on your company’s
real challenges. The right use cases help test the
applicability of concepts and how to tackle them.
Optimizing Discovery Interviews
What actions can help guide your strategy to plan a successful data science initiative?
Leaders in data science have learned that
internal knowledge sharing is critical to success.
Learn tips on how to implement a digital hub
for collaboration to build your capabilities and
increase adoption of data science best practices.
Selecting the Right Use Cases
How can you ensure your develop data science solutions that can help solve real challenges in your organization?
Improve your progress in machine learning
and AI by understanding the technology,
tools, and business processes you need to
succeed. This Blueprint illustrates functional
requirements and how to address them.
Series 2: The Data Science Lifecycle
Series 2 includes six briefs that highlight real business challenges and solutions at different stages in the Data Science Lifecycle. These insights are designed to provide inspiration and ideas for solving similar challenges and address situations that are stage-specific.
Acquire - Turning Legacy Data into Assets
In this brief, the challenge is transforming legacy data into useful assets. You’ll learn how to gain incremental value from legacy data by choosing a technology solution to extract, transform, and load data into a format that can be reused.
Metadata - Enabling Data Sharing
Collecting metadata is an important subprocess of the Acquire stage, and ideally should occur at the same time you acquire datasets. The challenge here is how to best document details about your data sources to enable sharing data and ensure proper interpretation of results.
Store - Optimizing Data collection
The quantity of data, variety of sources and types of data, and ability to make data available to a wider group of users is a challenge to manage. In this brief, we explore how you may need to revise processes and tools.
Visualize - Gaining Insight Faster
In this brief, we cover the challenge of accelerating time-to-insight using visualization techniques. Data visualization can be applied to every stage of the Data Science Lifecycle to accomplish tasks more quickly and easily.
Analyze - Gaining Deeper Insights
Most companies build systems to understand “what happened?” and “why did it happen?” This brief explores the challenge in machine learning and AI to answer a new set of questions like “what will happen next?” and “what actions should I take when it does happen?”
Case Study - Cleanse Visualize, Analyze
This brief is a case study about the practical reality when an analytics issue doesn’t fit into a single lifecycle stage. The challenge is to create a repeatable process for cleansing data that can be applied to multiple datasets, use data visualization to rapidly identify insights, and create preliminary advanced analytic models.