Leveraging Data Science in the Electric Power Industry

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.

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.

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.