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TROVE Data Science is now the data science division of E Source.

Unleash your data’s business potential

Utilities across the US and Canada face a common set of challenges in harnessing the power of data and machine learning to optimize their operations. We know:

  • It’s hard to attract and retain top data science talent that possess the diversity of skill sets needed.
  • Data is costly and difficult to organize, manage, and use. Data that’s sufficient for one purpose often proves difficult and costly to repurpose.
  • It’s challenging to gain organizational alignment to drive business value through big, and often unproven, ideas.

Data science as a service

Our data-science-as-a-service (DSaaS) approach is a scalable solution to support your data and artificial intelligence (AI) efforts. It integrates seamlessly with your existing teams, no matter where you are on your data science journey.

Data

Our industry veterans can help your team strategically organize and manage data. We enrich your current data with third-party and proprietary E Source data sets.

Science squad

No data science solution is a one-size-fits-all offering. Our team of data scientists with strong utility domain expertise can help augment your team’s capabilities in tailoring and customizing your data science and AI tools to their greatest potential.

Solvers

We’ve spent years building AI and machine learning models and algorithms tailored specifically to solve utility-centric problems. Our portfolio of more than 40 solutions solves a wide yet specific set of problems in the utility industry—from algorithms that lower transmission and distribution costs while improving reliability to those that improve customer satisfaction and participation in a wide set of customer programs.

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Agile data science

Gone are the days of multiyear efforts that yield little or no progress. Our team has embraced the Agile approach of delivering software that responds quickly to the dynamically changing business environment.

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Accelerate the value of your data

Our agile data science approach helps utilities identify data and machine learning models that will quickly add value to their businesses. Our four-step, collaborative learning methodology helps clients identify the right opportunities for prediction, put their data to work, and get meaningful results fast—all in an IT-light solution that’s focused on delivering tangible outcomes with measurable returns.

A circle graphic with each step leading to the next, then the last back to the first again, describing the agile data science model: 1. Develop the concept. 2. Workshop the concept. 3. Prove the concept. 4. Operationalize the concept.

The four steps of agile data science include applying this methodology to the concept:

1

Develop

Determine which high-value opportunities to tackle with predictive data science.

2

Workshop

Dig into the data to make sure the data assets and access are clearly mapped out. Then work with subject-matter experts and internal stakeholders to examine the nuances of the opportunity and calculate how success will be measured.

3

Prove

Develop the solution for a logical subset of your business using your data and our AI models. Ensure the results are actionable with validated learnings and a clear return on investment.

4

Operationalize

Deploy the solution at enterprise scale, address priority gaps, automate data feeds, and partner on the plan to fully implement the solution for your business.

We’re on your team

Let’s schedule a time to discuss your challenges and how E Source can help.

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