Five Facts About Capgemini’s Big Data for Utilities Practice (Part 2)


Ethan L. Cohen

November 16, 2016

In my prior blog post, I shared two facts about our big data for utilities practice that address questions we routinely get in meetings with current and potential clients. I discussed how our practice has evolved over the course of 50 years, and how we continue to adapt to best serve clients based on past engagements and our research in the space.

In this post, I’ll go into more detail about how we’re working with utilities and share what Capgemini looks for when hiring data scientists.

3. Solutions Are Designed to Fit the Diverse Needs of Clients

We are seeing a prolonged uptick in demand as utilities become more familiar with the benefits of data science and analytics, and as regulators approve and even incent investments in data science, analytics and big data capabilities. We help utilities leverage data to unlock the next level of performance by:

  • Understanding and foreseeing consumption patterns of electricity, gas and water
  • Predicting the likelihood of failure for essential equipment
  • Improving how the utility network operates, taking into account new supply technologies such as wind, solar and energy storage

We have many reusable utility industry data science and analytics models including solutions for:

  • Advanced Planning & Scheduling:  Place the right resources, equipment, and material in the right quantity, at the right time
  • Business Process Analytics: Better insights for decision making and better performance of business processes without having to recruit and train personnel, or continually invest in new technology
  • CFO Analytics: Aggregate large amounts of financial data and generate practical insight through advanced analysis tools
  • Customer Insight: Understand, segment and manage customers in a more effective way, and build the right customer engagement programs
  • Internet of Things Analytics: Harness the data and insights from connected devices in order to generate value
  • Operational Analytics: Optimize supply chains and the management of assets
  • Risk, Regulation & Compliance: Ensure the organization is effectively protected
  • Social Media Analytics: Influence and engage your customers in real time while generating more revenue
  • Analytics as-a-service: Deliver the insights based on your own data, focusing exclusively on business value and opportunities

4. Track Record of Unlocking Business Value

Capgemini is working with electric power clients on some of the most challenging, high value areas of their business. For example, we are currently developing data science and analytics capabilities with a utility on the West Coast of the U.S. focused on electricity theft and fraud deterrence. Capgemini is also developing data science and analytics capabilities for the same customer to optimize segment meter-to-cash processes, which will lower the cost of customer service operations.

In another example, Capgemini is using data science and analytics working with a utility company in the northeast U.S. for grid optimization, transmission and distribution analytics, and asset wear and tear condition-based maintenance. Finally, with a European utility company, Capgemini is using data science and analytics to aid in wind farm design and to model wind flow and power output in a neural network. With these and dozens of other high impact customer engagements, Capgemini’s is leading the industry into new frontiers of how data and data science can be leveraged as the conduit for unlocking new business value.

5. Talent is a Priority

When hiring team members for our data science practice, we look for experience and expertise in:

  • Domain knowledge including industry expertise, and familiarity with data sets and operational understanding which underpins the ability to maximize the value from data within a specific business context.
  • Ability to imagine outcomes, including identifying business problems and creating an analytic strategy and approach to solving these problems.
  • An ability to generate genuine insights and apply data science in a rigorous way to test and evaluate hypothesis, not simply run software tools.
  • Realize value including implementing analytics in the enterprise and helping clients to capture value from insights.

Visit this link to learn more about our big data for utilities practice:

This article was written by Ethan L. Cohen from Capgemini: Capping IT Off and was legally licensed through the NewsCred publisher network.

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