ENGIE GEM - Building a Path For All Users to Easily and Securely Gain Insights From Their Data

s-raguideau

Name:
Stéphane Raguideau

Title:
Digital & Data Accelerator

Country:
France

Organization:
ENGIE - Global Energy Management

Description:
Global Energy Management (GEM) is one of ENGIE’s Business Units. At the heart of the energy value chain, we optimize the Group’s assets portfolio including electricity, renewable technologies, natural gas, environmental products and bulk commodities such as biomass. We also develop our own external commercial franchise worldwide and rely on four main expertise to offer tailor-made, innovative and competitive solutions. We provide services in energy supply & global commodities, energy transition services, risk management & market access, and asset management. With a staff of 1,400, offices in 15 countries including 8 main spots, GEM has an extended geographical coverage in Europe, the US and Asia-Pacific.

Awards Categories:

• AI Democratization & Inclusivity

Challenge:

Data science is at the core of our activities at ENGIE - Global Energy Management. Users across departments manage various sources of data, including:

• Energy consumption,
• Market data,
• Weather information,
• Deal and order book,
• Etc.

This data is leveraged by the business for many purposes, including:

• Pricing,
• Risk management,
• Data reconciliation from various sources,
• Reporting,
• Etc.

But access to the data was limited due to its sheer volume, security considerations, and tooling segmentation. In addition, coding skills were required for accessing it, which excluded many users who did not have a technical background.

Users needed to manually retrieve the data through a variety of applications, which caused several issues:

• Task repetitiveness, which was very time-consuming - and namely included extracting data from the different systems in place.
• Data availability, as all data sources were not always referenced and only IT may have been able access these.
• Operational risk, which is related to the quality of the data and the manual processing taking place (e.g. mistakes in copy/pasting steps),
• Coding skills required to manipulate the data and automatize part of the process, e.g. “Visual Basic for Applications” (VBA) in Excel, or Python.
• Tooling was not fit for the volume of data (in particular, Excel).

Solution:

Dataiku enabled us to solve some of these pain points:

• Data is now easily accessible through a number of plugins created internally, which enable users to easily and securely interact with the different data sources,
• Low code/no code data manipulation: visual recipes enable users to prepare and transform the data to fit their needs, without any coding skills required.
• For more complex operations, the collaborative visual interface enables our IT teams to work hand-in-hand with the business on building and editing workflows.
• Sharing insights from the data is made easy with the dashboarding features.
• Process automation, leading to:
• Shortened time-to-market, now that reporting and analysis are available on-demand.
• Increased monitoring capabilities, as monthly and weekly analysis can easily be turned into daily reports.
• Reduced operational risk, as manual operations are now automatized.

Impact:

As with every new tool, Dataiku requires specific onboarding to maximize its benefits. At ENGIE Global Energy Management, our users have different profiles and backgrounds, hence they are not all familiar with data manipulation and analysis.

It is therefore important to provide them with training opportunities, regardless of their division (trading, risk, back office, finance, IT, etc.). This includes:

• Understanding their needs and identifying a use case to conduct a Proof of Concept (POC).
• Developing the most relevant training in regard to their profile and skills.
• Building the Dataiku plugins and connectors to allow them to easily and securely access the data.
• Hosting regular workshops (at least once per week) on select topics throughout the POC, including partitioning, Python recipes, machine learning, automation, dashboarding, pattern recognition, etc.)

This training path is set to two months, after which users are given autonomy to access the data, manipulate it for their day-to-day needs, and most importantly, are able to explore new areas to gain more insights from their data. This has been a key pillar of data democratization within ENGIE - Global Energy Management.

Becoming a data scientist or an engineer doesn’t happen overnight though, hence we’ve developed a framework to monitor the projects created in Dataiku and ensure they’re following established governance and best practices, including data connections, scenarios, data sharing, partitioning, plugins types, etc.) All users are therefore able to produce insights safely!