Microsoft Energy Brazil

Furnas

Data Lake / Data Warehouse Modernization · IT Operations Automation · Predictive Maintenance

“An accelerator was needed, an architecture that gave us the possibility to accelerate deliveries.” Managing a large volume of data for a complex and critical system Predictive maintenance of transmission assets is already part of Furnas’ daily life, with routine maintenance and checks, and the knowledge of technicians, with technology being an ally to the procedure’s improvement.

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Furnas is a mixed economy power generation and transmission company, a subsidiary of Eletrobras (Centrals Elétricas Brasileiras S.A.) and has become a reference in the sector in terms of suitability for the Brazilian regulatory landscape, focused on operational efficiency, process performance, and sustainability of investments, in addition to transparency in its management.

Having started in 2020, the deployment of the solution is expected to end in 2023.

In 2020, Furnas started this project and, with an open notice, carried out a partner selection process, in which Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) joined as an academic arm, responsible for defining and building AI models, while Radix, a Microsoft partner company, joined as project executor, responsible for architecture, data integration, and other processes.

Owned or in partnership with the private sector, they also manage 34,995.13 km of transmission lines and 72 substations in the states of São Paulo, Minas Gerais, Rio de Janeiro, Espírito Santo, Paraná, Goiás, Mato Grosso, Mato Grosso do Sul, Pará, Tocantins, Rondônia, Rio Grande do Sul, Santa Catarina, Ceará, and Bahia, and in the Federal District.

Reported impact

Business outcome

Solution

Traditional electrical industry company, Furnas, underwent a digital transformation process where AI became a key part of improving predictive equipment maintenance, anticipating potential failures that could lead to regulatory agency penalties. “An accelerator was needed, an architecture that gave us the possibility to accelerate deliveries.” Managing a large volume of data for a complex and critical system Predictive maintenance of transmission assets is already part of Furnas’ daily life, with routine maintenance and checks, and the knowledge of technicians, with technology being an ally to the procedure’s improvement. The solution architecture brings together Azure Data Lake , a storage service that offers identity, management, security, and easy integration with data repositories like Azure Data Factory , a fully managed data integration service, Azure Synapse Analytics , which performs big data processing for insights generation, Azure AI , an AI service suite, and Video Analyzer

Solution architecture

8 components · 5 layers

Ingest
- Azure Data Factory Ingests and orchestrates data movement into the analytics platform.
Compute
- Azure AI Provides AI services used by the solution.
- Azure Synapse Analytics Provides large-scale analytics and data processing for the solution.
- Azure Provides the secure, scalable cloud foundation for the solution.
- Azure Data Lake Analytics Processes analytical data workloads for reporting or machine learning.
Storage
- Azure Data Lake Storage Stores source and curated data used by analytics and AI workloads.
Serving
- Power BI Delivers dashboards and visualization for business users and decision makers.
Uncategorized
- Microsoft 365 Business Standard Supports the core application workflow described in the customer story.

Architecture clues

Finally, Power BI also integrates the solution.
In 2020, Furnas started this project and, with an open notice, carried out a partner selection process, in which Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) joined as an academic arm, responsible for defining and building AI models, while Radix, a Microsoft partner company, joined as project executor, responsible for architecture, data integration, and other processes.
The solution architecture brings together Azure Data Lake , a storage service that offers identity, management, security, and easy integration with data repositories like Azure Data Factory , a fully managed data integration service, Azure Synapse Analytics , which performs big data processing for insights generation, Azure AI , an AI service suite, and Video Analyzer, a platform with the ability to capture, record, and analyze live video and publish the results.
“Electrical companies still lack simpler evolutions, such as data integration and standardization,” explains de Carvalho.
“In the project, we are delivering a data lake that will be used in other solutions as well, a gain that is already extremely important.” New tools fostering a new culture Rodrigues also states that the project, in addition to generating product, has a very strong internal capacity, bringing all the knowledge inside the company – which has a path of cultural change ahead.
“The goal of the work is to be able to identify failure situations from equipment operational standards and operational data such as orders, notes, and records.” Thus, already used to the Microsoft Cloud that would allow the insertion of that solution into the Furnas architecture, a key concern of the project was in relation to the data from several core systems of the company, which were collected and concentrated in a data lake, where they could be worked with.

Evidence from the source

Finally, Power BI also integrates the solution.

Furnas is a mixed economy power generation and transmission company, a subsidiary of Eletrobras (Centrals Elétricas Brasileiras S.A.) and has become a reference in the sector in terms of suitability for the Brazilian regulatory landscape, focused on operational efficiency, process performance, and sustainability of investments, in addition to transparency in its management.

The solution architecture brings together Azure Data Lake , a storage service that offers identity, management, security, and easy integration with data repositories like Azure Data Factory , a fully managed data integration service, Azure Synapse Analytics , which performs big data processing for insights generation, Azure AI , an AI service suite, and Video Analyzer, a platform with the ability to capture, record, and analyze live video and publish the results.

“An accelerator was needed, an architecture that gave us the possibility to accelerate deliveries.” Managing a large volume of data for a complex and critical system Predictive maintenance of transmission assets is already part of Furnas’ daily life, with routine maintenance and checks, and the knowledge of technicians, with technology being an ally to the procedure’s improvement.

Transparency Evidence & methodology 61/100 reference value · 85% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value61/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth15/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity16/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact0/25
0 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence85%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
4 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity6/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Microsoft
Furnas customer story

First-party
https://www.microsoft.com/en/customers/story/1784062354779116638-furnas-azure-energy-en-brazil

Source type
Vendor-published customer story

Published
Jun 25, 2024
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Published on source page Jun 25, 2024 · Microsoft · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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