Microsoft Real Estate and Construction Austria

Strabag SE

Risk Management

This enabled the organization to develop use cases to prove the value of data including its risk management project.

With approximately 75,000 employees, its projects range from civil engineering to transportation infrastructure.

Reported impact

Business outcome

Strabag SE, the global construction company, partnered with Microsoft to build a Data Science Hub to collect decentralized data and leverage it for insights.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Strabag SE built its Microsoft solution to improve risk-monitoring and decision workflows. This enabled the organization to develop use cases to prove the value of data including its risk management project. Key components included Azure OpenAI Service ran model inference or heavy processing, and Azure Machine Learning ran model inference or heavy processing. Operational data flows into Azure Synapse Analytics, where Azure Machine Learning applies transformations and business logic before Azure Machine Learning delivers governed insights to teams across Strabag SE. The governed Microsoft stack gave Strabag SE a reusable foundation for generative ai / llm, ai and machine learning while keeping deployment, security, and day-to-day operations inside the Microsoft estate. Strabag SE, the global construction company, partnered with Microsoft to build a Data Science Hub to collect decentralized data and leverage it for insights.

Data flow

Operational data flows into Azure Synapse Analytics, where Azure Machine Learning applies transformations and business logic before Azure Machine Learning delivers governed insights to teams across Strabag SE.

Solution architecture

4 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language models that generate grounded responses, summaries, and copilots.
    • Azure Machine Learning Builds, trains, and operationalizes predictive models and machine-learning pipelines.
    • Azure Databricks Transforms large-scale datasets and runs analytics or model-processing jobs.
  2. Storage
    • Azure Synapse Analytics Combines large-scale data integration and analytics for enterprise reporting workloads.

Architecture clues

  • Constructing a future built on data "In time," says Bornschlegl, "STRABAG Innovation and Digitalisation hopes that employees see that in the same way you need different materials to work together to build a sturdy house, when you collect and connect information, you can see the bigger picture and build a stronger organization - and that's data science." With the help of Microsoft, the team are continuing to develop use cases to prove the value of being data-driven and have around 15 projects in the pipeline.
  • The system, which is decentralized, uses Microsoft Purview for data governance, as well as various Microsoft products including Azure Synapse Analytics, Azure Databricks, Azure Machine learning, and Azure SQL.

Evidence from the source

"With this solution, enabled by Azure OpenAI Service, we know when the weather will prevent us using a crane on-site," begins Bornschlegl.
Strabag SE, the global construction company, partnered with Microsoft to build a Data Science Hub to collect decentralized data and leverage it for insights.
The system, which is decentralized, uses Microsoft Purview for data governance, as well as various Microsoft products including Azure Synapse Analytics, Azure Databricks, Azure Machine learning, and Azure SQL.
This enabled the organization to develop use cases to prove the value of data including its risk management project.
This is why risk management is such an important part of what we do." Strabag SE used Azure OpenAI Service to create a solution that allows it to assess the potential impacts of a project in its earliest stages by comparing it against all its realized projects - therefore minimizing the risks that come with it.
With approximately 75,000 employees, its projects range from civil engineering to transportation infrastructure.
Transparency Evidence & methodology 66/100 reference value · 87% confidence · 1 source

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

01

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 value66/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 confidence87%
  • 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
    6 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 specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

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

  1. Microsoft

    Strabag SE customer story

    First-party
    https://www.microsoft.com/en/customers/story/1596825960615628483-strabag-discrete-manufacturing-azure-en-austria
    Source type
    Vendor-published customer story
    Published
    Feb 9, 2023
    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 Feb 9, 2023 · 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.