Reported impact
Business outcome
The interoperability of the two solutions, Lautenschlager reports, has reduced data transfer times far more than the company initially expected.
- Productivity Improvement
- Revenue Growth
- Risk Reduction
- Scalability
MicrosoftManufacturingData Lake / Data Warehouse ModernizationEnterprise Knowledge SearchReal-time AnalyticsAI and Machine LearningData AnalyticsData PlatformGenerative AI / LLMProductivity ImprovementRevenue GrowthRisk ReductionScalability2024 MicrosoftMS#0078d4ManufacturingGermanyCompany leadership also wanted to embrace more advanced AI and unified analytics solutions.Machine Learning Azure Databricks Azure Synapse Analytics Power BI Azure Machine Learning ORGANIZATION SIZE…Feb 7, 20245Data Lake / Data Warehouse Modernization|Enterprise Knowledge Search|Real-time AnalyticsAzure Databricks · Azure Synapse Analytics · Microsoft Copilot0 2024-02-07 5
NETZSCH Group
Data Lake / Data Warehouse Modernization · Enterprise Knowledge Search · Real-time Analytics
Company leadership also wanted to embrace more advanced AI and unified analytics solutions.
Machine Learning Azure Databricks Azure Synapse Analytics Power BI Azure Machine Learning ORGANIZATION SIZE 1,000-9,999 employees COUNTRY Germany BUSINESS NEED...
The resultant table had 82 million rows of data
The resultant table had 82 million rows of data and took up nearly 1.4 gigabytes of space.
Solution
NETZSCH Group modernized its analytics estate on Microsoft Copilot, Power BI, Power Platform, moving from fragmented legacy tooling to a governed cloud data platform. Operational, enterprise, or sensor data is ingested into a shared cloud store and transformed with Azure engineering services so teams can work from one current source of truth. Power BI then serves dashboards, self-service exploration, and decision support to business users at scale. The interoperability of the two solutions, Lautenschlager reports, has reduced data transfer times far more than the company initially expected. The architecture emphasizes managed services, reusable integrations, and governed data handling so the organization can scale adoption without rebuilding the solution for each new team or use case.
Data flow
Data flows from operational systems through source-system connectors into the platform data layer, where Azure Databricks prepares and enriches it; Power BI then exposes governed analytics and decision support to end users.
Solution architecture
- Compute
- Azure Databricks Transforms large-scale data and runs analytical or machine-learning workloads.
- Azure Synapse Analytics Runs integrated SQL and big-data analytics across enterprise datasets.
- Serving
- Microsoft Copilot Provides generative assistance inside end-user productivity workflows.
- Power BI Delivers dashboards, reports, and self-service analytics to business users.
- Orchestration
- Power Platform Provides the low-code automation and application platform for workflow modernization.
Evidence from the source
"By pairing that with our strong focus on self-service analytics, we very quickly realized that nothing else came close to Power BI in meeting our needs." The identification of Microsoft Power BI as a focal point of its new solution led...
Company leadership also wanted to embrace more advanced AI and unified analytics solutions.
In particular, NETZSCH Group was interested in the interoperability between Power BI and Azure Synapse Analytics, another part of the Microsoft Intelligent Data Platform.
The interoperability of the two solutions, Lautenschlager reports, has reduced data transfer times far more than the company initially expected.
Together, using both Synapse Analytics and Azure Databricks, the companies implemented the rest of the cloud infrastructure and services NETZSCH Group needed.
Transparency Evidence & methodology
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 value57/100
- Problem clarity12/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth15/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards 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.
- https://www.microsoft.com/en/customers/story/1729350670604692860-netzsch-azure-synapse-analytics-manufacturing-en-germanyMicrosoftFirst-party
NETZSCH Group customer story
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 7, 2024 · Microsoft · First-partyOpen 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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