Alteryx > Case Studies > Siemens' Efficient Data Management: A Case Study on Alteryx and Tableau Integration

Siemens' Efficient Data Management: A Case Study on Alteryx and Tableau Integration

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Technology Category
  • Application Infrastructure & Middleware - Database Management & Storage
  • Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
  • Cement
  • Equipment & Machinery
Applicable Functions
  • Maintenance
Use Cases
  • Leasing Finance Automation
  • Time Sensitive Networking
Services
  • System Integration
About The Customer
Siemens is a multinational conglomerate company founded in 1847 in Berlin, Germany. The company operates in various sectors including industry, energy, healthcare, and infrastructure. With a global presence in 85 countries, Siemens employs around 380,000 people worldwide. In this case study, the focus is on Siemens' smart infrastructure operating company, which was seeking to automate its data preparation and load process. The company was dealing with large volumes of complex corporate financial data and was looking for a code-free, fast, and agile solution to streamline its operations and improve productivity.
The Challenge
Siemens, a global company operating in 85 countries, faced a significant challenge in consolidating financial data from across the company. The process was complex, involving the integration of financial data with external market data, productivity data, and detailed data on customers or products. The finance department was tasked with calculating numerous KPIs, growth rates, and margins, which were then aggregated through a regional hierarchy and business segmentation. This entire process was conducted using spreadsheets, leading to a high risk of errors in the complex formulas used. Furthermore, any slight change in the analytical question required the controller to redo the entire analysis, a time-consuming and labor-intensive process. The company also faced difficulties in maintaining and updating a manual data preparation process that involved 3,000 lines of VBA code, which was prone to errors and hard to hand over to another person.
The Solution
Siemens adopted Alteryx to automate their data preparation and load process. Alteryx was used to connect multiple data sources, transform them, and output them to Tableau. These data sources were then made available on Tableau Server for building any analytical content. Alteryx workflows were hosted on Alteryx Server and could be scheduled to run at any desired time, ensuring a fully integrated process. Data was extracted directly from the sources, run through the workflow, and then sent directly to Tableau Server. Siemens also used Alteryx's reporting tools to monitor the processes and receive immediate notifications in case of any failures. The company also implemented an automated update with Alteryx that ran every three hours, significantly reducing the time spent on data preparation. Additionally, Siemens used Alteryx to join information stored in database files on the cloud file share, providing a fast and easy connection. The workflows were then published directly to the Tableau servers.
Operational Impact
  • The integration of Alteryx and Tableau has brought about significant operational improvements for Siemens. The company can now answer complex questions quickly and reduce many manual efforts. The solution has also enabled Siemens to avoid the disconnection between the business and technical experts, allowing them to implement end-to-end business solutions themselves in just a few weeks instead of months. The self-service capabilities of Alteryx have enabled teams to move quickly on analytics projects and maintain full control over processes and updates. The solution has also fostered a platform for exchanging ideas on productivity actions, enhancing transparency across the business. Furthermore, the Alteryx workflows are easier to maintain, and the Siemens internal Alteryx community is helping build data literacy within the organization.
Quantitative Benefit
  • Significant time saved in preparing data for productivity reporting
  • Automated update process reduced from 4 hours to just 5 minutes
  • Data volume of around 50 million rows processed every 2 hours

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