Introduction

Siemens, a Fortune Global 500 and powerhouse in the fields of industry, energy, healthcare, and infrastructure, often relies on vast Excel data sets and spreadsheets to drive its decision-making processes. These Excel files are crucial for their operations, analytics, and financial reporting. But, like any organization working at this scale, there’s always the threat of data corruption.

This case study aims to illustrate the challenges Siemens faced with corrupted Excel files, the solution they adopted with DataNumen Excel Repair, and the results of this implementation.

The Challenge

On July 2016, Siemens’ internal IT team noticed an increasing number of reports from different departments about Excel files getting corrupted. The reasons varied – from unexpected shutdowns to issues during data transfers or storage device failures.

These corruptions led to several challenges:

  1. Operational Delays: Many teams at Siemens rely heavily on Excel for their day-to-day activities. A corrupted file could mean delays in reporting, processing orders, or even making crucial business decisions.
  2. Data Integrity Concerns: Siemens had to ensure that the data they were viewing and using was not just accessible, but also accurate. With corrupted files, there was always a risk of inaccurate data, leading to flawed decisions.
  3. Resource Drain: The in-house IT team was inundated with requests to fix corrupted files, taking them away from other essential tasks and projects.

The Solution: DataNumen Excel Repair

Upon evaluating various tools and solutions, Siemens decided to integrate DataNumen Excel Repair into their data recovery strategy.

Below is the order placed by the reseller Comparex Group:

Siemens Order

DataNumen Excel Repair stood out for several reasons:

  1. High Recovery Rate: During the evaluation phase, Siemens found that DataNumen consistently outperformed other solutions in terms of recovery rates.
  2. Ease of Use: The tool required minimal training. End-users could often recover their files without needing to escalate to the IT department, reducing the internal ticket load.
  3. Bulk Recovery: Given the size of Siemens, the capability of DataNumen to handle batch recovery was invaluable, saving both time and resources.

Implementation

Siemens initiated a phased rollout of DataNumen Excel Repair. The pilot phase involved training the IT department, creating a set of best practices and guidelines, and deploying the tool to high-priority departments.

Following the success of the pilot phase, Siemens expanded the deployment across other departments, offering training sessions and creating an internal knowledge base to aid employees.

Results & Benefits

After six months of implementing DataNumen Excel Repair:

  1. Reduced Downtime: The most immediate benefit was the drastic reduction in downtime due to corrupted Excel files. Employees could now quickly recover files on their own, ensuring smooth operations.
  2. Improved Data Integrity: With DataNumen’s robust recovery capabilities, Siemens was confident in the integrity of the recovered data.
  3. Reduced IT Workload: The number of tickets related to Excel file corruption dropped significantly, allowing the IT team to focus on other critical areas.
  4. Cost Savings: With faster recovery times and reduced IT involvement, Siemens estimated a substantial cost saving, both in terms of man-hours and avoided potential losses due to data corruption.

Conclusion

Data integrity and availability are critical for a global organization like Siemens. With vast amounts of data managed and processed daily, even minor disruptions can have significant ripple effects. The integration of DataNumen Excel Repair proved to be a strategic decision that addressed a recurrent problem efficiently.

DataNumen not only provided a tool but also a solution that enhanced operational efficiency, ensured data integrity, and reduced costs. Siemens’ experience stands as a testament to the importance of having robust data recovery solutions in place in today’s data-driven world.