Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Cement
- Finance & Insurance
Applicable Functions
- Procurement
- Sales & Marketing
Use Cases
- Inventory Management
- Time Sensitive Networking
About The Customer
FLSmidth is a multinational engineering company based in Denmark that provides global cement and mineral industries with factories, machinery, services, and expertise. The company has nearly 12,000 employees worldwide and has been growing for more than 130 years, with numerous acquisitions adding to its size and complexity. FLSmidth not only makes money with plant sales but also with aftermarket parts sales. The company's goal is to become a data-driven enterprise and turn its data assets into revenue-generating resources.
The Challenge
FLSmidth, a multinational engineering company based in Denmark, faced significant challenges in managing its data assets due to its size, history, and the complexity of its data environment. The company, with nearly 12,000 employees worldwide, has been growing for over 130 years, with numerous acquisitions adding to its data complexity. Each acquisition brought in new systems and data assets, which were not readily available to everyone who might need them. Additionally, valuable tribal knowledge often got lost when employees left the company. These complexities posed significant roadblocks to achieving the company's top-line goals of creating a data-driven enterprise and turning data assets into revenue-generating resources.
The Solution
FLSmidth found a solution in implementing a data catalog. A data catalog helps untangle data complexity by enabling faster data discovery, centralizing documentation on data, and fostering collaboration that leads to greater productivity. However, the challenge was to connect the data catalog to tangible business value. FLSmidth did this by finding concrete use cases that demonstrated how the data catalog could improve the bottom line. For instance, they used predictive analytics to identify when a part would fail 90-days beforehand and proactively inform the customer when they should order the part to minimize negative impact. Another use case was improving their ability to sell products by identifying and stocking parts that were needed more often. After an extensive search, FLSmidth chose Alation’s Data Catalog for its collaborative approach and machine learning capabilities.
Operational Impact
Quantitative Benefit
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