In this article, we will analyze the case of a gas provider that is undergoing organizational restructuring, including a remodel of their data warehouse to improve time responses and insights by using Data Vault 2.0.
About the Client
Problem Statement
The gas industry is very versatile and is confronted with fast-paced changes. Here we can find issues like:
- Incomplete data integration and suboptimal usage of data leading to inefficient daily operations
- Inability to meet business needs due to rapidly changing demands from users
- Lack of flexibility when facing adjustments or modifications of current technologies (like closing or opening Data Source Platforms)
- Information silos and high complexity of data harmonization for each business area
The Challenge
In this case, we find a handful of challenges, for example:
- Limited resources for information delivery purposes (on premise infrastructure)
- Adapt to business requirements in an agile way
- Provide highly complex information, needed for strategic business projection (in elements like planning and forecasting)
- Multiple Information Marts dependent on the data warehouse layer
- Deliver near real-time high quality information, so operators can make quick decisions. This is a key aspect of the company’s financial sustainability
The Solution
In this case, the customer sought assistance from Scalefree to build a strong, flexible data warehouse (DWH). The solution was based on remodeling their current DWH with Data Vault 2.0 methodology. The Data Vault 2.0 architecture helps to integrate the current source systems, but also ensures flexibility, so absorbing changes is easy when source systems are added or eliminated. Besides, the Data Vault 2.0 provides iterative work to quickly adapt to changes.
Tangible Results for the Client
Through the implementation of the Data Vault 2.0 methodology, the company, was able to:
- Optimize operational processes
- Reduce of costs via improved productivity thanks to faster analytics
- Increase agility in the development of business users requirements and changes in the data platforms
Technologies involved
Interested in learning more? Get the detailed case study!
Marc Winkelmann
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