Just the Headlines

Short on time? Here are the key facts.

  • A logistics company reached out to Aptitive seeking help combining information across their key data sources to gain efficient and holistic reporting.
  • The data vault solution implemented by Aptitive provided a scalable, organized, and audited platform that standardized and centralized the company’s various data sources.
  • By implementing a data vault model to integrate all of the source systems, the logistics company was able to easily access all of their delivery and financial KPIs through reporting.

Overview

A global logistics and transportation company sought to improve their data strategy as a means to drive efficiency and better utilize insights within their data. Aptitive implemented an innovative data modeling method called data vault to integrate four organizational data sources. This model ensured that the business could analyze trends across all of its data sources while providing a scalable data structure for future changes.

Industry

Transportation and Logistics

Featured Technologies

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The Challenge

The logistics company wanted to develop reports that tracked KPIs and trends across a holistic view of their data. However, gaining insights across their many data sources proved difficult because their data was not stored in one centralized place. With the addition of more data sources anticipated, they needed a plan to integrate their data. These challenges led to barriers in identifying areas of improvement within the company that were essential to remain competitive in the logistics industry.

The Solution

Aptitive began by modeling the four most essential source systems with the knowledge that more systems would be integrated later. The Aptitive team determined that a data vault model was the best way to address these challenges for multiple reasons:

  • The data vault structure (hubs, links, sats, and refs) directly correspond to reporting structures of dimensions and facts. Allowing for an easy integration of multiple data sources in the data warehouse.
  • The model would speed up data transformation through parallel loading.
  • Data vault allows organizations to add data sources without redesigning the solution or significantly interrupting productional data flows.
  • It naturally accounts for auditing historical data.

The Outcome

By implementing a data vault model, the company quickly was able to integrate their data sources and report on trends across a holistic view of their data. The solution ensured the client achieved this goal because data vault does the following:

  • Automatically saves a history of attribute changes.
  • Centralizes data across multiple source systems – this is essential for logistics companies who often use multiple data inputs and acquire other companies.
  • Flexibly scales so data can be added when business changes occur without deleting information.
  • Stores relationships in separate tables than attributes to decrease the time it takes to aggregate metrics.

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