Success Story High Tech

Large Shipping Container Operator Improves Data Quality by 30%

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Since 1996, the client has been the world’s largest container ship and supply vessel operator. As part of a Danish business conglomerate with operations in the transportation, logistics, and energy sectors, the head of clusters required that data from all clusters be compiled and analyzed to identify business trends and performance.

The client was having difficulty accurately measuring, collating, and analyzing this data based on the information provided by their team. Our solution was to create a centralized data warehouse that would process and store values from various clusters, allowing for proper analysis and insights.

the Results

Key Outcomes

At the close of the project, Apexon was able to deliver the following features and upgrades:

30% improvement in job report consistency & data quality

25% increase in contracts from existing customers due to improved relationships

25% increase in customer channel reporting built from new EDW

Geographical channels/areas in need of work could be identified to improve logistics-transportation, which resulted in the capture of 39 new agreements-customers in just a fiscal year

The challenge

2 key areas

Besides the complications involved in streamlining the existing infrastructure without disrupting the flow of data, Apexon specialists have discovered that:

Difficulty Analyzing Business Trends The client was having difficulty analyzing the business trends and performance of their cluster based on the shipment cycle and customer service provided by their team

No Centralized Data Store There was no centralized data store collating all these data from different clusters which made it difficult to store and analyze the data

The Solution

4 key areas

To address the customer’s requirements, we implemented a centralized data store with capability to collate and handle data volume efficiently.

Centralized data warehouse

Built a centralized data warehouse to process and store the values for 32 KPIs

Designed Data Models

Designed data models to store the values for 11 KPIs

Data Extraction & Processing

Designed data extraction and processing routines to handle the multitude of data types and handle data volume efficiently by applying best practices like application of filters and indexing

Dashboards

Built dashboard to render the KPIs

Success Story High Tech Large Shipping Container Operator Improves Data Quality by 30%