A telecommunications market leader wanted to understand how insight from available network datasets might help it achieve significant improvements in the performance of its network, including its mobile and fixed-line infrastructure. The company already had access to hundreds of terabytes of data per day on all aspects of its infrastructure, but it was underutilised in terms of its potential to provide valuable new insights, including on network performance, outage patterns and customer satisfaction levels.
The company had access to a large number of rich data assets, however, extensive work was needed to migrate them into a single, cloud-based ‘datalake’, and then to identify which of those datasets were potentially valuable for use in the generation of new, machine learning-based insight. Additional work was also needed to put in place suitable systems to support – and where possible automate – the ongoing assembly of that data, to enable it to be readily utilised in the generation of insights into the future.
Maltem Australia deployed a 20-person, specialised team of data engineers, scientists and business professionals to support the company’s own team in assessment and assembly of the datalake.
This included supporting the identification, triage and preparation of critical datasets for migration, assisting with the processes for ingestion of those datasets into the new datalake infrastructure, and the development of data dictionary and data modelling assets, to support ongoing use of the system and functionality.