Data Engineering for an Asset Management Product of an IT Giant


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Data Engineering for an Asset Management Product of an IT Giant

Client Background

Client is a global IT service giant that provides hardware, software and services to consumers, small to medium to large sized businesses covering customers in the government, healthcare, telecom and education sectors.

Business Context

Client had embarked on a journey to build a state-of-the-art asset management product and had distributed teams across the globe working on this. There were teams looking for data – right from development to testing to technical support teams – across different environments- Dev, QA, performance, and training.

Documentation of schema was considered a low priority as a result of which much of the information was lost due to vendor retrenchment. The product management team was expecting a quicker turn-around from all the teams on grounds for the grand launch of their product

How did we help the Client?

Adopted a two-pronged approach – a) leveraging whatever inputs existing business teams could provide as well as leverage limited database schema inputs that had been documented b) adopted a re-engineering approach, wherein we performed various business transactions, monitored how tables got impacted, how the relationships were working out across tables.

Custom toolkit for data intelligence was used to capture inputs about the data requirements from the business teams and apply data intelligence to ensure data quality and data distribution as desired by the stakeholders.

Leveraged native SQL queries in the course of data engineering exercises, to understand the relationships, and dependencies amongst tables.

Benefits Delivered

Designed solution helped create meaningful data catering to the needs of electronic asset management products within the stipulated timeframe helping the client get back on track with their release schedule.

Created data was loaded across different environments, leveraging one-time generated data across multiple environments- which gave a considerable head start to all stakeholders through quicker release cycles.

Toolkit provided significant value add to the customer in terms of helping them get the larger perspective of data to be present, as well as helping the customer refine some of the requirements.

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