Building control systems have traditionally managed heating, ventilation and air conditioning (HVAC) separately from other systems. Siemens Industry, Inc., envisioned a greater sophistication: Why not build a control system that would co-ordinate all these variables, as well as monitor building humidity, fire alarms, door locks, security, lighting and other building features, all while collecting historical data and storing network configuration information?

Knowing the importance of a flawless data architecture in the finance industry, EFL was considering multiple options to upgrade their data warehouse. This system lies at the centre of their operations and plays a crucial role in allowing them to provide the highest quality services to their numerous customers. One of the possibilities they were investigating was a full migration to an innovative data warehouse solution — Snowflake. EFL has a large and complex data warehouse, with multiple company departments requiring access to parts of it. The client wanted to check how effectively Snowflake’s tools can help manage this situation. In general, EFL wanted to validate whether this platform meets their needs in terms of functionality, performance and cost-efficiency. It was crucial for them to get an independent expert opinion and not rely purely on the data and materials provided by the vendor. Objectivity was selected as a result of successful collaboration between the two organisations on multiple occasions in the past.

The Client needed a reliable technology partner to take over the support of a business-critical system. Objectivity ensured continuity of operations and proposed a set of enhancements to reduce technical debt as well as improve the user experience and stability of the application.

The existing enterprise architecture needed to be assessed to determine if it could accommodate Tonik Energy's planned business growth.

The client, a major airline, faced challenges in processing over 10,000 invoices a month from various partners in different formats and languages. This manual process was time-consuming, error-prone, and delayed data analysis.