Giving FX trading
a competitive edge
Clarifying complexity: making sense of 90 million rows of data
The FX trading division of a European Investment Bank was looking to enhance the efficiency of the operational processes supporting their business line. Their operational processes, like those of any financial institution, are challenged by a variety of factors. By leveraging visual representations of data, they were able to optimise their operational processes, increase efficiency, and gain a competitive edge in the dynamic FX trading environment.
Empowering FX Trading with actionable insights, timely decision-making, and enhanced risk management capabilities
To uncover operational efficiency opportunities, Calimere Point conducted a three-month data analysis with over 90 million rows. We used unconventional tools to make sense of this data and effectively conveyed the intricate operational processes to stakeholders, spanning from team leads to senior management.
Industry: Financial Services - Investment Banking
Calimere Point had conducted a data analysis exercise to gain insights into operational efficiency opportunities.
Three months’ of data saw datasets with more than 90 million rows. We had to use non traditional tools to bring clarity and insight into such data.
The complexity of the underlying operational processes needed to be clearly communicated to stakeholders from operational team leads up to senior management levels.
Designing the Optimal MIS Solution
The client already had data dashboards but they had become unwieldy and needed to be throttled back so they could run.
Calimere Point's combination of modern data analysis tools that then fed data visualisation software was a more efficient and manageable process.
The Dynamic Dashboard
The ability of modern dashboards to cut data across multiple dimensions simultaneously and hone into an area of interest helps to find the patterns across.
Heat maps allowed us to see the user groups, desks and timestamps where more issues occurred and then follow the chain up to the initial data fault.
Our “Hirst” diagram allowed us to easily identify calendar errors.
Histograms cut across different time stamps and trade types identified pathological time intervals.