Why Regulated Market Infrastructure firms are turning to a big-data IT Cost Tools
The Finance Departments of Market Infrastructure firms need to provide transparency of internal costs at a segmental level to their regulators, in order to prove they are pricing their services ‘fairly’. They also need to provide evidence to their auditors that the computation of Transfer Pricing charges and VAT recoveries are fully compliant with global accounting standards.
In order to meet these requirements, Finance need to implement a consumption-led IT Costing Model. IT Costing Models are by very nature complex and multi-tiered. The global Legal Entity (LE) structure and complex technology ecosystem of Market Infrastructure firms further adds to the complexity.
To deal with this complexity, a core requirement of the IT Costing Model is that it has to be legal-entity aware – with the source costs clearly tagged as they pass through each tier of the IT Cost Model. Secondly, as the staff, contracts, and infrastructure could be distributed anywhere globally, each line of cost should be split in proportion to its cost consumption and enriched by the attributes of each passing layer of the IT Cost Model. These requirements collectively provide end-to-end transparency of costs from source LE to consuming segmental / divisional level.
As traditional tools such as Excel and other 3rd party vendors are based on legacy relational databases; they lose the granulari- ty of costs as it navigates through the various tiers of an IT Costing Model. Whereas CostLens’s big data engine does the exact opposite – it enriches the cost details with meaningful and relevant attributes, as it passes through each tier of the IT Costing Model.
While the core demand on Finance Departments may be driven by Regulators and Auditors; an IT Costing Model is incredibly useful for IT decision making, as most decisions within an IT department are relied upon Finance to produce accurate cost-benefit analysis, thereby automating the reporting that is required.
Improving the data quality and accuracy of the model enables Finance to support IT Departments in a more strategic manner. Informed data-driven decisions such as:
i. Annual cost of ownership of applications/services for Business Case inputs
ii. Benchmarking against industry standards to identify Cost efficiency savings help Finance add strategic value by moving away from a purely operational emphasis.
Read the attached PDF for further details