How To – Use Field Level Analysis To Optimise Category Cost


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In our second article in the How To series we focus on how you can utilise Xmon with field level analysis to optimise your cost further.

When downloading reference data from third party vendors you will be charged for each billable category, which you are requesting. A billable category is charged as soon as a single field from that category is requested. As a consequence, it is crucial to know what is requested in your organisation across all systems and environments at a field level. Such a granularity could be challenging and time consuming to report month after month, unless you are equipped with a “smart meter” for reference data, such as Xmon.

Xmon is able to analyse the traffic of reference data downloads made across all systems and environments of your organisation. Our advanced reporting engine consolidates all requests for a given calendar month and provides a distinct list of fields requested by category and provides a cost estimate for each request, as well as aggregated cost indicators.

Equipped with this analysis, Xmon empowers the data management team to identify expensive categories, which are triggered by just a few fields, as well as challenge the business to review the necessity of retrieving this data. Our clients were able to reduce their overall reference data cost between 10% to 15% on multiple separate occasions, by only turning off fields that were no longer used in downstream systems.

If the data is required by the business, the review is usually followed by validating the scope for those fields by asset type, as indeed not all fields may be needed across all asset types. The approach of “one-size-fits-all” is quick to implement but more expensive to run, resulting in spiralling and uncontrolled spend. In one of client use cases, we guided a large asset manager to split data acquisition requests by asset type and only select fields relevant to each asset class, generating a cost saving of around 22%.

Once the above optimisations have been performed, we are able to focus on investigating alternative commercial models within the same vendor. Depending on the volume of data requested, one pricing model might be more optimal than the other. In these cases, Xmon is able to provide out-of-the-box simulations comparing pay-as-you-go pricing, band pricing, bulk data subscriptions and enterprise models. Helping you to find the most optimal pricing model could generate significant savings up to 33%, as was the case with one of our large insurance clients.

If you are looking for help understanding and optimising your reference data spend, get in touch!