Treasury Today Country Profiles in association with Citi

Data mashups help improve decision-making and reduce costs

New analytical tools and processes based on concepts derived from the internet will be the next stage in driving treasury efficiency and improving decision-making.

In web-speak a mashup is a web page or application that uses and combines data from a number of sources to create new services and information that were not the reason for the collection of the original source data. By bringing together disparate data sets in previously-unheard-of ways, treasurers will gain new insights into the true drivers of their businesses and new benchmarks against which to measure their performance.

As the authors at Spend Matters point out in their Compass publication, Spend Visibility and Beyond – Analytics Broader Role in Procurement and Supply Chain, with new spend analysis systems, data mashups will be possible that leverage both spend data and other sources of information.

They point to a number of new potential data combinations, including: tax/VAT payments and types, budget data versus actual, sourcing and savings data, commodity market pricing feeds, marketing analytics data, credit and P-card information, supplier financial risk data, vendor files with line-item invoice detail, inventory data, services/contingent workforce data, payment terms including working capital and cost of capital for both buyer and supplier, logistics data, and claims adjusting information.

In addition, research firms Gartner and Forrester Research have both come out recently with studies on the impact that these new analytics solutions will have on business and finance.

For example, from a corporate treasury perspective, take foreign exchange. Imagine a company has a number of upcoming debt issues or M&A plans that will require major foreign currency transactions. New business intelligence systems could troll through preselected external data feeds and be set up to automatically send real-time alerts via an internal social media solution — both via web and to mobile devices – to the treasury FX manager, the treasurer and the CFO when spreads on a certain currency pair hit a certain level.

Dashboards with modelling tools graphically displaying the impact of an FX trade at that time on current forecasts and liquidity could be set up via the BI tool to be automatically sent through the social media solution, alongside the alert. Then the three executives could immediately — via the social networking tool — discuss the value of enacting a transaction at that point, the CFO could give approval and the FX manager could enact a trade, all in near-real-time.

While this is a simple example, the immediacy of information provision – and the association of different data streams – to a treasurer, treasury staff, and to the CFO—plus the ability to connect that with other technologies, such as social media and mobile devices, could be invaluable in improving decision-making.

However, it requires technology that can not only pull that data from highly disparate sources but also clean it and provide flexible analytics around it that are specific to the particular needs and wants of the user. In the past, most analytics solutions had preset variables that could be analysed based on preset data units.

New spend analysis and business intelligence technology that has been evolving over the past year and a half is focused on providing greater flexibility into how data is classified and analysed. By being able to not only choose how data is classified, but also reclassify it as desired it lets users cut and recut the data to get a different perspective and meet different objectives.

Plus, new systems will have the ability to clean data, fix mistakes, and match the cleaned data in different ways. Some vendors are already offering business intelligence and data analytics solutions that allow data mashups, albeit with varying levels of customization — such as Rosslyn, BIQ and Endeca.

For companies with sophisticated treasury and finance operations that want to make better use of all the varying data at their fingertips to improve decision-making and reduce costs, it makes sense to look into the possibilities. It will be some time before these solutions become an everyday reality, but they are the future.