Big data for Finance, is it necessary? - Part II


After reflecting on it a bit more I've come down to 2 main drivers why big data for Finance is a necessity:


  • The first is that regulators, especially in the financial services industry, are asking for data on a more granular level. Initiatives like IFRS 4 phase 2 and Anacredit are requiring data on a contractual level to be reported and I guess that more initiatives like this will come. Say what you want about regulations but one positive is that they create a level of urgency in organizations to take action and IFRS 4 and other regulations will force finance organizations to take action on big data to meet these regulatory requirements.


  • The second reason I discovered during a session on Integrated Reporting is, that it is expected that predictive analytics is needed to define which KPIs are the most material to report on. In other words predictive analytics are needed to Identify those KPIs (from the hundreds or thousands of possibilities) which are most important to building an Integrated Planning and  Reporting process and platform that supports the business model. It's not only about being accountable by reporting, but also forward looking performance management with predictive analytics is needed to optimize the business model for all stakeholders. Again in the financial services industry I see a change from accountability to forward looking performance management to be better in control and to better support the business with analytics and accurate rolling forecasts for both financial AND non-financial information.

For predictive analytics a lot of reliable data, calculations and visualisation capabilities are required to do scenario analysis in order to define materiality and a reliable forecast and the finance organization has to have confidence in and control over a much more granular level of data to support this.  
At Tagetik we support our customers with our Business Integrator functionality to capture large volumes of data at a granular level in a staging database, perform calculations at a very detailed level and aggregations for reporting the results. This solution has been in use for several years by our banking customers in Italy and North America. In Italy it is used to report to the Italian and European Central Banks and in North America it is also used by several retail banks to calculate reliable cash flow and portfolio projections at the financial instrument level. Both use cases entail millions of rows of data and banking specific calculations at the lowest level of detail. In the past data volume limitations, performance degradation, and calculation sophistication made this close to impossible for the finance organization to accomplish. Now this type of big data use case is not only possible, it is becoming essential for regulatory reasons in financial services and in other industries for profitability analysis and kpi reporting at the customer, sku, or other very granular levels.  
My expectation is that the easy creation of a staging area for predictive analytics, modeling, calculations, aggregations for both reporting and rolling forecasts at a much more granular level will be common practice in the very near future, just as it is happening right now in financial services.  


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