Although the average finance function does not have to contend with so called “Big Data” (terabytes, petabytes, and even zettabytes of data) it is required to battle daily against the unrelenting demand for additional information brought about by new regulations, complex business models and operational needs. Take for example a new European Parliament Directive (2014/95/EU) which from 2017 will require Europe’s 6,000 largest companies to disclose material non-financial and diversity information on social and environmental matters, such as, employee-related aspects, respect for human rights, anti-corruption and bribery issues, and diversity on boards of directors. And all of this is in addition to other regulatory requirements specific to each industry such as Solvency II for insurers. It is clear that regardless of industry sector, CFOs need to be prepared to blend data from a variety of data sources – often with very little notice.
Historically, interfaces between core financial processes and other systems has been the CFO’s Achilles heel. In the past, responsibility for extracting, transforming and loading information from another source system has typically been ceded to the IT function, often leaving finance professionals unable to respond quickly enough to changing information needs or new sources of data. In the current environment in which businesses are being ravaged simultaneously by the twin challenges of rampant economic, regulatory and market change and unprecedented volatility, finance professionals need to be masters of their own destiny.
So the tools for harvesting and managing data from multiple data sources need to be finance-friendly, allowing non-technicians to extract data and manage interfaces. But today’s pressing demands also mean that data must be traceable from its origins in source systems right the way through to its final destination in management and statutory reports.
It’s what OneStream calls “walk-through”, i.e. where the system can identify and report separately on what has happened to the data (original unadjusted data captured from source systems, currency translation, adjustments and so on) at every stage of its journey. And all of this needs to be bi-directional so that finance professionals can drill back to source systems or forwards to consolidated numbers as needed.
But data-blending in corporate performance management needs to handle more profound changes as well such as the need to accommodate more granular information in budgets and forecasts, the ability to deliver very fast, almost real-time information updates from masses of data, to accelerate management decision-making and the ability to combine multiple data-sets (relational (Tabular) and/or Analytic (Matrix)) to provide new insights.
Data-Blending provides seamless transitions between different data-sets and opens up an entirely new approach financial modelling. Optimal analytic models can now be supported and supplied transparent relational detail, thus negating the need for constant metadata maintenance. This allows financial planning administrators to more efficiently deliver and maintain “agile” planning cubes. For example, highly detailed plans can be created and stored relationally, while only the relevant summary is placed into the OneStream XF analytic model. Data-blending, delivers seamless drill down to the relational details. With data-blending, companies can have the best of both worlds by keeping the detail available on demand in source systems or relational tables in OneStream XF while also retaining an agile budgeting model.
It may not grab the headlines but data-blending is emerging as a key ingredient in corporate performance management.