According to a recent KPMG study, 90 percent of finance organizations see data and analytics (D&A) as a top investment priority — with a focus on moving toward predictive and prescriptive analytics. However, despite the high appetite for D&A, most finance organizations are struggling to get the value they want, and many CEOs are concerned about the integrity of the data that drives enterprise decisions.
To remain relevant, finance organizations must transcend their role as recordkeepers and become enterprise-focused business partners that deliver financial, operational, and strategic insights. And as the speed of disruption increases, finance also must move quickly. But all too often, organizations start by assessing requirements for a new process technology; then they spend months or years trying to harmonize systems and perfect their data before building the solution.
“A better, faster approach is to start with the endgame — the business questions the company is struggling to answer — and then work backwards to determine what data is necessary, where to get it, what technology is required to answer the questions,” says Joe Moawad, KPMG Finance Transformation principal in Chicago, “with this kind of reverse engineering, or “right-to-left” planning, companies can build analytics relatively quickly for a specific area of strategic focus.”
As expectations for insights and analysis shift from descriptive and diagnostic to predictive and prescriptive, finance must become not only technology-enabled but also performance-centric. That requires accurate insights through dynamic forecasting and planning. Part of this effort is identifying the right KPIs to measure, based on the internal and external drivers of growth, profitability, and sustainability. Another part is integrating the company’s planning processes. Indeed, CEOs expect that one of CFOs’ top priorities should be aligning planning to corporate strategy.
By identifying the right KPIs for corporate strategy, the CFO of the future will set the drumbeat for how the company operates. Finance organizations are starting to look at performance measures in new ways:
- Customer lifetime value. This measure, long used in telecommunications, is becoming common in other industries as well. Instead of focusing just on short-term revenue, companies are focusing increasingly on long-term customer retention.
- Customer experience profitability. Finance will help companies strike the right balance between the experience that customers expect and what makes financial sense to deliver.
- Growth from digital channels. This emerging metric, becoming especially prominent in insurance and banking, reflects the ROI in digital products and services.
- A twist on traditional capital metrics. Going beyond return on assets, invested capital, or capital employed, leading finance organizations are taking a venture capitalist’s (VC’s) view. They’re contemplating what the future could look like — in terms of new services, markets, and business models — and evaluating alternative investment scenarios with less traditional measures of value.
In the future, the data for these predictive and prescriptive analytics — along with the descriptive and diagnostic measurements used for financial statements and operational reporting — will become fully automated, improving speed to insight. New technologies will extract data in real time from multiple systems and publish it straight to user-friendly dashboards that are available to the business for collaboration, analysis, and better decision-making.
“Across industries, leading finance organizations have already started down the path,” says Moawad, “some companies have begun using machine learning to correlate multiple data streams, uncover new insights, and develop more accurate forecasts.”
Finance is becoming the enterprise provider of data-driven insights, and it stands to reason that CFOs should lay a critical role in the enterprise data strategy. In addition to owning and organizing all financial data, finance can take responsibility for the quality and governance of other data as it impacts enterprise performance.
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