In my previous blog, I discussed CFOs driving analytics into finance processes to optimize visibility and decision making. Today, I want to discuss how finance is becoming the hub of quantitative analysis and data stewardship.
Finance teams becoming quants¹ (quantitative analysts)
The era of Big Data is upon us, and it brings the Internet of Everything. Twenty billion devices are emitting information, from phones and watches to crates on ships, pallets on trucks, planes flying overhead, and even satellites photographing cars in parking lots. The global supply chain is now radically transparent.
Making this deluge of unstructured data intelligible was long the preserve of data scientists writing custom code. New predictive modeling applications with easy-to-use interfaces supply business users with the tools to integrate IoT and social media data, and move beyond rudimentary methods such as trends, extrapolations, and even exponential smoothing techniques once seen as cutting-edge.
While the new analytic tools are intuitive for data exploration and self-service reporting, statisticians and data scientists are joining finance to help develop hypotheses and interpret data sets for new insights.
- Visionary CFOs are dispatching teams to search for patterns in buying behavior predicted by sentiment analysis.
- Revenue forecasts incorporate price elasticities, viral coefficients, and social influencer impact to estimate unit volumes and price yields.
- High volumes of activity-driven data are sourced and used to attribute operating expense to distribution routes for profitability calculations.
- Business unit controllers determine variability of detailed cost pools to understand true operating leverage.
- Billing and collections managers analyze customer region, industry, and idiosyncratic factors in multivariate models to proactively address payment behavior.
- Procure-to-pay process components including inventory, cash, and labor are seen as constraints in an equation to optimize the objective function.
- With the goal of retaining the most important assets—employees—finance collaborates with the human resources team to scenario-model turnover using competitive and macroeconomic inputs.
- These quantitative corporate finance models are the next frontier for business model innovation and gaining competitive advantage.
All roads lead to finance
The foundation for analytics is harmonized data. Disparate source systems are frequently not standardized, with more than a few not having a documented data model. Throughout the enterprise, these silos begin as special projects and often proliferate into data lakes without cross-functional connections. CFOs are engaging as corporate information stewards to own and maintain the single source of truth.
Mastering organizational data to ensure quality and consistency is a finance core competency. Whether it’s defining and tracking a customer, product, supplier, process, employee, contractor, inventory item, fixed asset, or legal entity, finance is the hub to establish a data dictionary, and to access controls and security protocols.
Leveraging a common information taxonomy with clear decision rights will drive efficiencies from order to cash, procure to pay, record to report, and plan to perform. These responsibilities also provide critical support for finance in mitigating regulatory and reputational risk. The master data fiduciary role aligns well with the historical finance strengths of detail orientation, data integrity, and a 360-degree view of the business.
So, how can the finance organization transform to utilize these new technologies and skill sets for maximum impact? In my next blog, I will discuss the rise of the machines. Stay tuned!
Want more information on how to utilize technology to kick start dynamic planning? Read the research paper, Dynamic Planning: Live in the Moment to Succeed in the Future.