So much has been written about the promise of business analytics to analyze data for competitive advantage. But, who should own analytics? The CIO? CFO? Some other business unit executive? According to 23% of respondents in a recent Deloitte Touche study, the most frequent leader of analytics is the business unit or division head, followed closely by the CFO at 18%. Some 20% revealed there was no single overseer of their business analytics. However, when they looked at the area most often found to “invest” in analytics, finance lead the pack at 79%.
Finance has long been data-driven; but in today’s competitive market, CFOs need to use analytics to forge a connection between strategic and operational decision-making. This approach translates into running the business day-to-day and making decisions at the operational level (which traditionally resided outside the finance chief’s domain) versus managing the business via traditional financial planning, budgeting and forecasting alone. CFOs can assume greater control of operational decision-making when armed with analytics that answer questions like “What products should be pulled forward in inventory or out of the supply chain completely?” or “Does it make sense for us to hang on to some of our larger customers that are the least profitable for us?”
While some CFOs may hesitate to lead analytics in operational areas because it’s not necessarily about issues that deal with the big picture, the insights that analytics can provide (about customer-related performance, for example) can go right to the bottom line. Analytics can be a strategic weapon in the way it leads to improved operational discipline and profitability performance. Based on Deloitte’s findings, and our own work with financial executives of manufacturing and distribution businesses, CFOs can lead the analytics “charge” by pinpointing business areas where those analytics can bring value and competitive advantage. Some of the areas in which analytics can drive value outside Finance include:
- Sales and Marketing (price points, revenue drivers, demand and price elasticity, customer retention and churn analyses, promotional spend analysis)
- Procurement (supplier spend analysis and vendor management)
- Business Units (analysis of margin erosion, pricing, asset utilization and service level / customer profitability alignment)
- Supply chain (collaborative forecasting, new product introduction profitability, etc.)
Identifying an analytics-focused initiative that cuts across areas to make a major impact at the operational level is step #1. As a corporate financial executive, you need to determine what business decision or goal would drive margin or growth through analytics outside your realm. As a case in point, one of the participants in Deloitte’s study (a $1B food manufacturer) led a spend analytics initiative that created supply chain efficiency and reduced costs by shedding insights that could help them better manage their sourcing function. The analytic model they used looked at SKU-level profitability to help the company better understand the types of decisions it could make to reduce costs. In this case, Finance crossed over into decision-making traditionally outside its function by using an analytics solution to evaluate sourcing and suppliers, and then deployed the data to its buyers on a monthly basis.
While cross-enterprise analytics is an endeavor that takes commitment, there’s no doubt that it can pay off handsomely in the end in terms of enhanced profitability and corporate performance. Kudos and best wishes to those of you corporate financial leaders who have taken ownership of or are planning an initiative to drive analytics throughout your businesses to support operational decision-making.Tags: Stratum
Categorised in: Intelligent Analytics
This post was written by Pat Passett