Better financial forecasting methods through analytics
Forecasting is key to strong financial decision making. Recent improvements to hardware and software, mobile devices and social collaboration tools now allow businesses to collect, store and analyze more data to help them create accurate forecasts. New tools also provide opportunities for better financial forecasting methods, alleviating some of the tedious work and improving financial executives' ability to predict future trends.
Effective forecasting takes time. Data from all operational areas must be analyzed to create multiple views across a number of time periods, and this process can be time-consuming. If financial systems are not integrated, inconsistent inputs and outputs of forecasted and actual figures may lead to wasted reconciliation efforts without providing insight into what the numbers mean.
Further, valuable data sources vary depending upon the type of forecast and the business's growth stage. Many kinds of forecasts, such as cash flow, personnel, sales, cost, products and pro forma, may be required. Some vital data, such as sales and marketing information, must be gathered from outside departments, and capturing that data can be tedious if it is not initially usable, reliable or accurate.
Effective financial forecasting methods that utilize data analytics tools, however, minimize repetitive inputs and tedious calculations. They allow information to be tailored for different audiences, from consolidated data and presentation-style reporting for senior managers to transactional details for line managers.
Spreadsheets can continue to be an integral part of planning, but can be complemented with interactive tools that permit changing variables and constantly updating and saving forecasts. Alternatively, analytics dashboards with graphics and key performance indicators give management a user-friendly view on a real-time basis.
How can analytics present more options for better decisions?
The struggle with forecasting may not always be obtaining data, but instead with interpreting it and creating actionable insights.
"The difficult part of performance metrics is defining the metric, linking it to strategic priorities and defining the 'use case,' how you will react to the output," Manny Korakis, chief financial officer at S&P Dow Jones Indices, explained in a recent interview with me. "The use case eliminates 'nice to have' metrics and analysis and focuses on business-critical metrics. Having those metrics available helps point to potential issues before they bubble up and gives you something to measure and manage against to determine how good your business decision was."
Organizations can use big data to gain knowledge about associations among phenomena and then use these insights to make better, timelier decisions. According to an article in the American Accounting Association's Accounting Horizons, predictive analytics are an invaluable resource, as they inform organizations about whether certain events are likely to occur, as opposed to providing an explanation of why or how things happened after the fact.
For instance, another Accounting Horizons article noted that when Wal-Mart analyzed terabytes of its transactional data, the retailer discovered that customers buy additional flashlights when a hurricane approaches, and increase their purchases of strawberry toaster pastries sevenfold. These findings helped Wal-Mart better manage its inventories. In a similar way, geographical and demographic data has the potential to reasonably predict financial revenues and sales in individual business locations.
What are the challenges of data analytics?
In order to be helpful, big data must be gathered, analyzed and transformed by someone with analytics experience. The usefulness of data is also limited by quality, quantity and accessibility.
Further, business planning that forecasts earnings and cash flows for years to come can be challenging.
"Obtaining the right competitive and market data can be difficult," Maureen Cullum, chief financial officer at Cynvenio Biosystems, explained in an interview. "Data can be too old, from different geographies or from businesses similar only in scope. This is especially true at very early stages of a business, with new products or launching existing products in a new market. Material available from large data sources like accounting firms or industry specialists can be overwhelming and hard to interpret. When preparing projections to raise capital, using such speculative data can be troublesome."
Better information enables better data analysis, which allows better business planning. Finance teams that can overcome these challenges and efficiently use analytics will be able to present more options to their executive teams to enable better business decisions. Learn how to effectively transform your organization's disconnected planning, budgeting, forecasting, reporting and analysis processes into more dynamic, efficient and connected experiences.