For the past several years Ventana Research has focused more on analytics and their importance to improving business performance. We’ve done extensive benchmark research in business analytics, detailing how they are used generally in business and in major functional areas of companies as well as their application in specific industries. We adopted this focus because technology advances are changing the landscape of analytics. Its use in business management, for example, is getting new scrutiny these days because of three important changes in information technology.
One change is the increasing wealth of data that companies can use. It’s not just the data now available in the cloud. Over the past decades, organizations have implemented a range of systems for managing core business processes and collecting the data that go with these processes. ERP and CRM systems were among the first, but especially in larger companies, just about every function and every department uses some system that collects data. Almost all of these systems store this information in ways that make it feasible to access it. Second, so-called big data is making it possible for organizations to process much larger data sets than ever before to gain intelligence and insight into business operations and markets. Third, in-memory data processing is enabling companies to get immediate answers to queries, even through complex analyses of very large data sets, rather than having to wait minutes, hours or days. This accessibility changes the dynamics of planning and review meetings, for one thing, because it enables a far more fluid and interactive dialog around the questions “Why did we get the results we got?” and “What should we do next?” than has been the case in the past.
Yet all of this progress shouldn’t obscure the enduring value of simple ratio analysis. This technique for understanding business performance predates even the adding machine, going back centuries. Although it is widely used in the finance function, I think most companies today underutilize ratio analysis. Our benchmark research in finance analytics shows that finance groups do a good job with basic, well-established metrics such as profit margins or days sales outstanding (DSO) as well as debt and liquidity measures. But they – and the rest of the organization – do less well in monitoring and reviewing ratios that combine financial and nonfinancial data, especially where these involve key performance measures. Ratio analysis can help here.
It is particularly useful for assessing the efficiency of processes and the effectiveness of results, and at its core, business is about transmuting inputs into outputs, such as pounds or kilos of steel or direct labor hour per completed product unit. Indirect cost efficiency also can be measured as a ratio, such as the number of full-time equivalents (FTEs) employed per 1,000 invoices processed. Effectiveness can be measured by, for instance, the percentage of repeat customers, manufacturing defects per 100 units or, in customer support, the percentage of first-call resolutions.
Finance departments tend to focus on financial ratios and overlook operational ones, which may be viewed only by that specific part of the business. Thus, a periodic assessment of the profitability of a particular retail store may only include revenue and costs. However, without looking at the gross profit per sales employee and/or the average revenue per sales employee, it’s difficult to distinguish between the direct and indirect factors that are determining branch profitability.
Because they measure the relationship between inputs and results, ratios are especially useful as quantitative performance metrics. Potentially, there are thousands of these ratios that a company can use for setting objectives, monitoring results and assessing performance. However, it’s important to focus on the “key” performance ratios – those that have the greatest impact on the results of individuals, business units and the company as a whole. Companies can have a difficult time identifying their key factors. This is where driver-based modeling and planning come in because the process of creating these models sorts out the important from the marginal measures.
The use of advanced analytics is growing in importance as technology provides companies another way to achieve an edge on their competitors. At the same time, it’s critical that executives and managers build on the basics. If an organization cannot formulate the most important ratios that define business performance, and if it cannot readily access the data needed to perform this simple division, it’s unlikely to be able to handle large sets of data effectively and benefit from more advanced analytic techniques. Instead it is likely to wind up experiencing the “big garbage in, big garbage out” syndrome.
Robert Kugel – SVP Research