In 2011, IFAC issued International Good Practice Guidance, Predictive Business Analytics: Improving Business Performance with Forward-Looking Measures. The guidance helps accountants and other finance professionals embrace predictive business analytics (PBA), which helps ensure that the strategic and operational decision needs of internal business users are supported by relevant management information and analysis.
The role of professional accountants in assisting their organizations in making the most of PBA to improve strategy and performance management enterprise wide is more important than ever. Organizations are drowning in data but starving for information. In speaking with the New York Times in 2009, Erik Brynjolfsson, an economist and director of the Massachusetts Institute of Technology’s Center for Digital Business, put it this way: “We’re rapidly entering a world where everything can be monitored and measured. But the big problem is going to be the ability of humans to use, analyze, and make sense of data.”
Utilizing analytics has huge relevance in all walks of life, not the least in professional sports. The baseball book and movie Moneyball: The Art of Winning an Unfair Game highlighted the use of statistical analysis to maximize results for the US Oakland Athletics baseball team. The book explains how the Athletics’ general manager, Billy Beane, “seized upon a system of thought to make what is an inherently uncertain judgment, the future performance of a baseball player, a little less uncertain.”
In business, the application of analytics is vast—including increasing customer profitability, improving employee retention, and driving greater operational efficiencies. But PBA is about more than analytics and statistics. It is about ensuring a clearer line of sight throughout the organization and better aligning what we do as executives, managers, and operational staff with strategic goals.
Collecting, sorting, and analyzing data can only be part of the picture. Professional accountants add value by being able to identify the goals and the decisions that matter most to an organization. Defining the key questions dictates the type of analysis and source data required.
Since the IFAC guidance was published, the lexicon in the area of analytics and business intelligence has unsurprisingly grown further.
Big data, a relatively new buzzword referring to our ability to collect and analyze the vast amounts of data we are now generating in various ways, relates to PBA. In a recent survey by the Association of Chartered Certified Accountants and the Institute of Management Accountants, 62% of respondents cited big data as hugely important to the future of business, potentially giving savvy businesses an edge on their competitors. But big data relies on building capability in an organization to improve decisions about strategy and its execution. This capability helps an organization take a structured and systematic approach to anticipating future events, forecasting possible outcomes, solving small problems before they become large ones, and pursuing actions and decisions that improve performance.
Therefore, PBA should not be seen as a separate discipline performed by a few skilled professionals. It can be embedded into existing management and accounting tools, such as strategy maps (visual representations of an organization’s strategy), scorecards, managerial costing, and rolling forecasts, to help provide useful analysis and insights.
PBA also involves implementing business intelligence technologies and data mining to facilitate data insights and accessibility. And it requires using trends and statistical analysis, such as correlation, segmentation, clustering, and regression analysis.
Following the principles of PBA set out in the IFAC guidance helps provide the basis for analysis that is essential to improving decision making and management control. This includes changing the business strategy, and aligning activities and performance with strategic and operational objectives by measuring the right things in the most effective ways.
The principles ensure that foundational concepts support efforts to manage performance, such as the need to demonstrate strong cause-and-effect relationships. Ensuring alignment between strategy and operations requires using measures and key performance indicators (KPIs) that are relevant to understanding performance and value drivers. To successfully predict outcomes, it is important to understand the cause-and-effect relationship between events (their drivers). The key question is, “If X happens, what Y will happen as a result?” In the development of medicines, for example, the number of clinical data errors will slow down the clinical trial process and, hence, lengthen research cycle time, ultimately delaying product launch and anticipated revenues.
Effective performance management also requires using a blend of historical as well as forward-looking financial and non-financial data. The use of leading, or driver-based, measures that reflect processes and activities, such as time spent with customers, are predictive. Lagging measures focus on stating the performance results at the end of a time period and after the fact.
The 2011 guidance from IFAC is now supplemented by a new book, Predictive Business Analytics—Forward-Looking Capabilities to Improve Business Performance by Lawrence S. Maisel and Gary Cokins. The book leverages the seven principles set out in IFAC’s guidance with real world experiences and illustrates, through case studies and examples, how PBA integrates with several important business management and improvement methods and techniques.
In chapter six, a case study on MetLife provides an insight into a practical application of PBA. MetLife established a performance management program, called Management Operating Review, to foster an evidence-based approach to enterprise performance management. This involved developing KPIs that align MetLife’s strategies to its detailed operating performance measures. However, the development of KPIs was managed within a performance management program and framework that were designed to:
- Link strategy to operational outcomes and measures;
- Ensure a balance between lead and lag indicators across various performance dimensions, such as financial, customer, operations, and service quality;
- Encourage management review and action focused on drivers and relative performance improvements; and
- Implement rewards and incentives to influence desired behaviors.
The program used PBA as a foundation of its success using specific steps.
- Establish Policy and Charter for the Management Operating Review Program
- Develop KPIs and measures
- Establish data management
- Conduct management reviews
- Build organization and supporting resources
- Maintain communications and change management.
In terms of defining measures, the process is iterative and KPIs can be refined through their use. It took time to agree on a set of core business-specific management metrics, as well as those that are needed to manage the performance of a distinct business unit. For each measure, data sources were identified and validated, and an operational process put in place to provide routine reporting. The management metrics reporting system included the use of dashboards and management reports to ensure timely and effective communication of what selected measures reveal about performance. Monthly reviews of results by the senior leadership team led to planned actions going forward.
Executives, managers, and finance professionals can use the principles and ideas in the IFAC guidance and the book to think about how to develop a capability in PBA that will ultimately help their organizations to be more resilient and responsive. Building this capability takes far more than just collecting data. It’s the total approach to PBA that can make you successful.
