Mary C. Driscoll
Senior Research Fellow
Published by (www.proformative.com/blogs/mary-driscoll/2014/01/07/cfos-need-master-predictive-analytics?utm_source=digest&utm_medium=email&utm_content=link&utm_campaign=digest_email
January 6, 2014
Does this sound familiar?
CFO to CEO: “We closed out the quarter and verified that the numbers were right. But we don’t know what caused plan short-falls in two out of six units.”
CEO to CFO: “Tell those two managers that if this happens again, they’re out the door.”
There’s got to be a better way to address problems in plan execution. Threatening to fire managers is not the answer, usually. Instead, the CEO and CFO must genuinely support a cultural shift that prompts managers to ask questions along these lines:
“What can cause revenue or earnings shortfalls? How can we see them coming earlier?
What decisions happen daily at the operating level and can, in the aggregate, disrupt or enhance the trajectory we need to hit our financial targets?
We have learned that if X happens, the impact will be Y. And we know the most likely magnitude of that impact under various scenarios. So, what can we do to influence the way X and Y correlate?
How can we minimize risk or amplify benefit based on our knowledge of the way X and Y correlate?
Big Data vs. Predictive Analytics
In this day and age, a company has to know—and not guess at—the ways in which non-financial factors impact financial results. And business leaders need finance teams, with their vast and natural analytical skills, to help them compete on facts and develop highly-educated views on cause- and-effect scenarios. For example, “if we bundle products and services in various ways and model various pricing options, what can we reasonably expect in terms of (a) customer retention (b) increased revenue, and (c) impact on margin targets?”
Feeling a sense of urgency, a lot of people are now throwing around terms such as Big Data and predictive business analytics (PBA). It’s a good bet, though, that there will Big Disasters as companies start to wield the tools and techniques now available to process large and complex data sets. Big Data is one thing, but using PBA to make faster, smarter decisions is a different kettle of fish.
There is a fabulous new book out that I recommend to anyone interested in these developments. “Predictive Business Analytics—Forward Looking Capabilities to Improve Business Performance,” (John Wiley & Sons, Inc.,) was co-authored by Lawrence S. Maisel and Gary Cokins, both long-time thought leaders in the realm of corporate planning and financial management. (The book just came out and is available on Amazon.) In my opinion, it’s mandatory reading for financial planning and analyses professionals, cost accountants, and anyone else involved in resource allocation, business decision-support, and performance management.
Maisel and Cokins point out that many organizations have developed specific applications or practices such as forecasting, modelling, and contingent planning to answer their analytical needs. They warn, however, that these tools can vary in terms of their usefulness, relevance, and responsiveness. The authors argue that what’s truly needed is “PBA that is rooted in a structured, continuous, and data-driven process that enables an organization to select actions…” PBA’s purpose is to identify how the future might look and what subsequent actions are needed. “PBA is forward-looking by nature,” they explain, “oriented to an organization’s enterprise level and based on analysis of relevant business data and drivers that have strong and traceable links to financial results and operational performance.” They also note that drivers can be both external and internal.
This book is far more than a primer for those folks just now wading into the waters of business analytics. Chapter 4 is devoted to developing a predictive business analytics function, with deep coverage of:
• Selecting a desired target state
• Adopting and developing a PBA framework
• Process design
• Model development
• Data capture
• and more.
Subsequent chapters cover crucial concerns such as how to integrate performance management with analytics; integrating business methods and techniques, defining KPIs, the pros and cons of various implementation approaches, change management, education, and training. There is a vital discussion in Chapter 10 on predictive accounting and marginal expense analytics.
These dense topics are handled with an engaging writing style, and the authors provide tons of practical examples, cases, and illustrations to present a clear picture of the competencies and skills needed to succeed in this new era. Professor Tom Davenport heralded the dawn of this new era over 5 years ago in his best-seller “Competing on Analytics.” This new book from Maisel and Cokins builds on that and a wide assortment of academic research as well as practical consulting engagements. It also summarizes a set of guiding principles published by the International Federation of Accountants and Lawrence S. Maisel in 2011.
If one of your New Year’s resolutions is to be able to hold your own in discussions of predictive business analytics and the role finance can play, here’s an authoritative knowledge booster.
—- Mary Driscoll is senior research fellow for financial management at APQC, www.apqc.org a non-profit business research firm based in Houston.