In a business world grown increasingly complex by globalization, the vast proliferation of data and a need for flexible strategies, more and more organizations are seeking better processes and tools to ensure that the right people have the right information at the right time, to make smarter decisions.
But according to Larry Maisel, managing partner at consulting firm DecisionVu and a recognized leader in the field of strategy and performance management, those smarter decisions won't necessarily come from analyzing the who's and what's within data, but by applying more sophisticated tools to understand why circumstances happen and intelligently gauge what will come next.
Maisel sat down with Business Finance to discuss the emergence of Predictive Business Analytics, how it can help redefine the way organizations operate and many of the misconceptions that impede the adoption of this new paradigm.
Maisel will be joining Business Finance as a regular monthly contributor, sharing his expertise on Predictive Business Analytics, highlighting key case examples, ways in which specific industries have applied the techniques and tools and how it can complement other financial applications such as budgeting, forecasting, and performance reporting.
BF: Define for us what Predictive Business Analytics (PBA) means.
LM: Predictive Business Analytics refers to the skills, technologies, tools and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. It focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling and fact-based management as input for human decisions -- or it may drive fully automated decisions.
BF: There have been some misconceptions between PBA and other forms of business intelligence. What separates the two in your view?
LM: Business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Additionally, data mining, sometimes referred to as Predictive Analytics, focuses on consumer behaviors and patterns. Business intelligence is querying and reporting.
But to me, words, querying, reporting, OLAP and alert tools only answer questions such as what happened, how many, how often, where the problem is and what actions are needed. Predictive Business analytics can answer questions like why this is happening, what if these trends continue, what will happen next, and what is the best that can happen.
BF: You have compared the use of business analytics in driving fact-based decisions to that of the human experience. Please explain.
LM: What I was thinking of was this whole notion that in the human experience, you understand it with your brain, in your heart you believe it, and in your body, your skeleton, you actually execute it. And I think that's a little bit how predictive business analytics works, in that as there's more confidence within the organization in the insight of the cause and effect relationships -- that's the brain, if you will -- the organization will come to believe and trust -- that's the heart -- and then the actual organizational structures and operations will then begin to execute it. That's the muscles. That kind of parallels the human experience. The body will not do what the mind does not drive it to do. And the mind will only drive it if it believes it and understands it.
BF: What are some of the bottlenecks you see impeding wider PBA adoption?
LM: Well, there's a great deal of confusion between Predictive Analytics as a tool to uncover and measure consumer buying behaviors and trends and Predictive Business Analytics as an organization process to plan and manage business activities as well as improve managerial decision-making.
The other significant bottleneck that I see is some organizations are too tightly structured around functional departments. If I'm in sales and marketing, what I see is sales and marketing. I may not see inventory and I don't see manufacturing as an issue because I'm so focused on my individual function. As a result, insights from cross functional data and actions might drop between the proverbial gray space.
BF: How is PBA allowing finance and insurance companies to deal with global risk and changing regulatory and reporting?
LM: The quick answer is distinguishing between performance and risk, but having these closely linked is the key to dealing with risk and regulatory factors. A good example is the person who is told to lose weight or lower their blood pressure. Tracking what you eat is about performance. Eating the right things is the risk factor.
As it relates to business performance, if you can distinguish your events, the drivers that are performance related versus those that are risk related, then you can begin to manage and integrate how each might impact your performance -- whether these are risks that must be managed or whether they are performance issues.
BF: As PBA matures and is applied in greater numbers, will this change the way financial organizations have operated?
LM: Absolutely. I read a great quote from John Kotter, who is a professor at Harvard and has written about this. He says, "CFOs do what the job description tells them to do and it often says to focus on the numbers. This often leads to financial professionals becoming the ‘numbers person,' simply because this is what is expected of him or her -- and, thus, the stereotype."
The answer to the question is if the finance professional is kept in this little tightly wound box called the Financial Police, the Controller, and the entire job is focused on history, they will not really add true value to the organization going forward. The imperative is clear: financial professionals have to step out beyond that box, if they are to make a significant, positive impact on their company's performance and be recognized for their individual contributions.