by Dave Wells
Everyone realizes that we live and work in a different world today than that of just one year ago. The year 2008 was a turning point in business and in society. Confidence is down, uncertainty abounds and uncomfortable words – turbulence, recession, bailout, etc. – dominate the news. The economy is the driving force, but it is not the only change. Customer behaviors are different, employee expectations have changed, corporate strategies are fluid and futures are uncertain. When the recovery occurs, it is unlikely that we'll simply return to business as usual.
So much has changed in the business world, yet strangely one thing remains the same. Many of today's business managers rely on the same analytics – the same metrics, scorecards and dashboards – as they used in the past. I find this puzzling because the relevance, usefulness and value of yesterday's analytics have diminished severely. Is analytic inertia – doing it the way we've always done it – part of the problem? Does anyone really believe that the analytics and actions of the past will be good enough for the coming months? Are we simply doing what is comfortable and familiar? Or are we stuck in analytic inertia because we don't know what we need to do differently?
I believe that inertia is much of the problem and that it results from uncertainty. We do nothing. We remain inert because we don't know what to do. To overcome analytic inertia we must:
- Realize that decision making has different implications in today's business climate than that of a year ago.
- Understand how and why today's business questions are radically different than those of the past.
- Recognize that having different questions implies getting different answers – the metrics change too.
- Use new analytic processes that are a good fit for the new kinds of questions that need to be answered.
- Find the right technologies to enable new analytic processes.
The Decisions
Remember when the difference between a good decision and an adequate decision was simply how much growth would occur? In an expanding economy, it was easy to look good even with mediocre business decisions. Boom times! Upward trends everywhere! What a lot of fun! And the few who promoted principles of decision process quality were often viewed as contrarians, academics or simply out of touch with reality.
But events of the past year remind us all that unchecked expansion can't continue endlessly. The near-guarantee of growth has disappeared, to be replaced by a new reality – decision quality matters. Forget about bouncing back from bad decisions; they will put you out of business. Also gone is the idea that you can squeeze enough growth from mediocre decisions to declare yourself a winner. Mediocrity is just a slower path to going out of business. Even good decisions may not be good enough; they buy time, but not certainty. In these difficult times you'll need some great decisions to restore confidence, conquer uncertainty and ensure the survival of your business.
Great decisions – it sounds like setting the bar at an exceptionally high level. Maybe so, but today's businesses need great leaders who can make great decisions. Those leaders will be characterized by two things – understanding the nature of uncertainty, and asking the right questions.
The Questions
It is almost cliché to say that these are uncertain times. But look a bit deeper than the superficial meaning and you'll find something significant. Uncertainty is at the core of current economic events. Uncertainty is paralyzing. With it comes indecision, lack of confidence and failure to take action.
Simply defined, uncertainty is all of the things that you don't know. In the myriad of unknown things, some are more important than others. Some are critical to the decision processes while others are trivial. Critical uncertainty is all of the things that you don't know and that you need to know to arrive at high-quality decisions. Critical uncertainty is the key to asking the right questions, and asking the right questions is the key to analytic value.
Let's compare questions – past and present. In the growth economy the questions were, not surprisingly, growth-oriented: What happened? How much of it happened? Why or what can I do to make it happen more? Monitoring and managing the good stuff is fun and invigorating.
Monitoring and managing in hard times is not nearly as much fun. It is hard work; it is sometimes discouraging; and the rewards are less frequent and less visible. Even the questions lack the optimistic tone of growth-era questions. Now we need to answer questions such as: What's the worst that can happen? What is the best that we can hope for? What do we really expect to happen? Why do things happen? And the mother of all business questions – the one that embodies uncertainty: What if ...?
The Metrics
When the questions change, obviously the nature of the answers is also subject to change. In business analytics, answers are usually presented as metrics. Consider a simple scorecard for an example of how metrics might change to answer the pressing questions of these challenging times.
A typical scorecard presents a list of performance indicators together with a small number of attributes for each indicator: the actual value, the target value, variance between target and actual, and the up/down trend of the actual value. The numbers presented in the scorecard do a pretty good job of answering the questions "what happened" and "how much." When a particularly unfavorable or favorable reading is found, the question of "why" is a subject of deeper analysis using tools such as OLAP. Or perhaps it is a subject of conjecture that is answered by gut-feel, as suggested at CIO.com in an article titled To Hell with Business Intelligence.
In this new and more difficult business climate, the questions are harder and the metrics need to be more robust. Four simple elements – actual, target, variance, and trend – no longer do the job. By expanding the metric to eight elements,
- Actual value
- Directional trend of the value
- Expected value
- Variance from expected value
- Worst-case value
- Variance from (proximity to) worst-case value
- Best-case value
- Variance from (proximity to) best-case value
This is progress; now we're moving in the right direction – richer metrics providing deeper answers to harder questions. But the big questions that are the core of critical uncertainty still remain: Why do things happen? What if ...?
The Analytic Processes
The most common analytic processes – those found in most business intelligence (BI) programs – use a sequence of goal-setting, measurement and monitoring. The monitoring activity supports a rudimentary feedback system where the essence of the feedback is "on target" or "not on target." This process is sufficient to manage in a growth economy where the questions center on what and how much.
Today's harder questions – why and what if – demand a more advanced analytic process. We need to shift from a process of goals-measures-monitoring to one of modeling-simulation-feedback.
Modeling supports the need to know why things happen. We need to understand cause and effect – to understand the influences of things upon other things. Knowing why means knowing what levers can be pulled to effect change. An advanced analytic process needs to include cause-and-effect models that capture the knowledge of influences and the complexities of why things happen. Causal loop diagramming from the systems-thinking community is a good fit here. You can read more about causal modeling in the articles A Systems View of Business Analytics, Part I – Introduction to Systems Thinking and A Systems View of Business Analytics, Part 2 – Recurring Patterns in Systems.
Simulation goes beyond why to answer the what-if questions. To simulate requires more detailed causal models than those described above. Simulation seeks not only to understand influences, but to quantify them. We need to know the strength of each influence upon the things being influenced, and we need to know the immediacy or latency of each influence. The systems-thinking model that supports simulation is known as stock-and-flow. Figure 1 illustrates a simulation model for a manufacturing scenario where the problem domain is workforce management. The modeling technique is explained in A Systems View of Business Analytics, Part 3 – Making Cause and Effect Measurable.
Stock-and-flow models prepare you to answer those what-if questions by addressing six principles of simulation:
- You know and understand the problem domain.
- You want to influence the behavior of the system, or know how it will respond to external influences.
- You understand the dynamics of the system – the influences and timing – within the problem domain.
- You know which system variables are receptive to external influences.
- You can assign equations to relationships between system variables.
- Behavior-over-time graphs will tell you what you need to know.
The Technology
Simulation is clearly something that requires software tools. You can't build and apply stock-and-flow models with pencil and paper. The technology for this kind of simulation is found in the systems-thinking community. I am aware of two software products that fit the need. While neither of these vendors positions their products as business intelligence tools, they clearly have a role in business analytics:
- iThink (www.iseesystems.com) – Modeling to simulate business processes and scenarios.
- Vensim (www.vensim.com) – Simulation software for developing, analyzing, and packaging high-quality dynamic feedback models.
Final Thoughts
Simulation doesn't replace more mainstream analytic technologies – it complements them. But it may be among the most important of analytic technologies. Some knowledge of what the future holds will certainly help to apply mainstream analytics in the right places and in the right ways. More importantly, answering questions about why and what-if will certainly help to navigate in today's business and economic conditions.
This article was originally published on the BeyeNETWORK on March 10, 2009. Reprinted with permission.
Dave Wells
Dave is a consultant, mentor and teacher in the field of business intelligence (BI). He brings to every consulting endeavor a unique and balanced perspective about the relationships of business and technology. This perspective – refined through a career of more than 35 years that encompassed both business and technical roles – helps to align business and information technology in the most effective ways.
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