Top 10 Ways to Make a Bad Decision

I'm usually a positive thinker, but I've observed that many leaders have an easier time committing to real change when there is a clear disadvantage to the status quo.  In that spirit, here's a quick Top 10 covering sure-fire ways to make a poor decision in your organization.  Have you seen others?  Share your comments and stories about what you have experienced.

10.  Make a decision based on money and time you've already spent.

9.    Play up information that confirms your current point of view.

8.    Ignore information that doesn't.

7.    Pay too much attention to the first thing you hear, or the first data you receive.

6.    Frame a decision only on the benefits OR risks, but not both.

5.    Wear rose-colored glasses when you are estimating the results.

4.    Wear doom-colored glasses when you are estimating the results.

3.    Believe that your "gut" is the smartest person in the room.  Corollary: Justify every decision with a quote by Malcolm Gladwell.

2.   Use nothing but data.

1.   Don't use data at all.

Best of the Blog: Decision Making Traps

In reviewing the most recent Google Analytics info on this blog, I've been struck by the response to our posts on HBR's classic article on decision-making traps, which appeared some time ago. Based on the popular demand, I have posted below the links to each of the entries.

Part I: The Anchoring Trap

Part II: The Status Quo Trap

Part III: The Sunk Cost Trap (my personal favorite)

Part IV: The Confirming Evidence Trap

Part V: The Framing Trap

Part VI: The Estimating and Forecasting Trap

We're glad so many enjoyed these posts.

Dangers of Scores in Decision Making -- From James Taylor

James Taylor, who we've referenced on this blog on several occasions, shared his thoughts today on the dangers of scores in decision making. He provides a great example: the Body Mass Index (BMI), which is fraught with issues but is nonetheless used inappropriately by companies and health officials alike.

I've seen the misuse of scores or other numbers in some of the organizations served by Big Sky. When a model is developed that produces a number or a score -- such as with Analytic Hierarchy Process, Statistics that measure strength of relationships, or even simple weighted averages -- there are many who focus too narrowly on the number without context, or fail to realize its limitations. For example, we will often help clients use quantitative methods to prioritize budgets, but the numbers that "pop out" are really nothing more than measures of preference; they are not truth. In many cases, they aren't useful measures outside of the model, and can't even be compared to previous analyses using the same method.

Scores must be balanced with other factors -- which James correctly points out can be codified in rules and often automated. For those decisions that can't be automated because they are inherently more political or are subject to other unpredictable factors, the limitations of "scores" should be carefully and frequently communicated to all stakeholders to avoid confusion or misuse.

Thanks to James for a great post.

Decision Traps and Ponzi Schemes

We have received great feedback from our readers on our series on decision traps, and they are also among our most read articles on this blog.  I ran accross an article today that discusses the real-world consequences of those traps: the $50B Ponzi scheme orchestrated by the former head of the NASDAQ.  For those of you who think that some people are too smart to fall into those traps, read this great analysis by Ken Hoffberg.   

To paraphrase Ken, the scheme demonstrates at least two of the traps covered in this blog:

• The Confirming Evidence Trap
• The Anchoring Trap

Read for yourself, and assess where you might be falling into decision traps. 

Using Visuals to Make Decisions: Excel 2007

We've previously posted about the importance of using visuals to make decisions. I ran across an excellent post by Jon Peltier at PTS blog that provides a very useful overview of the new charting functions in excel. For those of us who use excel frequently to manage and present data in support of decision-making, a solid understanding of the new 2007 features are a must-have. Thanks, Jon.

Why Optimizing Decisions is the Most Important Thing You Can Do, Part III

In our last post in this series, we introduced a simple, four-step approach to optimize decisions that we call Decision-Centric Business Improvement. The critical decisions identified through that four-step process represent the resource-intensive turning points of every organization’s growth. These decisions are diverse; some are large: corporate acquisitions, multi-billion dollar procurements, and 5-year strategic goals. Some are smaller: choosing a commodity supplier, making a hiring decision, or choosing the functionality of a software solution. Some decisions are manual, while some are automated. Some require one person; some require groups or even multiple organizations.

The last step in Decision-Centric Business Improvement is optimizing decisions along three angles: strategic relevance, technique, and technology. When optimizing decisions, it is critical that an organization work through each of these angles to build a coherent, balanced approach to the decision in question. The figure below illustrates these three “angles” of decision making.


The Decision Strategy Angle
The first angle of effective decision making is how the decision influences advancement of the organizational strategy. To clearly understand this angle, an organization should isolate the most important strategic metrics of the organization and describe the decision in terms of those metrics. If a decision cannot be shown to have a measurable impact on strategic goals, there is little chance that the decision can be successful.

The right approach in this angle is not to develop a new strategy, but rather to understand the strategy (whether implicit or explicit) and to define a particular decision in the context of the strategy. Traditional strategic planning tools—such as SWOT analysis, multiple forces analysis, or Value Chain Analysis—may be useful in this angle but should be focused on the decision.

The Decision Technique Angle
The second angle of effective decision making is the selection and application of the right tool for the job. A carpenter wouldn’t use a sledgehammer to drive carpet tack; similarly, a good decision maker chooses the tool that is just complex enough—but no more complex—to do the job. In this angle, an organization must understand both the soft and hard aspects of the decision. Hard aspects include the required speed and frequency of a decision, as well as the number of variables involved and whether the decision requires descriptive (backward-looking) or predictive (forward-looking) results. Soft aspects invlude the level of organizational buy-in required, political consequences, human factors, and transparency requirements.

For an automated supply chain decision, an organization might choose to develop a sophisticated algorithm that completes on the fly multivariate analysis. For a one-time strategic decision at a board meeting, it might use a decision tree or a consensus building method. Hypothesis testing, analytic network process, analytic hierarchy process, real options are other approaches that might be used to aid decision making.

The Decision Technology Angle
The third angle of effective decision making is the application of appropriate technology to enable the decision. Most organizational decisions will benefit from better management and distribution of information aided by technology, but not all. Knowing if, when, and how to apply technology is the component of decision optimization least understood and most prone to error.
Good decisions result from a qualified decision maker armed with the right information, delivered at the right time in the right context.

Rather than selecting one-off technology solutions, an organization should understand their “Decision Architecture” – an architecture optimized for effective decision-making. In many cases, this architecture may be comprised of existing systems rather than expensive new ones. Effective organization and adaptation of organizational IT can transform decision-making capabilities in many organizations.

While “hard” decisions—those with many variables or high speed requirements—are the most obvious candidates for the application of technology, technology can be a critical enabler of softer decisions too. Collaboration tools, role-based access control, and innovative application of existing technology (like wikis) can be critical enablers of infrequent, collaborative decision-making. In every analysis of a critical decision, whether “hard” or “soft,” technology should be considered as a important enabler of long-term success.

Seven Steps for Defining Decision-Making Projects

I recently visited a client site to participate in the definition of a consulting project that had been broadly scoped, but that required decisions to be fleshed out in terms of specific project objectives, scope, and key performance metrics.

Most of the management group that would be affected by the project was in attendance, including representatives from logistics, operations, and marketing. They were knowledgeable individuals, well motivated, and responsible for many key decisions in their functional areas. Though eventually successful, it was a long meeting, and we had some trouble honing in to the key elements of scoping the project. To some extent, this should be expected, especially for a complex decision making problem.

Yet, as I reflected on the meeting, I realized that there were certain steps we could have followed that would have been helpful for the task at hand, improved decision quality, and lowered the complexity and time involved in concluding our discussions

1. Define decision making boundaries for the project at the very outset. What is excluded is as important if not more important than what is included inside the decision making framework.
   
2. Reduce the fuzziness in the project definition to the extent as possible through a precise definition of the roles of each constituent group involved.
3. Reduce the decision to a well defined process at the right level in the organization. I have written on this issue in an earlier blog entry.
4. Create project timelines with periodic milestones consisting of broad work structures, with specific attention paid to how the project scope agreed upon would mesh with those milestones.
   
5. Define key performance indicators that would be used to measure project success, and directly link them with final project scope and definition agreed upon by everyone.
6. Create consensus by explicitly seeking inputs for each of these steps from all the key stakeholders present at the meeting.
7. Revisit and re-iterate the steps in the order listed if stuck in discussions that seem to be stalling. Long discussions on performance indicators for instance may be driven more by not having followed the earlier steps (such as defining project boundaries or process level of analysis) rather than by a lack of understanding on what is important for measurement purposes.

While individual situations vary, following these steps will in all likelihood accelerate the project scoping process, reduce fuzziness associated with multiple constituencies focusing their attention on different levels of analysis, and create defined goals and timelines. It will also result in a decision making charter for the project that will have a greater chance for success and goal attainment.

Why Optimizing Decisions is the Most Important Thing You Can Do, Part II

In our previous post in this series, "Why optimizing decisions is the most important thing you can do," we discussed the reasons why organizations should think hard about focusing on the critical decisions in their organizations, and why the speed and quality of those decisions will determine which organizations remain competitive. Today's discussion outlines our approach to optimizing decision-making in your organization.

Organizations that want to make better decisions, faster should adopt a decision-centric approach to improving business processes and their core capabilities. This approach, which we call Decision-Centric Business Improvement, is a four-step technique that requires the identification of critical capabilities, the description of those capabilities in terms of a process, the isolation of the critical decisions in that process, and the optimization of those decisions. At a high level it looks like this:

1. Identify core capabilities. First and foremost, every organization must understand those very few things at which it must be great. Not just good or very good, but great. Many organizations know what those things are; it might be research and development, it might be sourcing, it might be hiring, or it might be project management. In the diagram above, the circle represents just such a capability.
   


2. Describe it as a process. Second, every capability should be expressed in terms of a process. Whether your process is simple or complex, all have key elements in common, including: tasks, inputs, outputs, and decisions. After you know which capabilities are most important, they should be detailed in a business process that can be decomposed and analyzed.
   
   
3. Identify the important decisions. Third, the organization should highlight and understand the decisions in the processes. Most processes--and all important processes--contain decisions. Those decisions vary in complexity, importance, and frequency, but they are the turning points of every process. In the diagram above, the decisions are indicated as diamonds.
   
   
4. Optimize the critical decisions. Once it's clear where the decisions are in your most important processes, proceed with optimizing the most important decisions (the ones with the biggest impact on the process outcomes). Big Sky advocates evaluating each of these decisions from three angles: strategic relevance, technique, and technology. In other words, "why make the decision," "how to make the decision," and "what do we need to do to enable the decision."
   

Although the scope and duration of these engagements will vary with the size and complexity of the organization, we suggest starting small with a rapid diagnostic using this approach, and then layering on more challenging improvements over multiple project generations.

In our next post in this series, we will outline each of the three "decision angles" mentioned in #4 above.

Building Support for Decisions: Effective Facilitation

At Big Sky Thinking we've written quite a bit on using data in decisions and how to structure a decision-making process to increase speed and quality. For many decisions, getting the decision-makers to commit to a course of action is just as important as finding the right course of action.

Dan over at Decision Making Methodology Discussion Forum highlights how important effective facilitation can be in building support. I've seen Dan's advice work even when data is abundant; sometimes, cultural and political forces--which have surprising resilience in the face of objective data--can only be managed by effective group facilitation and well-designed change management.

You can read Dan's post here.

Why optimizing decisions is the most important thing you can do

The most important piece of advice we can give to organizations and their leadership for the next 30 years is this:

Optimizing decisions is the single most important factor in long-term organizational success. It's more important than strategy, organization design, quality, customer relationship management, innovation, or any other business model, technique, or practice.

That statement is provocative, but it's the reason why we started a company. It also begs the question, "What has changed to make optimized decisions so important?" This post outlines some of the reasons why; the next post will discuss ideas on what to do about it. The reasons are far too many to list here, but below are my views of the key interdependent factors.

1) Business model innovation. Innovations in business models--the underlying mechanisms that define the way organizations operate to provide goods and services--have been changing at breakneck speed in the last 15 years, and there is no reason to expect a coming period of stabilization. Organizations that are successful don't adopt a model and stick with it; they are hyper-adaptive to new business model opportunities when they emerge. The number of choices in business models and the resulting consequences are rapidly multiplying.

2) Intensifying expectations for regulatory compliance. Companies and governments entered a new era after 9/11 and the Enron scandal marked by a dramatic intensification of oversight by shareholders, regulatory authorities, Congress, OMB, and others. Not only is there pressure to make critical decisions quickly and accurately, but organizations must explain to overseers why the decisions were made. This new emphasis on transparency of decision-making is not supported by 20th century decision-making processes.

3) Compressing decision cycles. As business models shift and information becomes more accessible and available, organizations are faced with compressing decision cycles, particularly in critical capability processes. They have less time to choose options, and more options to choose from. In any decision, data must be aggregated, criteria established, options considered, and decisions made. Organizations have less and less time to pass each gate.

4) Advancing decision automation. Advancements in artificial intelligence compound the severity of the decision making problem. More and more decisions may be automated every year, placing additional pressure on manual decisions to either be expedited or automated themselves. Critical decisions will either be severe time traps in critical processes, or the source of substantial competitive advantage. Ignore this technology at your peril--over the next ten years the ability of software to solve complex, unstructured problems will revolutionize what organizations define as their core capabilities. James Taylor writes the best blog out there on decision automation.

5) Accelerating acceleration. As everyone knows, the innovation in technology, business, and life is accelerating. This is well documented---Moore's Law and studies of technology adoption curves are just two good pieces of evidence. However, what places so much more pressure on decision cycles is that the rate of change is also accelerating. Why? Because enablers of innovation are themselves undergoing rapid, logarithmic change. Ray Kurzweil and Alvin and Heidi Toffler have done some great writing on this phenomenon.

These are just five thoughts on my list. . . it's certainly not exhaustive. Our next post will focus on how we view solutions to the challenge--specifically, decision-centric capability development.