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I believe that if you work granularly, you can get a perception of things that isn't available from the written analysis. Data can tell you a lot. Opinions without data are severely risky.

Traditional analytical models of decision-making, while appropriate for evaluating choices for which there are ample historical data, can often kill off creative ideas that might be extremely valuable.

when creative people are performing a task, if the extrinsic reasons for engaging in the task are more important than the intrinsic reasons, they will tend to perform less creatively. Outside factors encourage people to focus purely on the accuracy and consistency of the outcome; while the task may be accomplished, creativity is blocked and interest in the activity stifled. This is not to say that creativity can be promoted by eliminating compensation for performance - the most salient extrinsic factor.

There are two things to be considered with regard to any scheme. In the first place, 'Is it good in itself?' In the second, 'Can it be easily put into practice?'

The culture of science is changing. The ultimate tool in most fields, once the powerful equation, is now the powerful computer, and this change describes a shift from the quest for powerful abstract formulas to a more open-ended willingness to plug in the data and see what happens. It is a shift from a culture of powerful explanations to a culture of powerful descriptions.

When a great question is first started, there are very few, even of the greatest minds, which suddenly and intuitively comprehend it in all of its consequences.

Note: Business 2.0 is now part of CNNmoney and some older articles are no longer available

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Fundamentally, prediction is never valued as much as explanation in academia.

Because...markets are moving so fast, data is not worthless, but it almost is. Everything is based on pattern recognition. Metaphors are organizing principles that explain a pattern. Metaphors also present a set of relationships that you can test against reality. When you find great metaphors they explain the universe. It's not in any sense trivial. It is playful.

So much of what we hear and what we're taught turns out to be false on closer scrutiny. Whether it is expert advice, what you read in the paper, or what your mother told you, if it is important, take the time to figure out for yourself whether it is really true.

The Plural of Anecdote is Not Data.

If you can explain one thing and its opposite using the same data you don't have an explanation. It takes a lot of courage to keep silent.

Cost-benefit analysis can be an effective tool to analyze simple, one-dimensional problems, such as whether to install dividers on dangerous stretches of highway, where relatively unambiguous data is in abundant supply and there is little controversy. It also is a good way to elucidate the trade-offs for a given policy or regulation, or to produce a summary statistic about its economic efficiency.

But the cost-benefit method loses its authority when it’s used to assess more complex decisions. It is inadequate for evaluations of interventions that will affect many different dimensions, such as markets, economies, health, the environment, and endangered species. Cost-benefit analysis is also inappropriate for products or processes over which there are disagreements about benefits or about which outcomes are important. And it should never be used as the basis for regulation in the presence of scientific uncertainty or value conflicts, or in an area where there are no authorities one can trust to know all the answers. Decisions like these require a more expansive methodology — one that isn’t dependent on the affectation of translating all value into economic terms, that is more transparent and responsive to outside criticism, and that pragmatically represents the interests of everyone involved: industry, government, and the public.

Over the years, I have found that most executives are smart and hardworking, and want to do the right thing. But they are terrible at critical thinking and analytical rigor — usually because they confuse the factors that lead to high performance with attributions based on that performance. They confuse drivers and results. We see this in everything from corporate culture to leadership to employee satisfaction to customer orientation.

We have to rely both on analysis and synthesis. Analysis—taking complex things and studying and understanding them—is very useful for knowing how well something is going to work and how you might improve it or make it more efficient. It’s not very good for coming up with major new ideas. There we have to be able to synthesize many competing ideas or competing insights—even if those things are in tension—into something that is somehow a whole. What designers and design thinkers are always searching for is the alternative that’s better than the initial starting points.

Businesses thrive on stability and repeatability. Stable and repeatable processes justify large-scale capital expenses; they justify large-scale employee training; and they reduce cognitive overhead because processes and decisions do not change and hence their rationale does not have to be explained repeatedly.

By contrast, an analytically based enterprise of the future will have to be designed around volatility rather than repeatability. Volatility—or rapidly changing decisions that are context- and time-sensitive—will be a big challenge for enterprises. Decisions are no longer easily explainable; capital investments cannot be based on mass repeatability but must cater to endemic volatility

There are three very distinct ways that organizations can fall into the analysis-paralysis trap. One is a managerial tendency to “over-fit the curve”—a statistical term that refers to the diminishing value of additional data once a pattern (or curve, in the graphic sense) has been found. Data collection has a price, inaction has a price and an analytically literate organization will clearly understand the cost of over-fitting.

The second cause of analysis-paralysis is waiting for data that simply does not exist, which reflects an inability to design experiments to generate the needed data. As mentioned above, experimentation has a price and inaction has a price, so an analytically literate organization will be characterized by a clear understanding of data gaps and the value of experimentation to break the logjam.

The third cause of analysis-paralysis is the fact that most companies do not know or articulate their risk tolerance clearly and are much more likely to penalize failed action than inaction. As a result, many managers do not act unless there is enough data to assure them of successful outcomes. An analytically literate organization will have a firm grasp of its risk tolerance. With guidelines and models for action under uncertainty, it will restore the symmetry between how it treats failed action and inaction.

Analytics is the science of better decision-making; and decision-making is the heart of business.

Note: Darwin Magazine is now dead. Some articles are moving to CIO. I will try to update the links when I have time...

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One of the most powerful words in the English language is why. When asked as an interrogatory, why has the power to change assumptions, preconceptions and mindsets. It has the power to initiate change as well as the power to affirm the right course. It is a word that should be used frequently but with great care. When used the proper way, it can be one of the most effective tools a leader can employ. And it's totally free.

Why is a word favored by those not satisfied with the way things are. These individuals tend to be inventors, entrepreneurs, scientists, social capitalists and politicians. By nature, they are catalysts. Inventors and entrepreneurs wonder about alternatives using why to provoke thought about what might be and try and quantify it as a product or service. Scientists use why as part of the scientific method that begins with a hypothesis and ends with proof. Engineers use why as a means of diagnosis: what happens and why. Social capitalists and politicians alike use why to question assumptions about the way organizations and governments serve their constituents. For all of these types of people, why becomes the trigger word for invoking alternatives as well as beginning the process of bringing people along to alternate points of view.

Why is a powerful tool for a leader to use but it should be used with discretion. Overuse can lead to unpleasant consequences. For example, the manager who always asks why to any request or any issue risks trivializing the power of the word itself (as well as becoming extremely annoying). Too many whys will turn people off; they will feel that they are being questioned rather than their ideas. As a result, they will cease communicating to the manager, effectively cutting him or her off from information as well as relations with the team, a disastrous situation for any manager.