James Guszcza, Bryan Richardson

By now there are hundreds of examples … in which analytics involving the most traditional of data sources outperform traditional modes of decision-making. …

Each case … involves “sorting” or “prioritization” decisions that (a) are central to an organization’s operations; (b) are made repeatedly, typically by experts relying on professional judgment in varying degrees; and (c) incorporate quantifiable information that is readily available, yet commonly used … [ Read more ]

James Guszcza, Bryan Richardson, Daniel Kahneman

In Thinking, Fast and Slow, the Nobel Prize-winning founder of behavioral economics Daniel Kahneman … writes of two fictitious mental processes that he calls System 1 (“thinking fast”) and System 2 (“thinking slow”). System 1 mental operations are rapid and automatic; they are biased toward belief and confirmation rather than analysis and skepticism; they tend to jump to conclusions and infer causal relations based on … [ Read more ]

James Guszcza, Bryan Richardson

Anita Woolley of Carnegie-Mellon University and her collaborators constructed a measure of collective intelligence and found that it is roughly as predictive of group performance as IQ is of individual performance. Surprisingly, collective intelligence is not explained by factors such as group satisfaction, cohesion, or motivation. Instead, the strongest predictors of collective intelligence—and group success—are equality of conversational turn-taking (measured using sociometric data) as well … [ Read more ]

The Personalized and the Personal: Socially Responsible Innovation Through Big Data

The possibility of creating data products and services fueled by fine-grained behavioral information, and informed by behavioral science and choice architecture, offers a framework for innovations that enhance rather than diminish public trust. Organizations that take such ideas on board can distinguish themselves through superior, consumer-oriented product design.

Too Big to Ignore: When Does Big Data Provide Big Value?

Much of the language surrounding big data conveys a muddled conception of what data, “big” or otherwise, means to the majority of organizations pursuing analytics strategies. Big data is shrouded in hyperbole and confusion, which can be a breeding ground for strategic errors. Big data is a big deal, but it is time to separate the signal from the noise.

A Delicate Balance: Organizational Barriers to Evidence-Based Management

For all of their promise, analytics projects are often stymied because of failures to appreciate that both data-driven analytics and expert decision-making have strengths as well as limitations; and that the strengths and limitations of each must be counterbalanced with those of the other. The image of “data mining” should give way to the image of “data dialogues”.

James Guszcza, David Steier, John Lucker, Vivekanand Gopalkrishnan, Harvey Lewis

The same body of psychological research that underpins behavioral economics also suggests that we are very poor natural statisticians. We are naturally prone to find spurious information in data where none exists, latch on to causal narratives that are unsupported by sketchy statistical evidence, ignore population base rates when estimating probabilities for individual cases, be overconfident in our judgments, and generally be “fooled by randomness.” … [ Read more ]

James Guszcza, John Lucker

Our intuitions can lead us badly astray in a way that is as surprising as it is straightforward. Kahneman identifies two types of mental processes. “Type 1” mental processes are fairly automatic, effortless and place a premium on “associative coherence.” In contrast, “Type 2” mental processes are controlled, effortful and place a premium on logical coherence. Although we fancy ourselves primarily Type 2 creatures, many … [ Read more ]

Beyond the Numbers: Analytics as a Strategic Capability

Uncovering the realities that lie behind the data is what business analytics is all about. Precisely because they are hidden to the casual observer, they lend competitive advantages to the organizations that discover and implement them in business first.

James Guszcza and John Lucker

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

Irrational Expectations

Predictive analytics is emerging as a practical discipline, one that can help business leaders make better decisions. In a world whose complexity in many respects has moved beyond our cognitive abilities, numbers are nothing to fear. In fact, they may be your greatest ally.