Too Much and Not Enough Information

intermediate2 min readmanagement · business · decision science · psychology

Organizations can have too much information to assess comfortably and too little useful knowledge to guide a decision. Management slogans may offer tempting shortcuts, while collecting more data may add noise. The task is to assess evidence, analyze it competently, and connect the findings to a specific decision.

THE IDEA

In their 2006 Harvard Business Review article, Pfeffer and Sutton argued that ‘executives routinely dose their organizations with strategic snake oil: discredited nostrums, partial remedies, or untested management miracle cures’.3 A management framework can be appealing because it offers a simple explanation, recognizable success stories, and a clear next step. Its popularity does not establish whether it will work in a particular organization. That question requires evidence about the proposed change and the setting in which it will be used.

Information overload makes such shortcuts attractive. The source invokes the familiar 7 ± 2 estimate of memory capacity, but that estimate is not a universal limit on the evidence a decision-maker can evaluate with records, tools, and time. The practical problem is how to organize many potentially conflicting inputs so that they can be assessed.

Collecting more data does not necessarily solve that problem. Organizational data can be incomplete, poorly measured, or mixed with irrelevant variation. Having abundant data is like having usable farmland: it is a resource, not a guarantee of a harvest. Even competitors with similar tools may differ in the questions they ask, the quality of their records, and their ability to interpret results.

Analytical skill matters because someone must decide what a measure means, which comparisons are justified, and how much uncertainty remains. Technical methods also need to fit the decision. A sophisticated analysis of the wrong question can be less useful than a simple analysis of the relevant one.

More analysts can produce more reports and increase the volume to review. To build knowledge, an organization needs to connect new findings to previous work, record where results agree or conflict, and explain what they imply for a choice. The aim is evidence that helps people decide, rather than data collection or analysis for its own sake.

When to use it

When evaluating a new management framework, critically assess the evidence and relevance to your organization's unique context rather than adopting it because it's popular.
When faced with large volumes of data, invest in advanced analytical training for your team to transform raw data into meaningful insights.
In decision-making meetings, be aware of the tendency to rely on simple heuristics under information overload and deliberately seek out contradictory evidence.
When benchmarking against competitors, focus on developing proprietary knowledge creation capabilities rather than copying data collection methods.

FROM THEORY TO EVERYDAY LIFE

In practice

Adopting Agile Management Without Evidence

A company eagerly adopts agile management frameworks because they are trendy and promise fast results, ignoring that the specific context and lack of rigorous evidence may lead to wasted resources and unmet expectations.

Data-Rich but Knowledge-Poor Firm

A hypothetical retail chain collects detailed customer data but produces reports that do not answer its operating questions. Better measures, appropriate analysis, and knowledge of the business may help it use the data; simply collecting more records would not address the problem.

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A MOMENT TO REFLECT

Put it to work.

What management fads is your organization currently following, and what evidence supports their effectiveness?
How does your organization ensure that data analysis leads to actionable insights rather than just more data?
What steps can you take to improve knowledge creation know-how within your team or department?