The Data Talent Trap - Why Your Best Analysts Are Quiet Quitting
Your analysts aren’t lazy, they’re underused. Learn why top data talent is quietly disengaging, and what you can do to turn reporting roles into strategic engines.
Business leaders face a complex landscape filled with hard-to-predict threats and opportunities. Volatility, fragility, and ambiguity abound. This mandates more sophisticated approaches to strategic decision-making under uncertainty.
In the past, leaders made choices relying on straightforward predictions, like estimating steady 5% growth each year. But when unexpected events occurred, reality diverged drastically from predictions. For example, a company might reject the uncertainty of a shifting market landscape, confidently projecting stable demand. Yet disruptive new competitors and technologies then emerge, upending their forecasts and plans.
Organizations need to accept uncertainty as normal when making decisions today. Rather than rejecting uncertainty or failing to account for it in singular predictions, they should incorporate potential variability into their planning.
Uncertainty refers to difficulty in assigning probabilities to future outcomes. It is distinct from risk, which assumes event likelihoods are quantifiable. For example, risk would be estimating a 60% chance of rain based on weather data. Uncertainty would be difficulty predicting weather far in the future due to climate change.
There is more uncertainty today than ever before because of:
Old ways of making decisions don't work as well anymore. In the past, leaders made choices based on experience and single predictions of the future. But the world is changing faster than ever before. Unexpected events are happening more often. This makes the old approaches unreliable.
Leaders need new tools to make good decisions when the future is unclear. Two useful frameworks are Scenario Planning and Probabilistic Thinking. Using these can help organizations succeed even when things are unpredictable.
Scenario planning is a technique for decision-making under uncertainty. It evaluates a range of plausible future scenarios and assesses options under each.
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