![]() Expanding the time horizon compounds inaccuracies in the estimate. Forecast accuracy depends on estimated initial conditions. Treating long-term forecasts with suspicionīusiness forecasts are like weather forecasts. To deal with uncertainty, leaders should: Organizations need to account for the uncertainty inherent to data-based predictions. ![]() When there is a complex set of variables, a larger sample of observations will be needed to filter the signal form the noise, because each additional variable increases the likelihood of finding a spurious correlation, leading to false positives, over or underestimations, and/or overconfident predictions. ![]() Flexible algorithms fit datasets closely. The curse of dimensionality limits the utility of complexity. More data typically allows for better performance, but more complexity can lead to diminishing returns. Understand the limits of your data (continued from previous page) Banks had credit scoring models to evaluate default risk, but the predictive value was irrelevant to the question “Which applicants will be profitable?” Fairbanks and Morris eventually acquired this data through an expensive experiment phase. When Fairbanks and Morris devised their information-based strategy, the necessary datasets didn’t exist. 11 Data acquisition represents a significant expense, either directly, or more likely and consequentially, as an opportunity cost. 10 Understand the limits of your dataĭata collected in the past is not necessarily relevant to current use cases, so it may be necessary to invest further. A competitor attempting to copy Amazon will likely be unable to acquire the same volume detailed data, rendering their recommendations dramatically less relevant. They now leverage this with recommendations to match customers with products they would be likely to purchase. The acquisition cost was selling books below cost to learn customer characteristic profiles and purchasing tastes. Amazon invested in building its data assets for years before being profitable. Historical decisions put present constraints on data monetization options. Plan around limits of historical decisions
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