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Human Obsession with Prediction - Coggle Diagram
Human Obsession with Prediction
Assumption
Although the advent of ephemeral streams of big data and cheap computing are making it
possible to anticipate things that firms care about, most firms struggle to harness them.
Humans’ unwavering obsession with prediction and always obsessed over predicting the future.
Good Assumption
Sound Data
Data is raw facts, which become information when they are sorted, condensed, and contextualized.
But not all data is worth managing. Not all information it produces is competitively valuable.
Insight, or knowledge, is actionable information.
For example, 24, 32, and 28 are data of temperatures for tomorrow which makes them information. Deciding what you will need to wear tomorrow is insight that you can act on.
Collecting and managing data is the IT unit’s job
This is where the non-IT manager must help your IT colleagues figure out what types of information are competitively valuable to your line function and how fast you need it.
Sound Model
Software apps transform data into information then into business insight.
So, turning raw data into actionable insight must begin with questions
As a non-IT manager, we can help identify 1) what business outcomes are competitively important to predict, within what time constraints, and 2) which model we would use to predict them.
We can begin with just one business outcome that we believe could differentiate we from our archrivals
Predictions are only as good as the managerial insight in the underlying models.
The underlying models/reasoning is based on functional domain knowledge, which can come only from non-IT managers.
For example, classifying a customer in a high-maintenance group requires a marketing manager’s insight into what predicts membership in that group (e.g., purchase frequency, average order size, and returns history).
Models in other functional areas require that line function’s insight (e.g., fulfillment timeliness from operations, account delinquencies from accounting, demand-and-supply models from operations staff, and product pricing from senior management).