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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. One of the primary sources of tension? What is Data in Use?

Testing 169
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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. Boiling all the information down to a single model does not help us challenge to what degree we think the future will differ from the past. A single model may also not shed light on the uncertainty range we actually face.

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Predicting Movie Profitability and Risk at the Pre-production Phase

Insight

The process of selecting and engineering features is laborious but crucial since the success of any model depends heavily on the quantity and quality of the input data (recall: “garbage in, garbage out!”). Below is the result of a single XGBoost model trained on 80% of the data and tested on the unseen held-out 20%.

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