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Neil Raden and I introduced the basic classification of decisions used here in our book, Smart (Enough) Systems , back in 2007: Strategic, one-time one-off decisions typically made with plenty of time for analysis. Use datamining techniques to classify and categorize your customers and transactions.
Like when Oracle acquired Hyperion in March of 2007, which set of a series of acquisitions –SAP of Business Objects October, 2007 and then IBM of Cognos in November, 2007. Reeboks made it possible for aerobics classes to become main stream beyond its dancer beginnings. In BI we have had our seminal moments too.
One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and datamining.
Risk and Robustness Our estimates $widehat{beta}$ of the "true'' coefficients $beta$ of our model (1) depend on the random data we observe in experiments, and they are therefore random or uncertain. Springer New York, 2007. [8] Controlling Risks under Different Loss Functions: The Compromise Decision Problem. Kempthorne.
Master data management. Data governance. Scoring – i.e. profitability or risk. Structured, semi-structured, and unstructured data. Data pipelines. Data science skills. Technology – i.e. datamining, predictive analytics, and statistics. Best practices for exploring collected data.
Drinking tea increases diabetes by 50%, and baldness raises the cardiovascular disease risk up to 70%! Did we forget to mention the amount of sugar put in the tea or the fact that baldness and old age are related – just like cardiovascular disease risks and old age? 3) Data fishing. So, can statistics be manipulated?
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