Declarative versus imperative paradigms in games AI by Combs N.

By Combs N.

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Ly/sloan-big-data. Aspirational Experienced Transformed Use analytics to... info | 15 Compared to aspirational organizations, transformed organizations were: • • • • Four times more likely to capture information very well Nine times more likely to aggregate information very well Eight times more likely to analyze information very well Ten times more likely to disseminate information and insights very well • 63% more likely to use a centralized analytics unit as the pri‐ mary source of analytics (analytics organizational structures are covered in Chapter 4) Again, there is a complicated tangle of cause and effect and biases here, but there is an association between competitive advantage, rel‐ ative to industry peers, and analytics sophistication.

Info | 43 Velocity How much data you need to process per unit time. Imagine sampling Twitter data during a presidential debate to provide current sentiment. You have to not only process a huge amount of information, but do so at a rapid clip to be able to provide some real-time sense of how the nation is feeling about the remarks during the debate. Large-scale, real-time processing is complex and costly. ) Even organizations that collect a huge amount—Facebook, Google, and yes, the NSA, too—didn’t make it happen overnight.

This chapter focuses on the ways that we know that data is reliable, and all the ways that it can be unreliable. I’ll first cover the facets of data quality—all the attributes that clean data has. After, I will delve into the myriad ways that data can go bad. That latter section is rela‐ tively detailed for a couple of reasons. First, because there are numerous ways data quality can be impaired. These different ways are not theoretical. If you’ve worked with data for a while, you will have encountered many, if not most, of them.

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