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We explore this question from two angles:

• Assuming collection and centralization, what approaches can be used to unlock value in large disparate datasets in need of cross-validation?

• Given the sensitivity and risk inherent in amassing such data, could a digitized form of cooperative compliance present a compelling alternative?

Entire books have been written on how to manage large datasets such that the data is clean and accessible. For the sake of brevity, we will move beyond the specifics of data storage, management, indexing, joining, governance, lineage, etc. and directly into what methods can be applied to extract value.

Big data analytics is the catch all phrase for using computer algorithms against large datasets to accomplish such tasks as finding outliers, grouping associated items, predicting outcomes or screening against defined criteria. Most recently these tasks often but not always involve machine leaning, which is a subset of artificial Intelligence. It is therefore important to understand what machine learning is and how it can be used. While machine learning comes in many forms, the classic example enables you to mine for patterns in existing data by capturing into an algorithm the correlations between inputs and outputs. The resulting algorithm can then be applied against new inputs to make predictions of output.