The Data Daily

Best Practices for Analytics Profiles

Last updated: 05-14-2018

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Best Practices for Analytics Profiles

In our Big Data engagements, we talk about the importance of building detailed “profiles” of our most important entities, such as customers, products, devices, machines, employees, partners, stores, wind turbines, cars, ATMs, etc. As part of our data science process, we build a profile on each individual entity that:

1)    Captures that entity’s tendencies, propensities, patterns, trends, behaviors, relationships, associations, affiliations (plus, in the case of humans, interests and passions)

2)    Compares that entity’s current state and recent transactions, activities, and interactions to their individual profile in order to flag “unusual” activities and behaviors that might be indicative of a problem or monetization opportunity

But what do we mean by the word “profile,” and what elements might comprise a profile?

A profile is a combination of metrics, key performance indicators, scores, business rules, and analytic insights that combine to make up the tendencies, behaviors, and propensities of an individual entity (customer, device, partner, machine).

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