Sunday, June 21, 2015

Descriptive Vs. Predictive Vs. Prescriptive

Descriptive Analytics (EDA)
The purpose of descriptive analytics is simply to summarize and tell you what happened. For example, number of post, mentions, fans, followers, page views, kudos, +1s, check-ins, pins, etc. There are literally thousands of these metrics – it’s pointless to list them – but they are all just simple event counters. Other descriptive analytics may be results of simple arithmetic operations, such as share of voice, average response time, % index, average number of replies per post, etc.

Predictive Analytics

The purpose of predictive analytics is NOT to tell you what will happen in the future. It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature.

The essence of predictive analytics, in general, is that we use existing data to build a model. Then we use the model to predict data that doesn’t yet exist. So predictive analytics is all about using data you have to predict data that you don’t have.

Prescriptive Analytics
Presecriptinve analytics not only predicts a possible future , it predicts
multiple futures based on the decisions makes action. Therefore a prescriptive model is
by definition, also predictive. As such must be validated too.
A prescriptive model can be viewed as a combination of multiple predictive models
running in parallel one for each possible input action
The Goal of the most prescriptive analytics is to guide the decision maker so the decision he makes
will ultimately lead to target outcome

In prescriptive analytics, we also build a predictive model of data
The predictive model must have two more added component in order to be prescriptive
1. Actionable: The data consumers must be able to take action based on the predictive outcome of the model

2. Feedback System: The model must have feedback system that tracks the adjusted outomes
based on the action taken. This means the predictive model must be smart enough to learn the complex relationship between the users' action and the adjusted outcome through the feedback data

1. Descriptive Analytics: Compute descriptive statistics to summarize the data. The majority of social analytics fall in this category
2. Predictive Analytics: Build a statistical model that uses existing data to predict data that that we don’t have. Examples of predictive analytics include trend lines, influence scoring, sentiment analysis, etc.
3. Prescriptive Analytics: Build a prescriptive model that uses not only the existing data, but also the action and feedback data to guide the decision maker to a desired outcome. Because prescriptive models must be actionable and have a feedback data stream, social analytics are rarely prescriptive

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