Analytics

Analytics is the "science of analysis" i.e., dividing a large set of data into certain "themes" and understanding various relationships between these to make out some business sense and take it for strategic advantage or for efficient operations is the field of Analytics.

In olden days, data that used to be stored in the computers is predominantly "Human generated transactional data" i.e., when an transaction happens between a producer and consumer, the data related to such transactions was stored in the software systems. This is relatively small amount of data.

As the days progressed, various "machine generated events" like a customer's website views, different clicks, data from automated sensors (like RFID etc., ) and various system log events are being stored for analyzing the behaviors.

This post is to enumerate different mathematical models and their uses in the field of business and web analytics.

1. Descriptive models: Used to classify the data into different groups. For example deriving the age of a person based on the first driving license date. Determining the sex based on height and weight etc., Focus is on as many variables as possible.

2. Predictive models: Used to find the causal relationships between the themes of data. Focus is on specific variables. These models give a probability of a set of outcomes.

3. Optimization/decision models: Used to derive the definite impact of certain decision and optimize the result within a set of constraints based on the data.

PMML = predictive model markup language from dmg is the xml based standard that can be used to exchange the models across multiple supporting applications.

The trend is in-database analytics that brings the data analytics into the database core engine and databases that are specifically built for the purpose of analytics based on columnar storage that makes the database an "analytical database" instead of a mere data storage and retrieval engine.

Oracle has published a good reference paper on this subject that can be found here -Predictive Analytics: Bringing tools to data.

over and above the thematic analysis there is an increasing demand for spacial and temporal analysis of the data. The field of analytics will converge into a single set of tools where one can analyse the data using the slicing and dicing functionality on all the dimensions of themes, spacial characteristics and temporal analysis at the same time with loads and loads of machine generated data is not far in the future....

Recently, I came across this paper that presents a framework for thematic, spacial and temporal analytics that can be possibly combined with data mining option....