Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley and SAS Business Series)

In the year 2000, an organization that had a database holding a terabyte of data was at the forefront. Today you can buy a terabyte disk drive for your computer for under $100!

advanced analytics is to quantify the cause of events, predict when they might happen again, and identify how to influence those events in the future.

The choice of priorities can drastically change the strategies and tactics the team will need to use to get there. For example, is the priority to be the biggest company in the industry? Or is it to have a solid base of the most satisfied customers? Or to have the highest profit margins? Or to have the lowest attrition rate?

There have been some improvements made recently, but R still isn’t able to scale to the level of other commercial tools and databases.

An analysis is commissioned to identify if viewing a product on the web increases the likelihood of purchase or not. It will take quite a bit of work to parse the web data and combine it with other customer data since the web data is new. A starting point can be as simple as a correlation analysis.

We’re talking about running analytics processes 40, 50, even 100 times faster than is possible with traditional approaches.

Analytic processes may require filters on the front end to remove portions of a big data stream when it first arrives. There will be other filters along the way as the data is processed. For example, when working with a web log, a rule might be to filter out up front any information on browser versions or operating systems. Such data is rarely need

How does an organization capture, analyze, and utilize this rich information to drive insight?

This provides the capability to be proactive and nudge a customer down a purchase path they have yet to complete.

There are benefits to a table-based enterprise analytic data set. First, you have a true “compute once, use many” scenario. The total system load from analytic professionals is going to be vastly reduced. This is because instead of having each person repetitiously running the same type of process to do big joins and aggregations, it’s going to be r