Data Mining

On Wednesday May 12 celebrated the 10th Forum of Business Intelligence, which has been Dataprix Media Partner and I had the opportunity to attend.

The event was quite interesting, and it was a good opportunity to learn first-hand opinions and impressions of deployments responsible for business intelligence projects.

I find it very appropriate that most of the interventions were responsible for the area of IT or business data warehouse, as the vision and how to communicate to the person who runs a project internally is often the most realistic and that can best explain the needs, problems and what actually can be considered at the level of business success or failure of a technology project.

Pervasive Business Intelligence could be translated as Omnipresent Business Intelligence, at least at the enterprise level. This is to make the BI system to reach all levels of the organization, at the right time, and the information needed for each. You can even include integration with other systems operating normally, and also provide interaction capabilities, not just access to information.

TDWI have done a study on the extent of introduction of the BI tools in business. Used as a basis a survey of over 700 people involved in BI projects and in-depth interviews with over 20 vendors and professional world of BI.

The results of this study is the report Pervasive Business Intelligence, Techniques and Technologies to Deploy BI on an Enterprise Scale, which way to summarize what I find most relevant:

Poll Results  

Adoption of BI tools and level of use

• Adoption based on roles

Others who lead the adoption of both ratios (allocation of licenses) and use of BI tools are the business analysts, followed by managers and executives.

n the post in our forum CRISP-DM to Spanish Translation Daniel Alejandro attached a document with a Castilian translation of the CRISP-DM methodology for developing data mining models.

In electronic publishing MCData.ti can be found a fairly complete classification of different tools related to business intelligence and data management. 
This is the description that makes the tools of Data Mining

In the article Data Mining: torturing the data until they confess Luis Carlos Molina provides a very illuminating on data mining, including examples of interesting applications of it.

Article including the abstract and index, drawn from the same publication:

Summary: The title of this article is an informal explanation of the activity that makes a technology called data mining (data mining). The purpose of this technology is to discover hidden knowledge from large volumes of data. Over the past decade, due to large computational advances, has been incorporated to the organizations to become an essential support when making decisions. Organizations such as corporations, professional sports clubs, universities and governments, among others, make use of this technology as an aid in making their decisions. Some of these examples will be cited in this paper.

CONTENTS
1. Introduction
2. Data mining: concepts and history
3. Applications
    3.1. In government
    3.2. In the
    3.3. In college
    3.4. In space research
    3.5. In sports clubs
4. Extensions of data mining
    4.1. Web mining