Data Mining Tools creates Efficiency

According to Management Information Systems, “Data mining tools are the software tools you use to query information in a data ware house”.

It is the manipulation in the databases that include query-and-reporting tools, intelligence agents, multidimensional analysis tools, and statistics tools. A lot of queries need answers at the fastest and most reliable way. Data mining tools is one popular and effective way to do it. Data mining tools are useful in forecasting trends and behaviors that allow businesses or organizations to have better information that will contribute in the decision making which leads to a competitiveness.

The concept of Data mining is used decades ago in different industries and incredible progress as the years goes on that meets the needs of the user in decision making.

Evolution of business data to business information are Data Collection (1960s), Data Access (1980s), Data Warehousing and Decision Support (1990s) and last but not the least Data Mining (emerging today). Businesses collected data from surveys, research information and analyzed information and previous transactions that are large quantities inputted in databases. As the needed information of every business arises, the evolution of business data creates a way to diversify the information into a specific detail with a high percentage of accuracy and rapid response.

The Scope of Data Mining
Thearling discussed about the new business opportunities take place because of the two capabilities of data mining:

-Automated prediction of trends and behaviors, this answers can be easily detects from the data quickly even in the large databases.

-Automated discovery of previously unknown patterns, this data mining tools classify previously hidden patterns in one step in the databases.

This is very effective in Statistical Qualitative Control to avoid having deficiencies and errors in the production area. It will give a better forecasting that will minimize cost and will make a gain to compete in competitive advantage. Using the previous data, the abnormality will be easily detected based on the unknown patterns and having this information, management or operation management will immediately analyze the cause and decide which solution or strategy will be executed to the given situation.

A Database needs a reliable and appropriate Hard disk drive, RAM (Random Access Memory), ROM (Read Only Memory) so that it will perform well. The faster it works the more chances for the users to experiment and test all the data mining tools to get the appropriate solution and easily understand complex data.

According to Gartner Group Advanced Technology Research Note, “With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage.”

The concept of data mining is known before and much more when the evolutions of technology boost in the market. The demand of the use of data mining is high and the development of this computer tools is continuous. Especially to the large business and organization, data mining tools makes the decision making easier to decide and quick to achieve the result.

There are different kinds of data mining tools available in the market and after choosing what data mining tools to use, building appropriate model is important.

Ever since building model are applied before the beginning of computers or data mining technology. Building model will help to know the answer in the certain situation using the data you have. If the formulated model is good, there is a high chance of getting the optimal answer you want to apply in the current situation.


DNA of a forensic accountant is one of example that used data mining tools in the field of science. DNA of a forensic accountant which used data mining to analyze large quantities of information that identify trends or inconsistencies, ambiguous facts in a inclusive and well-organized method using “intelligent” computer applications. Like other computer software and application tools, it aims optimization that reduces costs and maximizes profits and develops a strong customer relationship.

An Algorithm is a step by step instructions created by the users to solve a problem in a specific situation. While in the concept of data mining, algorithms are formulated to find relations and in sets of data in a database.

Excerpted from the book Building Data Mining Applications for Customer Relationship Management by Alex Berson, Stephen Smith, and Kurt Thearling.

Two sections of the most common data mining algorithms use today:

-Classical Techniques: Statistics, Neighborhoods and Clustering

These techniques are widely used since early 1980. Statistics is a mathematical approach that is known to its probability which predicts the possibilities of outcomes that might occur or takes place. Neighborhoods is a technique that shows the similarities in the databases. While Clusters allows the data to group together based on the needed information of the user.

In Philhealth (Philippine government corporation), questions like what are the chances of 20% increase of new customers? Which patterns are significant in previous customer records? Those are just an example of questions that most people want to know that statistics can contribute. While Neighborhoods, shows the similarities in the records of customers just in a minute, depends how large the database is. Lastly clustering data, like for example the age, gender and address of the customers can contribute in marketing and other queries that can develop better services to the customers. PRIZM system and MicroVision are the two examples of clustering systems that can be used. Using data mining tools, Philhealth management can come up in planning the finest decision possible.

-Next Generation Techniques: Trees, Networks and Rules

It is the most common techniques used that build a predictive models, this leads to discover new information. Decision Tree breaks down the information in the databases that will show the useful information to the question you want to answer. Networks can detect patterns or trends in connected networks or nodes that can solve difficult problems and create decisions. A Rule has limitation to gather the needed data with the use of “if this and this and this then this”.

DATAMIN course and our Professor Mr. Duremdes at De La Salle Canlubang help us to somehow relates in deciding data mining tools to use in a certain situation in I.T perspective. Data mining tools are quick to run and give result at the minimum time, to determine the best techniques, trial and error can be done to see which techniques to use however decisions are made by the user whether to use the result in data mining tools or not. Knowledge worker should choose the most appropriate data mining tools based on what information in the database is all about, when to use the chosen techniques which leads to a competitive advantage, why it is needed in the business or organization and who will create and apply the data mining tools.