Page 161 - DCAP208_Management Support Systems

Management Support Systems

Notes gives idea and hypothesis on what the patterns might be. While predictive modeling is the
process of using the pattern gather from the database and use the data to predict future. The third
categories are the forensic analysis.

Did u know? Forensic analysis is the process of implementing the extracted patterns to
determine differences or non-standardized data.
Data mining automates the process relevant patterns of current and historical data in the database
to be analyzed to forecast the future. Through the ability of data mining tools to predict and
analyze behaviors of data in the databases, it will be able to guide the organization to produce
proactive and efficient decision making and answer question that is urgently need to be solve in
a little time.

It is very difficult for data mining software companies to create tools which are geared towards
businesses. The reason for this is because many of the people who are responsible for this
technology will place an emphasis on computer algorithms.
To most business owners, algorithms are not important. Data mining is a technology that is now
being used mostly be large corporations, and because of this, the focus cannot be algorithms.
Many popular data mining programs have algorithms that only compose about 10% of their
structure. The question that many developers must as themselves is where should the emphasis
be placed on the other 90 percent?
The first place that data mining developers can focus on is database integration. The data mining
tools that are created must be able to function with data warehouses. When the files are flat, this
will not allow the tool to work with many databases, and this will cause problems. Fortunately,
many data mining developers have taken this advice, and are designing their tools in a way that
allows them to work seamlessly with the data warehouses of many companies. However, there
are still some developers that are not doing this. The next area that is important for data mining
tools is called automatic model scoring.

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Caution Scoring is one of the most tedious aspects of data mining.
There are a number of contemporary data mining programs that cannot score the models that
they create. If you are using any of these programs, you will have to develop your own scoring
system. This is tedious, time consuming, and unnecessary. In addition to this, when you have to
manually produce a scoring system, it is likely that you will have many errors. The scoring
system will often have to be done by the information technology department, and they don’t do
it correctly, there could be a number of problems. To solve these problems, developers will
want to create data mining tools that automate the process of scoring models that have been
created.

By automating the process of scoring data mining models, companies could become more
efficient and less prone to errors. Another area where data mining programs need to improve is
exporting models between different software programs. Once a model has been generated, it is
important for other programs to be able to understand it. By doing this, the process of scoring
can be much more efficient, the models can be used by numerous tools. In addition to this, it is
important for data mining tools to begin using more business templates. The goal of a company
is to solve a business problem rather than a statistical issue. Developers will want to calibrate
the data mining tools in a way that makes them more relevant to business users.
Users should also be given more control over the data mining programs they use.

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