The aim of Data
With the increase of database volume and the generalisation of data access, analysis needs of business and trade users created the demand for the development of Data Mining tools.
More than classical data navigation products, business
users now also need analysis tools that can perform guided
and on-line analysis, following an interactive and
user-friendly process, and giving easy to understand
With the development of data warehouses and storage
solutions, the amount of data held by companies is
becoming more and more important.
Much of that information
may not seem interesting at first, are stored because
it is now easy and inexpensive. This point explains
why data mining has become a key issue in today's information
systems: when data mining is needed, the raw data
necessary to process the analysis will almost always
be there, waiting to reveal all the hidden trends
ISoft and Data Mining
ISoft provides tools that are
designed specifically for the business user. With
ALICE d'ISoft, you will be assisted during these steps.
This user friendly tool with its intuitive interface
will guide you through all the analyses, performed
as an on-line process.
Use Amadea to prepare your data and Discovery to evaluate
the quality of your source data.
The need for Data
The need for data mining has developed since business
users wanted to reappropriate their data, to easily
test their model and build new ones, guided
by their strong knowledge of business. One major
improvement of data mining over classical navigation
tools is that, more than just testing an already
supposed trend, it can discover completely new patterns, and fully validate a model. The model can be built automatically but each step of its construction can be controlled
by the business user. This model then enables him
to predict the behaviour of other data,
and to evaluate their chances of behaving as predicted.
But this work on the data is efficient only if it
follows a good methodology. Several steps should be
followed, in order to reach significant results.
Starting from heterogeneous raw data, you should
first set up your data warehouse, in order to have
a clean relational database.
You can then start the alimentation of your data mining
tool. At this stage, you should be able to provide
a good data description.
One key step is a good definition of your problematic.
However powerful it might be, a data mining tool needs
you to isolate the question you want to answer.
Even if this question can change during the analysis,
a good starting point must be defined.
Then only you can proceed to the analysis steps. During
the analysis, you will need to go back several times
to a preparation step, where you can define new, more
relevant variables, use aggregation formulas... During these steps you will draw on your knowledge, to isolate and keep the interesting models and remove others..
After your analysis, you will be able to report your
conclusions, as an expert in your field.