Data Warehousing Mining
Data warehousing mining is the process of accessing a centralized
database for the purpose of business analysis and discovery. In
data warehousing mining business data is taken from multiple sources
and is stored in a central repository where the data warehousing mining
takes place. Data warehousing mining has traditionally been the
domain of large organizations, but smaller businesses are finding that
the tools and resources are now available to make data warehousing
mining accessible even to the most modest business.
Does Your Business Need Data Warehousing Mining?
Just a few years ago the answer to this question was directly related
to company size. Smaller businesses did not have the financial
ability to implement data warehousing mining, making it an exclusive
enterprise solution. With the introduction of open-source databases,
low cost data integration tools like EMANIO Unite! and affordable data
mining software like EMANIO Insight!, however, data warehousing mining
is now a question of need vs. cost. The easiest way to answer
the question of need is to consider the amount of data your business
owns. Data warehousing mining is an ideal way to take vast volumes
of data from places like CRM, ERP, Accounting and other systems, in
order to create a clearer picture critical business trends. The
better decision on your need for data warehousing mining then is related
to vision. Specifically, do you need to have greater insight
into what drives your business? If the answer to that question is yes,
you may need data warehousing mining.
Getting Ready for Data Warehousing Mining
Before you begin to consider what you will need for data warehousing
mining, however, you need to ensure that your business and your systems
are ready for data warehousing mining. In the world of data warehousing
mining the old saying of "garbage in, garbage out" is certainly
true. Most businesses pay little attention to how "clean" or "complete" their
data is until after they have embarked on data warehousing mining. A
better way to prepare is to consider these issues before a data warehousing
mining project is started. Specifically, how much dirty data
do you have? dirty data consists of incomplete fields, fields with
improper entries and other typographical mistakes that can pollute
a data warehousing mining project. In addition you should consider
the "completeness" of your records - for example, how much
demographic data do you have on your customers? The more data
your records have, the more effective your data warehousing mining
will be.
Data Warehousing Mining and the Small Business
Once you have identified that you have the right data for data warehousing
mining, the next step will be to consider the tools you will need to
implement data warehousing mining. At the core of a data warehousing
mining project will be the database used for the data warehouse. With
open source tools like My-SQL as well as low-cost database systems,
the options for a core database are plentiful. Additionally
you will need access to extract, transform and load (ETL) tools like
EMANIO's affordable Unite! product, to help you populate your data
warehousing mining project with data from your multiple sources. Once
you have your data set up it's time to use reporting and analysis tools. Products
like Crystal Reports and EMANIO's Insight! data mining tool will give
you affordable means of creating a successful, low-cost data warehousing
mining project.
Using Data Warehousing Mining for Success
Now that you have created your data warehousing mining project the
next focus will be on best use. To get the most from data warehousing
mining, the key will be to ensure that the "right" data is
in your data warehousing mining repository. To ensure this your
best bet is to consider what key metrics are important
to your company? The
data related to those metrics will need to be part
of your data warehousing mining. For example, if average days
outstanding (DSO) is a key measurement, having data related to sales
and the days each sale requires to be paid will be a critical part
of your data warehousing mining.
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