At Mainward we understand that data mining is a critical activity for organizations with major, expanding databases.
Mainward understands that data mining seeks to find new patterns hidden in the data stored in large databases common to major
organizations. These databases, containing operational, marketing, and customer data, form an untapped resource. Data mining is the
procedure to unlock and exploit the patterns in those databases.
Data mining has two principal activities: finding patterns in data and describing those patterns clearly. Successful data mining provides insights into your data, explains your data, and enables you to make profitable predictions from it.
Mainward data mining solutions identify where mailings have
succeeded or failed. This enables the customer to refine the
content of future mailings and to target audiences more likely
to respond and/or to purchase.
At Mainward we provide data mining techniques in order to
solve a critical problem: how to stay on top of the information
contained in rapidly growing databases. Today, more data often
results in less information. Organizations are often overwhelmed
by an expanding surge of data from multiple sources.
Mainward helps convert data into valuable, profit-making
information.
Data mining provides a series of methods to filter, select and
interpret data. Organizations that excel at these skills will
dominate their markets.
Setting goals
It is important to understand what are the goals of the data mining exercise: find clusters
of customers, discover unexpected expenditures, provide insight into new market areas.
Data selection
It involves careful isolation of variables from one or more databases.
Data preprocessing
Once the raw data has been selected, preprocessing is often necessary. Missing or extreme
values must be identified. If the data is not numeric, coding schemes must be employed
before the modified data can be used by the technical tools, such as cluster analysis.
Analysis
Analysis proceeds using one or more specific tools. Often multiple techniques are used,
and the analysis step is an iterative, experimental one.
Validation
Once analysis has found new patterns, validation is necessary to confirm these patterns
can be profitably exploited.
Presentation
A presentation of the results is essential to show management both the results of the data
mining and success of its predictions.
Predictions
Data visualization techniques are vital for this step.
Deployment is a key factor in obtaining a high ROI in data mining. Organizations
that efficiently deliver results to staff - whether they´re planning marketing
campaigns or cross-selling to customers in a call center - consistently achieve a
higher rate of return.
Technological advances make it possible to update massive datasets containing
billions of scores in just a few hours. Tactical data mining models can be updated
in real time, with results deployed to customer-contact.html staff as they interact
with customers.
Our solutions offer a broad range of techniques designed to meet virtually any
data mining needs.
