Data Mining
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.
The patterns that data mining discovers can have several forms
- Trends in data over time
- Clusters of data defined by important combinations of variables
- Evolution of these clusters over time
Marketing database purposes
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.
The Process
Data mining involves a number of steps:
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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
Data selection involves careful isolation of variables from one or more databases. It is better to start with less data than more, and to evolve a more complex model from a successful simple one. -
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.
Data Mining Tools
Data mining uses a variety of analytical tools. The most commonly applied techniques include:
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Clustering
Searches for groupings in the data. Clustering begins with no assumption about how many groups there are, and explores the data to establish the most informative number of groups. -
Classification trees
It defines groups by selecting breakpoints for the variables. -
Association rules
It use the correlation among variables to develop rules that express, likely, or common relations among variables. -
Neural networks
It extends classical statistical procedures by removing assumptions and allowing complex, nonlinear relationships to be efficiently modeled. -
Fuzzy logic and clustering
They can adapt to the intricacies of real world data. These tools enable the analyst to represent the ambiguity that often exists in data, for example, how to define, large versus small customers. -
Visualization
This technique provides immediate insight into the patterns revealed by the data. Modern data mining tools offer a variety of graphic displays to display key results with immediate impact.
ROI of Data Mining
Deployment: key to data mining ROI
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 staff as they interact with customers.
Our solutions offer a broad range of techniques designed to meet virtually any data mining needs.