Data mining, the extraction of unseen extrapolative information from large databases, is a powerful new technology with great prospective to help companies focus on the most important information in their data warehouses. Data mining tools foretell future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move ahead of the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve..

Today's organizations accumulate huge volumes of data from a variety of sources on a daily basis. However, turning increasingly large amounts of data into useful insights and finding how to better utilize those insights in decision making remains a challenge for most.. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line.
Some successful application areas include:

• A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations.
• A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.
• A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.
• A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.

To get answers to complex questions and gain an edge in today's marketplace requires powerful, multipurpose predictive analytic solutions so you can learn from, utilize and improve on knowledge gained from vast stores of data. Team Webgalli provides a wide range of software for exploring and analyzing data to help uncover unknown patterns, opportunities and insights that can drive proactive, evidence-based decision making within your organization.