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Here's what we'll cover:
A major challenge for businesses is how to turn large, convoluted data sets into information that users can leverage to improve operations. Data mining software allows users to apply semi-automated and predictive analyses to parse raw data and find new ways to look at information. For example, ecommerce companies use data mining applications to analyze visitor demographics and discover how to deliver a better customer experience.
Data mining applications help users discover correlations and connections within large data sets. These might have gone unnoticed without these algorithms.
Companies implement data mining software systems to:
A classic example of how data mining software can be used is with customer purchasing patterns at grocery stores. If shoppers tend to buy items such as toilet paper, diapers and alcohol before the weekend, retailers can place these items closer together to maximize revenue. Store owners can further capitalize on this opportunity by running specials on these items to encourage additional purchases.
When evaluating data mining software, you should consider the following:
Best-of-breed or integrated suite? Buyers should consider whether they want a stand-alone, best-of-breed data mining application or would prefer to go with the data mining module from their existing Enterprise Resource Planning (ERP) provider. If buyers choose to evaluate stand-alone systems, they should discuss integration capabilities with these pure-play vendors.
Do you need to invest in hardware? Businesses without IT resources (or a budget to invest in new, faster servers) may choose to instead host their data in the cloud. However, in-memory processing advances have improved the speed and capability of these applications, lessening the IT investment previously necessary to effectively utilize data mining applications.
Do you have the talent to utilize these applications? Like with any software application, data mining solutions require the right questions to discover useful answers within data. For example, if you are evaluating data mining tools from enterprise vendor SAS, do you have analysts versed in the sample, explore, modify, model, assess (SEMMA) framework used in SAS data mining applications? Businesses must have sophisticated users to make the most out of their investment in these systems.
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