A major challenge for businesses is how to turn large, convoluted data sets into information that users can leverage to improve operations. Meanwhile, as companies struggle to find the best approach, their data sets continue growing larger and more convoluted, while some of their competitors turn their own analyses into actionable insight and competitive advantage.
Data mining software addresses this exact problem. It’s a core application in most business intelligence initiatives and it’s often the only tool able to extract insight from mountains of data. And as computing and application costs continue to become more affordable, data mining is no longer an exclusively enterprise-class endeavor. Now, even companies in the SMB space are rolling out data mining initiatives and reaping their rewards.
In this buyer’s guide, we address the following points and answer the following questions:
Data mining software allows users to apply semi-automated and predictive analyses to parse raw data and find new ways to look at information. It’s typically applied to very large data sets, those with many variables or related functions, or any data set too large or complex for human analysis.
Some examples of how data mining is used in different industries include:
Ecommerce companies use data mining to analyze visitor demographics and discover how to deliver a better customer experience. They might, for example, find that some products sell better during certain times of the day. Using this insight they could increase sales by reconfiguring which products are displayed based on time of day.
Insurance companies use data mining to find patterns in populations that can inform the processes of underwriting and policy management. Armed with these insights, they can offer more attractive policies tailored to specific customer segments.
Service providers use data mining to better cater to their clients’ needs and make suggestions for the most effective upsell opportunities. Cable and internet service providers regularly mine customer data to improve their service offerings.
It’s also important to note what data mining software does not do. Namely, it doesn’t collect the data in the first place. Most data mining solutions are designed to work with pre-existing data sets. Buyers are advised to pay close attention to the language and descriptions used in vendor marketing materials to ensure the tool they buy is the actual solution they need.
Data mining platforms often include a variety of tools, sometimes borrowing from other, related fields such as machine learning, artificial intelligence and statistical modeling. The offerings do vary from vendor to vendor, but there are some features common across the board. These can include:
|Data pre-processing||Help convert existing data-sets into the proper formats necessary in order to begin the mining process.|
|Cluster analysis||These tools can categorize (or cluster) groups of entries based on predetermined variables, or can suggest variables which will yield the most distinct clustering.|
|Anomaly detection||A common data mining tool that finds outliers and anomalous entries in vast, complex and/or interrelated data-sets.|
|Process automation||Data mining, by definition, requires automation. But different data mining platforms require different degrees of human input and oversight.|
Data mining applications help users discover correlations and connections within large data sets. These often include numerous entries with multiple variables and can even contain mixed structured and unstructured data. Because of the size and complexity of these data sets, any valuable correlations within them would have gone unnoticed if not for the tireless algorithmic analysis performed with data mining tools.
While specific goals vary from company to company, we can say that companies generally implement data mining systems to:
Accelerate discovery with semi-automated analyses
Segment customers into groups based on homogeneous activities and demographics
Generate models to predict future trends
A classic example of how these systems 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.
Microsoft acquires LinkedIn. June, 2016: Microsoft corporation announced it had reached a deal to acquire the professional networking site LinkedIn for $26.2B. It’s generally agreed that the software giant is making the acquisition in large part to improve its data mining capabilities with LinkedIn’s vast stores of user data.
Data mining gives researchers insight into human behaviors. June, 2016: Researchers at Harvard University, Cornell University and Microsoft Corporation announce results of a study that looks at the factors that influence how people make decisions when under pressure.
Some consumers more open to data mining. June, 2016: As data mining becomes more familiar to consumers, it’s becoming clear that some are more willing to have their data mined than others. This report looks at the relative willingness of Chinese consumers to provide personal data to companies, sometimes in exchange for free services or account credits.
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