Data mining is like mining for gold: it’s the process of extracting value from an asset, in this case, a large dataset. Data mining also includes the presentation of this information intended to create action or provide new insight, and so data mining isn’t complete until its lessons are internalized by its consumers, the decision makers in your organization.

Data Mining Evolution

Most companies use data mining software to extract value from their big data investment. Often, the front line tool is Microsoft Excel, but its limitations for data mining quickly become apparent in sophisticated organizations. Professional data mining software developed for large, complex, diverse, evolving, and often unpredictable data sources is now a critical tool for deriving the maximum value for a big data investment. Enterprise-class tools allow analysts to preserve source data while testing new extractions and visualizations on the fly, often saving 80% or more of the end to end time to insight. Data mining software analyzes the patterns and relationships in the data regarding transactions based on open-ended user queries, using machine learning, statistical and neural methods. All of them should allow you to explore the following four relationships in transaction data.

Data Relationships To Explore In Data Mining

  • Classes are stored data can be used to spot data in predetermined groups. For example, a restaurant chain could mine data regarding customer purchases to find out when customers visit a specific restaurant and what they buy and target advertising accordingly.
  • Clusters are data categorized according to their logical relationships or client preferences. That same restaurant chain might discover that workers from the local office building frequently visit during lunch, based on the zip codes in their loyalty program, and introduce offers suited to their tastes.
  • Associations can be built between various data items. In the restaurant example, they could match social media check-ins with their loyalty programs and discover that many of the enterprise’s employees are women who take their lunches to go; and offer more options, or improve their website for call-ahead orders.
  • Sequential patterns allow analysts to predict behavior patterns and trends. The restaurant above might discover that on Mondays, their loyal customers don’t come as often. Further research could reveal that the company brings lunch in for their team on Mondays. To improve profitability, the restaurant in question might reduce staff or offer coupons on Mondays.

Winning with Data Mining

Getting insight from traditional data mining techniques used to take weeks. Using tools like Designer Cloud to prepare data for data mining can reduce that time from days, and some cases, hours. When an organization uses data mining to get faster insights from its data regardless of an analyst’s technical background, the potential for transformational insight is unlimited. 

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