Mining Data for Business Insights

Anyone who has an Amazon.com account has experienced the power of data mining at the retail level.  The company periodically sends its regular customers “recommendations” of products they might like based on what they (or like-minded consumers) bought in the past.  It is classic cross selling made easy.

 How does Amazon.com know what you might like?  The simple answer is the company has deployed data mining software that reveals vital information about your buying preferences.  This article looks at some features of data mining technology.

What is Data Mining? 

Data mining is a powerful tool for digging deep into enterprise data to reveal underlying patterns and relationships that can be used to build prediction models.  The tool brings the benefits of predictive analytics to business processes.  In short, data mining fuels business insights through trends predicated on detailed analysis of vast amounts of related data. 

This explains why data mining tools are worth considering in the context of a database or data warehouse and business intelligence (BI) system.  It also explains the trend among  power users toward the integration of analytical functions into a data warehouse.    

What Types of Data Can Be Mined? 

Generally, when people talk about data mining, they talk in terms of numbers.  This is understandable as practically all initial deployment of data mining technology was directed at sifting through numerical data, including: 

• in-house transactional data, such as sales, purchases, and inventory;
• in-house operational data, such as payroll and administrative data;
• third-party data, such as industry trend data, pertinent national and global forecast data. 

However, businesses now accumulate vast amounts of non-numerical information, in the form of text-based documents, emails, survey feedback, and meta data (a.k.a. data about the data itself).  The explosion in the business role of the Internet translates into significant amounts of non-numerical data captured from diverse Web-based applications.  These text data are a mixed bag, which must be sorted for a better appreciation of their information value.  Therein lies the relevance of text mining tools used to analyze text documents by extracting key phrases and attributes, which in turn can be combined with other data to create a more comprehensive picture for decision-making.  

 Who Are the Big Users of Data Mining? 

Who knows your credit card better than you do?  Your favorite retailer, that’s who!  In addition to Amazon.com, other major users of data mining tools include Wal-Mart, major credit card issuers, and retail supermarkets.  

These companies design products and promotions to target customers identified by buying behavior, demographic characteristics, and income.  Even your geographic location becomes crucial marketing information when combined with other seemingly disparate bits of data about your buying habits. 

Because of the capability of data mining technology to drill through layers of data, it provides insights at almost granular level.  Information can be distilled to permit the configuration of stores and in-store product displays to capture specific customer segments.  It takes most of the guesswork out of designing effective marketing campaigns. 

In much the same way that data mining can be used to identify patterns that drive efficient marketing strategies, it can also be used to flag data anomalies, which can be helpful in fraud detection and prevention. 

Leading Data Mining Software on the Market 

Recent industry surveys from IDC, Datamonitor, and Forrester Research put SAS Institute and Oracle at the top of the list of providers of standalone and integrated data mining tools.  Other vendors making the top providers’ list include familiar BI brands, such as  IBM-Cognos (soon to include SPSS), SAP-Business Objects, Microsoft, and Information Builders.

 © Copyright Rachel Agheyisi and Business Intelligence Notes Blog, 2009.

Advertisement