Data Mining vs. Other Business Tools

Data Mining Differs from Other Analysis Tools in Several Ways

Mar 12, 2009

Many tools support a verification-based approach where the user hypothesizes about specific data relationships and then uses the tools to verify those presumptions

This verification-based process stems from the intuition of the user to pose the questions and refine the analysis based on the results of potentially complex queries against a database. The effectiveness of this analysis depends on several factors, the most important of which are the following:

Query and Reporting Tools

Some business analysis tools have been optimized to address some of these issues. Query and reporting tools, such as those used in data mart or warehouse applications, let users develop queries through point-and-click interfaces. Statistical analysis packages are used by many insurance or actuarial firms to explore relationships among a few variables and determine statistical significance against demographic sets. Multidimensional OLAP (Online Analytical Processing) tools enable fast response to user inquiries through their ability to compute hierarchies of variables along “dimensions” such as size, color or location.

Data mining, in contrast to these analytical tools, uses discovery-based approaches in which pattern matching and other algorithms are employed to determine the key relationships in the data. Data mining algorithms can look at numerous multi-dimensional data relationships concurrently, highlighting those that are dominant or exceptional.

Other types of analytical methods rely on user intuition or the ability to pose the ‘right’ questions. Analytical methods – query tools, statistical tools, and OLAP - and the results they produce, are all user-driven, while data mining is data-driven, a significantly different process

Data Mining Supports CRM Solutions

Traditional methods of data analysis involve the decision maker hypothesizing the existence of ‘information of interest,’ converting that hypothesis to a query, posing that query to the analysis tool and interpreting the returned results with respect to the decision being made. For instance, a marketing director may hypothesize that notebook-owning 18 to 24-year old customers are likely to purchase the upcoming software release. After posing the query, it is up to the individual to interpret the returned results and determine if the list represents a good group of product prospects. The quality of the extracted information is based on user interpretation of the posed query results.

The intricacies of data interrelationships as well as the sheet size and complexity of modern data stores necessitate more advanced analysis capabilities than those provided by verification-based data mining approaches.

Discovery-based Systems

The ability to automatically discover important information hidden in the data and then present it in the appropriate way is a critical complementary technology to verification-based approaches. Tools, techniques and systems that perform these automated analysis tasks are referred to as discovery-based.

Discovery-based systems applied to the marketing director’s data store may identify many groups, including 18- to 24-year-old male college students with laptops, 24- to 30-year-old female software engineers with both desktop and notebook systems, and 18 to 24-year-old customers planning to purchase portable computers within the next six months. By recognizing the marketing director’s goal, the discovery-based system can identify the software engineers as the key target group by spending pattern or other variable.

Verification-based Approaches

Verification-based approaches, although valuable for quick, high-level decision support, such as historical queries about product sales by fiscal quarter, are insufficient for direct marketing to customer and or prospects on a one-on-one basis. For companies with very large and complex databases, discovery-based data mining approaches must be implemented to realize the complete value that data offers.

The success of data mining depends on having correct, up-to-date data. The most advanced data mining or other business analysis tools and algorithms will not compensate for data that is incomplete, outdated or inaccurate.

The copyright of the article Data Mining vs. Other Business Tools in Customer Relations is owned by Duane Sharp. Permission to republish Data Mining vs. Other Business Tools in print or online must be granted by the author in writing.