Data mining starts with the client. Clients naturally collect data simply by doing business; so that is where the entire process begins. But Customer Relationship Management (CRM) Data is only one part of the puzzle. The other part of the equation is competitive data, industry survey data, blogs, and social media conversations. By themselves, CRM data and survey data can provide very good information, but when combined with the other data available it is powerful.
Data Mining is the process of analyzing and exploring that data to discover patterns and trends.
The term Data Mining is one that is used frequently in the research world, but it is often misunderstood by many people. Sometimes people misuse the term to mean any kind of extraction of data or data processing. However, data mining is so much more than simple data analysis. According to Doug Alexander at the University of Texas, data mining is, “the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.”
Data mining consists of five major elements:
1) Extract, transform, and load transaction data onto the data warehouse system.
2) Store and manage the data in a multidimensional database system.
3) Provide data access to business analysts and information technology professionals.
4) Analyze the data by application software.
5) Present the data in a useful format, such as a graph or table.
This technique is a game changer in the world of statistical analysis and business. It is important in this realm because it can make predictions that older analyses techniques were simply not capable making. This visual from thearling.com may help understand the evolution and differences of data analysis through the years:
| Evolutionary Step | Business Question | Enabling Technologies | Product Providers | Characteristics |
| Data Collection (1960s) |
“What was my total revenue in the last five years?” | Computers, tapes, disks |
IBM, CDC |
Retrospective, static data delivery |
| Data Access (1980s) |
“What were unit sales in New England last March?” | Relational databases (RDBMS), Structured Query Language (SQL), ODBC | Oracle, Sybase, Informix, IBM, Microsoft | Retrospective, dynamic data delivery at record level |
| Data Warehousing & Decision Support (1990s) |
“What were unit sales in New England last March? Drill down to Boston.” | On-line analytic processing (OLAP), multidimensional databases, data warehouses | Pilot, Comshare, Arbor, Cognos, Microstrategy | Retrospective, dynamic data delivery at multiple levels |
| Data Mining (Emerging Today) |
“What’s likely to happen to Boston unit sales next month? Why?” | Advanced algorithms, multiprocessor computers, massive databases | Pilot, Lockheed, IBM, SGI, numerous startups (nascent industry) | Prospective, proactive information delivery |
Table 1. Steps in the Evolution of Data Mining.
Data Mining can be used in many different sectors of business to both predict and discover trends. It is a proactive solution for businesses looking to gain a competitive edge. In the past, we were only able to analyze what a company’s customers or clients HAD DONE, but now, with the help of Data Mining, we can predict what clientele WILL DO.
With Data Mining, companies can make better and more effective business decisions – marketing, advertising, etc – decisions that will help these companies grow.
For more information about how Data Mining can help discover trends and patterns in your market, contact the market research specialists at The Research Group by calling 410-332-0400 or click here today!
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Sources:
http://en.wikipedia.org/wiki/Data_mining
http://www.thearling.com/text/dmwhite/dmwhite.htm
http://www.laits.utexas.edu/~norman/BUS.FOR/course.mat/Alex/
http://databases.about.com/od/datamining/a/datamining.htm