Data mining tools: Advantages and disadvantages of implementation
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What are the advantages and disadvantages of data mining tools?
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I will take your question to mean the application of data mining technologies, such as SAS, SPSS or Microsoft Data Mining to solve specific business problems. Business problems need to be solved, and often technology is required. Many problems that can be solved with data mining technologies can also be solved with OLAP. Data mining is just a better tool in the toolbox for certain types of problems.
Disadvantages of data mining tools
The techniques deployed by some tools are generally well beyond the understanding of the average business analyst or knowledge worker. This is because the tool was generally designed for expert statisticians involved in the detailed science of predictive modeling. This would be the disadvantage of data mining today. If this advanced level of analysis is reserved for the few, instead of for the masses, the full value of data mining in the organization cannot be realized. For those with average analytical capabilities, data mining is not nearly as effective as it could be.
Advantages of data mining tools
Data mining tools that are interactive, visual, understandable, well-performing and work directly on the data warehouse/mart of the organization could be used by front line workers for immediate and lasting business benefit.
There are numerous, accessible data mining techniques that are more effective than most simply because they will be used by so many within an organization. With little investment, they can draw attention to significant anomalies that deserve further investigation. Data mining tools help customers analyze data by executing a series of actions and returning results that provide visibility into behaviors surrounding the dimensions of the company’s business. SQL Server 2005, for example, provides seven out of the box algorithms that can assist a company in obtaining insight into their business. Each algorithm works differently to produce an output of results. In all cases, the algorithms are trained by exposing them to the customers’ existing data sets. The training set might include sets such as order history, payable/receivables, web navigation logs, or customer demographic information.
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