What are the advantages and disadvantages of data mining tools?
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
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.
Is data mining reliable?
Defining web business intelligence (WBI), data mining and data warehousing
Data mining: Three steps to mining unstructured data
Evaluating analytics tools: Don't judge a tool by its label
Learn about the ethical dilemmas posed by the growing reach of analytics
Find out how to overcome executive resistance and empower data-driven decisions
Discover how prescriptive analytics affects the analytics maturity model
Related Q&A from William McKnight
Are business intelligence certifications worth it? Get certification advice from our business intelligence expert, William McKnight.continue reading
Learn the best way to select or customize your business activity monitoring software -- which begins with analyzing your business requirements.continue reading
Get examples of how data mining is used in vertical industries, such as retail, manufacturing, healthcare, financial and telecommunications.continue reading
Have a question for an expert?
Please add a title for your question
Get answers from a TechTarget expert on whatever's puzzling you.