5 Myths of Data Mining
Common myths about data mining
What is Data Mining?
Data mining is used to analyze data, detect patterns and relationships within it, and convert it into useful information for businesses to make better decisions. The analysis of data has occurred for centuries but has recently become prevalent since new specialty technologies have emerged into the marketplace. But, with this comes many misconceptions and myths about what data mining is, how it works, and the benefits of utilizing it.
Myth #1: Data mining is an extremely complicated process and difficult to understand.
Algorithms behind data mining may be complex, but with the right tools, data mining can be easy to use and can change the way you run your business. Data mining tools allow you to easily see and understand your data with simple to understand graphs, queries, and visualizations gives you insight as to how your business is performing. You can then identify problems and potential issues and make analytics-based decisions to improve upon your inefficiencies.
Data mining tools are not as complex or hard to use as people think they may be. They are designed to be easy to understand so that businesses are able to interpret the information that is produced. Data mining is extremely advantageous and should not be intimidating to those who are considering utilizing it.
Myth #2: Data mining is another trend that will soon die out, allowing us to return to standard business practice.
Quantitative practices have been employed by businesses for quite some time. Data mining is just a more developed practice that has come about since the beginning of the 20th century. Data is everywhere and the size of some databases are tremendous, making it extremely difficult for discovery to be done manually. With the easy-to-use functionality, cost and time reduction benefits, and the ability to conduct an analysis of your company’s performance within a quick to deploy, and easy to understand solution makes it hard to believe that something so advantageous and beneficial will ever fade away. If anything, data mining will be an everlasting and growing tool that will help us for years to come.
Myth #3: Data mining techniques are so advanced that they can replace domain knowledge.
Expertise and experience of the business and its markets cannot be replaced by data mining techniques. Knowledge about the new analytical methods that arise is important but, without knowledge of the business and its markets there is of no use to these methods. Therefore, it is critical to have an understanding of both.
If you are conducting an analysis of a company’s data, it is important to have someone who is an expert in the field to make sense of the information produced and vice versa. If there is someone with knowledge about the business and its markets, it’s important to have an expert in data mining conduct analysis with tools and modeling to help improve their business knowledge. Data mining essentially cannot exist without domain knowledge.
Myth #4: Only big databases are worth mining.
Although data mining is more commonly used for analyzing big data sets, it can be used for any size. Just about any amount of data can produce valuable information that can be used for businesses to detect issues and potential issues. Even these sample size datasets allow for businesses to find inefficiencies in which they can proactively or plan to improve upon. It may be more beneficial to pull certain data from a large data warehouse to conduct an analysis versus the entire database itself. You just need to know which data you want to analyze to produce valuable outcomes and conclusions.
Myth #5: Data mining is useful only in certain industries.
Although data mining may be most commonly used within highly data focused and innovation driven industries, it is a tool that can be used in any industry. There will always be an instance in which data mining might not be worth the return on investment. But just like how the size of the database does not matter, neither does the industry. There is value that can come from any type of data you analyze.
This article was originally published on Medium