Rules Must Keep In Mind For Data Mining

By Jyoti Nigania |Email | Feb 8, 2019 | 10632 Views

Positive returns on analytics investment require management action. But many managers are reluctant to take action based on analytics, especially when the numbers don't seem to match with their own gut understanding. They don't know much about analytics and don't really trust the process. 

Following are the 9 Laws of Data Mining:
1. Business objectives are the origin of every data mining solution: If you don't know what problem you're trying to solve, you probably won't solve it.

2. Business knowledge is central to every step of the data mining process: If you don't have someone who knows the business on the team, you won't get good results.

3. Data preparation is more than half of every data mining process: Analytics isn't always pretty. Most of the time and effort goes into the dirty work of cleaning data and getting it in shape for analysis.

4. The right model for a given application can only be discovered by experiment: In business applications, it takes a lot of trial and error to find predictive methods that work for you. This is different from classic scientific research processes.

5. There are always patterns: In practice, your data always holds useful information to support decision-making and action.

6. Data mining amplifies perception in the business domain: Do the analysis and you'll know and understand more than you did before.

7. Prediction increases information locally by generalization: Good analytics processes provide useful predictions and a better understanding of what's likely to happen in specific business situations.

8. The value of data mining results is not determined by the accuracy or stability of predictive models: Judge results by the value they yield for the business, not by the mathematical details.

9. All patterns are subject to change: What works today may not work tomorrow. You've got to keep investigating.

Positive returns on analytics investment are a realistic expectation when you begin with the right plan. You've got to understand the process, and that includes the understanding that analytics is a down-to-earth, nitty-gritty process that only produces results when you use the results to drive action.

Source: HOB