Six Sigma and Business Analytics: Data Mining

Six Sigma depends on the accuracy of the data you acquire. Without a strong data supply, you will struggle to achieve anything even with Six Sigma. The contrary, however, opens many doorways for process and quality improvement. Tools such as data mining are a necessity to acquire a strong data supply. Data mining sorts through large sets of data to identify trends and relationships. This allows you to devise solutions to problems through data analysis. Today, we look at some of the most challenging aspects of data mining and their consequences. Continue reading to learn how you can improve your data mining process with Six Sigma.

Difficulties of Data Mining: Limited Data Sets

Data mining is a time-consuming and frequently challenging process. It involves extensive computer-based work and uses techniques like classification analysis and association rule learning. Additional methods such as clustering analysis, outlier detection, and regression analysis are also key aspects of data mining.

While strong data mining work requires large data sets, you may sometimes find yourself with something on the small side. This is no one’s fault, depending only on the situation in which you are mining for data. Smaller data sets, however, typically render less suitable predictive models. You may find missing values, which will negatively affect results, suggesting unrepresentative conclusions.

Six Sigma methodologies like DMAIC can ensure you generate strong results, even with smaller data sets. The second and third stages of DMAIC employ focused steps to measure and analyze your data for issues. They allow you to recognize affinities between inputs and outputs, which you can use to your advantage.

Clustering

Clustering is the first stage in the data mining process, coming after pre-processing and cleaning, where we assemble the data. Moreover, clustering requires data miners to use deep analysis techniques to probe large data sets. The object of this is to discover recurring structures and groups within the data itself. Furthermore, Six Sigma is useful here as you can apply a standardized set of process steps to your work, to ensure minimal outliers and clean data.

Classification

Classification takes place after clustering, with every data point classified here to generalize your data. This then creates a new set of data, narrowing down assessment points, and reducing the complexity of your overall analysis. Like clustering, classification is a difficult process to complete. But Six Sigma can help streamline the process by removing non-value-adding steps.

Regression

Regression follows classification and clustering, where we use mathematical functions that represent data with the fewest possible instances of error. This is also like Six Sigma in that it seeks to eliminate variation wherever one finds it. Similarly, ANOVA will provide a breakdown of the key components of your data. You can then use regression to generalize the dataset for the next stage. This also creates more representative results. Remember, Six Sigma relies on strong data, and adhering to Six Sigma ideas will see you succeed.

Association Rule Learning

Association rule learning is a key technique in Six Sigma and data mining. This is where you search for trends and affinities between your variable. You determine here which products your customers typically purchase together. This data will allow you to create effective marketing to target specific customers. Using statistical hypothesis testing, you can recognize underlying factors and ways to leverage them.