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.

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Master Black Belt Curriculum

We believe Master Black Belt training should build upon already existing Black Belt training, expanding on the concepts delivered in their previous education. Master Black Belts will typically require two or more years of Black Belt experience (plus training), and five or more years of business experience. MBB’s previous work and expertise should inform their MBB training, building on their existing skills and knowledge base. Some areas of responsibility for Master Black Belts are as follows:

• Leadership and people skills. MBBs will typically manage and coach Black Belts, Green Belts, Yellow Belts, and entire Six Sigma teams, while on the project floor. Furthermore, leadership and people skills are essential to building strong teams and professional relationships. Additionally, MBBs teach team members to deploy improvement tools and solutions, as well as identify areas that require attention.
• Developing and implementing organizational metrics. Classical Six Sigma metrics include timeliness, accuracy, efficiency, workability of business and cost, as well as other measures of quality. Moreover, Master Black Belts should know how to select and implement appropriate metrics.
• Creating, maintaining, and reviewing Six Sigma curriculums in classroom-based training. As Master Black Belts, you will be required to assess and deliver Six Sigma training programs, providing coaching and support for other Belts.
• Networking is an essential skill for Master Black Belts to master, as it can open many doors for you when it comes to finding employment. Master Black Belts often work as consultants, an independent role that requires seeking out opportunities. Networking with other MBBs and Six Sigma practitioners will enable you to get the lay of the land. Likewise, MBB training should teach critical networking skills to build your network from the ground up. Maintaining strong professional relationships is important as an MBB, and training should support this.

Essential Master Black Belt Skills

• DMAIC. MBBs should be able to use and teach DMAIC in the classroom and on projects.

• Non-Parametric Analysis. When there are fewer assumptions to deal with, NPA enables MBBs to get a handle on their data.

• Multi-Vari and Practical Experiments. MBBs will be expected to address multiple parameters and sources of process variation at any one time. Similarly, experience with practical experimentation is just as important.

• Handling Attribute Responses and Optimization Experiments. Our MBB curriculum covers how to use Minitab to handle attribute responses and create optimization experiments.

• Advanced Regression and SPC Methods. Regression alone is a difficult tool to use, and SPC can be just as tricky, but advanced methods are often required. Our MBB curriculum teaches several different ARMs and ASPCs.

• Handling Multi-Response Experiments. We believe in going above and beyond Freeman-Tukey when it comes to MREs.

• Distributional Analysis. We teach our MBBs to understand the far-reaching effects of policy programs and funding decisions.

• SIPOC Diagrams. SIPOC diagrams identify key factors of process improvement projects before commencement.

• Identifying CTQ (critical-to-quality) factors. Quality MBB training teaches how to draw CTQ trees. This enables MBBs to measure and display improvement efforts and align them with customer demands.

• Statistical software training. Reliable, statistical data underpins all Six Sigma work. Our trainers teach MBBs how to use statistical software like Statgraphics, SigmaXL, and SPC XL.

• Poka-Yoke. Mistake-proofing improvement measures will ensure the same problems don’t arise in the future. Our course teaches MBBs to recognize opportunities and devise solutions to reoccurring issues.

Contact us if you have additional questions.

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