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Data mining is a technique of treating data in a certain way to improve interpretation and behavioral insights. Data mining is critical because it removes previously unknown key knowledge from data. Data mining is the process of extracting information from a large database. Methods such as data pre-processing, model considerations, complexity considerations, web updating, and simulation are used. Data mining has been used for over two decades. It supplanted early examples of pattern recognition such as regression analysis in the 17th century and the Bayes theorem in the 16th century. The technology development and its ubiquity have seen an increased data collection and storage techniques. The same way data collection has grown, the more need for a simplified data analysis forms. This research paper focuses on data mining, various types of data mining, and the benefits they have in the organizational setting. The core role played by this essay is to make it easier for multiple business organizations make the wisest decision when it comes to deciding the best data mining technique to use.
Cross-Industry Standard Process for Data Mining
Cross-Industry Standard Process for Data Mining, CRISP-DM is a data mining process describing various approaches applied to solving problems. Teradata, OHRA, and SPSS discovered CRISP-DM in the year 1997 among other counterparts. Data mining plays six major roles that help in data interpretation. First, data mining helps in dependency modeling. Here is where related data is recognized. In marketing field, it looks for possible relationship between given variables. A good example is identifying the most selling products and the frequent customers through a process referred as market basket analysis. Data mining helps in regression. Regression is coming up with a function that estimates relationship in data with few errors. Data mining is used to generate a summarized report on data sets. Data mining is used to classify using a known structure to be applied to the new data. A good example is a differentiation between trash and inbox in the electronic mailing system. Data mining helps to detect faults and errors in data records. Clustering is the other task carried out using data mining. Here data structures and patterns can be realized without the use of known criterion or formula (Antony et al., 2005).
CRISP-DM helps to achieve the above six tasks by breaking down the whole process in smaller segments. The sections are not restricted to be followed in their order, but the sequence can skip one to another depending on the kind of data as shown in figure 1.
Business understanding deals with investigating the goals of a business firm and utilizing the knowledge in data mining process. In most cases, the use of decision model is implemented. Data understanding phase includes gaining access to the initial data and familiarizing with it to discover any fault and formation of hypotheses for the restricted information. Data preparation involves selecting tables, forms, records and attributes to be used in data modeling. Here data-containing errors are cleaned. The next phase is modeling. This step applies various techniques of calibrated parameters for a particular data-mining query. It also calls for often step back to data preparation since the data mining problems maybe numerous.
It is important to evaluate the model that you prefer before you deploy it. Here you go through the procedure used to construct the model. After evaluating, you will be able to discover in case an individual business issue was omitted in building the model. In the event of any omission, it calls for a step back to business understanding phase. If all the business issues are well catered for then, the model is approved. The last step that does not involve any step back is the deployment phase. The gained knowledge during the creation of the model requires to be analyzed and be presented to the customer. This presentation is acquired by the generation of a report or segment allocation. In cases where the analyst does the deployment process, they need to familiarize the customer on how to use the model (Berry et al., 1997).
Fig 1: Relationship between various phases involved in CRISP-DM
Application of Six Sigma for Data Mining in manufacturing organizations
Six Sigma plays a significant role in the production process and enhancement of better working conditions. Most manufacturing organizations use six-sigma software to analyze data for various projects meant to maximize the productivity. Six Sigma has numerous roles to play in the organization. First, six sigma help users to make the right choice when it comes to choosing the methods being used. Secondly, it assists teams to analyze data efficiently and with a centralized approach in an organization. Six Sigma helps achieve confidence of management by reducing possible human errors, especially in data entry. Six-sigma software plays a significant role in reducing much effort ought to be used in sophisticated analysis and procedures of data mining. Six Sigma comes up with a standardized analysis method to be used in organizations. This review eases presentation of data (Antony et al., 2005).
The data mining process in manufacturing organizations involves some steps to be followed. First, business objective is defined. The target business purpose of an organization is analyzed through assessment of business rules and identification of target customer for a given product. The second step applied is sourcing data. It involves acquiring data sources that relate to the defined business objective such as production, transaction, and customer databases. The next step is preparing data. After obtaining data sources, you join them to acquire derived attributes preparing for the data mining. Such preparations include determining the number of transactions conducted in a day.
The next step is the data mining itself. Here software tools and techniques are used to assess any relationships available in a data respecting the sub-populations in the data. The fifth step is validation. For the sake of reporting and application of a given model to new information, validation is necessary. The last step is the assessment method. The findings of data acquired are assessed to approve whether it can be useful in the operation domain.
The relationship between Define, Measure, Analyze, Improve, and Control (DMAIC) and Six Sigma
Define-Measure-Analyze-Improve-Control (DMAIC) is the most used methodology of the six Sigma. DMAIC is used for solving defects and deterioration. Most of the professionals tend to use six sigma's DMAIC method to acquire leadership chances in business premises. Figure 2 below shows how the five business performances relate to one another.
Fig 2: The DMAIC Methodology Approach
The first phase of DMAIC is the define stage. At this juncture, the stakeholders agree upon parameters defining a project. Secondly, the customer needs are aligned with the goals of the project. Project plan and milestones to be achieved during the entire project are defined at this stage. The second phase is the measuring step. During this phase defects, data collection plan, detailed process map, and opportunities are developed. Secondly, the measurement system is validated where Y=f(x) relationship is the used at the beginning. Determination of sigma baseline is very crucial at this phase. The leaders to compare against future achievement will use the same sigma baseline later. A detailed process map will elucidate areas that require performance enhancement.
The next phase is the analyze step. This phase aims at focusing on the cause of the defect. Here sources of variation are discovered and definition of performance objectives. Data is analyzed to come up with fields providing efficient results upon change implementation. Besides, areas that data underscores are discussed and ways of improvement are defined. The fourth phase is the improve step. The core objective of this phase is to finalize a test run of the proposed change. Development of potential solutions and operation tolerances takes place at this stage. Secondly, the failure modes of these alternatives are assessed (Azis & Osada, 2010). This assessment is accompanied by a re-evaluation of the potential solutions. The sixth and the last step of DMAIC is the control phase. The aim is developing metrics used by the overseers to monitor and note continued success. Standards and procedures to be employed in the whole project are developed. Development of transfer plan and verification of benefits and profit maximization is acquired at this phase. Statistical process control is implemented at this stage too. Documentation is the last step that finalizes the action. The end of this cycle demands the address of additional procedures or the completion of the project.
Application of Define, Measure, Analyze, Improve, and Control (DMAIC) in manufacturing organizations
Many manufacturing organizations have tried to use DMAIC approach in optimizing their achievements. The various phases of DMAIC tend to cater to the customer and organizational demands to maximize business processes.
Define phase is the step that caters for the client needs in the Lean manufacturing. This step makes use of the SIPOC flowchart. SIPOC shows the relationship between the Supplier, input, process, output, and customers as portrayed by figure 3 below.
Fig 3: SIPOC Flowchart
In the second phase of DMAIC, qualitative data is gathered to have a clear understanding of the current situation. The organization, in this case, is ready to get rid of the wasting process. The Lean Six Sigma manufacturing methodologies are used to interpret this data and acquire the sigma baseline (Azis & Osada, 2010).
During the analysis phase, various analytical methodologies come in place to investigate the source of the problem. Lean manufacturing mostly tends to utilize synergy effect tool. For six Sigma, Failure Mode and Effects Analysis (FMEA) are used whereas Lean manufacturing makes use of value stream mapping to streamline timesaving techniques. At this phase, Analysis of Variance (ANOVA) comes in place to test the importance of the differences evident between different samples.
During the improve phase, both Lean and six sigma manufacturing come up with specific solutions concerning numerous defects. Here all the analysis that took place during the prior phase is implemented in manufacturing field. The six Sigma uses Design of Experiments (DOE). It is easy to understand factors affecting a process and come up with practice tests for verification of possible improvement theories since a structured statistical approach is provided. Lean manufacturing uses KAIZEN, activities that involve every employee in an organization to improve productivity.
The control phase comes in place after all defects elimination. The team is mainly focusing on the creation of a monitoring plan for measuring success plan. A response plan caters for a dip in performance.
In conclusion, data mining is very crucial in numerous fields and many disciplines. In communication control, analytic models of the consumer data make behavior prediction easy. Insurance field solves complex issues such as compliance, risk management, and fraud using data mining techniques. In education, data mining monitors the progress of student's performance and intervention methods. Other disciplines include banking, retail, and manufacturing fields. To manage the increasing competition in various areas, it calls for increased productivity. Data mining comes in to help achieve maximum profit.
Antony, J., Kumar, M., & Madu, C. N. (2005). Six Sigma in small-and medium-sized UK manufacturing enterprises: Some empirical observations. International Journal of Quality & Reliability Management, 22(8), 860-874.
Azis, Y., & Osada, H. (2010). Innovation in management system by Six Sigma: an empirical study of world-class companies. International Journal of Lean Six Sigma, 1(3), 172-190.
Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support. John Wiley & Sons, Inc.
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