Application Of Six Sigma For Data Quality Improvement

Information will also be retrieved in situations where information might have to be shared with partner companies and with the police or Tax Services.

Six Sigma more successful quality control methodology than other quality improvement techniques such as TQM or QC. Because it is very rigid with its targets and identify all types of defects by its tools. TQM technique involves predetermined quality level and standards for an organization to improve their efficiencies for production. It does not allow organization to realize full potential of it by limiting it to predefined standards. It also base on simple statistical tools and techniques. Six Sigma involves consistent and steady quality improvement with its

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There are always pros and cons to any quality improvement methodology. For instance, the pros of Six Sigma tend to place extreme importance on leadership and its support for the success of the project. Another pro is the integration of different human elements, which include cultural change, and focus on the customer and their needs. “By using the concept of statistical thinking, Six Sigma encourages applications of statistical tools and techniques that reduce variability” (Harry, 2000). The cons of Six Sigma include, not having the quality data available, especially when a new process has been implemented without having the data available. Often the solutions that Six Sigma proposes can be costly and only a small

XYZ Inc. may maintain data not provided directly by the customer. Sometime XYZ Inc. may receive data from third parties, such as business partners who provide services, or other web surfing activities (ISACA, 2017).

In a society where technology grows exponentially, the amount of information available is practically endless. Whether one needs to know ingredients to a recipe, or cures to an illness, the Internet has an answer. The simplicity of the Internet, however comes with a major drawback: the quality of the data. In Peter Cardon’s book Business Communication: Developing Leaders for a Networked World, he explains that there are five main components that one should consider in evaluating quality data: reliability, relevance, adaptability, expertise, and biases.

Reliable - The data must to be depended on to be reliable. It should be exact, steady with actualities and obvious. Lacking or inaccurate data for the most part prompts choices of low quality.

activities system-wide must be kept to a minimum. Finally, the system must yield high quality information. Quality is a major aspect of data integrity as this is the element of the system focused on giving current as well as accurate and consistent data at any time and in every location information needs to be accessed.

Abstract: Data Quality mostly mention about the quality of the data. Mainly the data considers to be of high quality. This research paper explains about how the Data Quality has the control over the large observation data which has brought many challenges to researchers. It also explains about how Data Quality Monitors data through the user defined algorithms and gives the analysis how the data is being processed. It clearly describes about the six features which ensure strategy planning for the data quality.

Data completeness is when the business should ensure that their client’s information is altogether filled in so it is finished and they will likewise need to ensure that their client's information is right generally their clients may get charged for something that they didn't commit, in light of the fact that they have the wrong information for that individual

The audience would be the data owners, data managers and IT personnel who would be responsible for the data quality (data administrator, operations manager or database admin).

This is a very important to the usability of the software. If the accuracy is off i.e. customer

Six Sigma methodologies focuses on reducing variation to eliminate defects and have a standard output with less inventory (Kubiak & Benbow, 2009). Implementation of Six

The concept of Six Sigma was developed in the early 1980’s at Motorola Corporation (Harry and Schroeder, 2000). Six Sigma can be defined as a statistical measure of the performance of a process or product (Kumi et. al., 2006). It is used as a quality control mechanism, which seeks to reduce defects or variations in a process to 3.4 per million opportunities thereby optimizing output and increasing customer satisfaction (Sambhe, 2012). Sigma is representing the standard deviation, a unit of measurement that designates the distribution or spread about the mean of a process (Six Sigma Academy, 2002). In addition, the Six Sigma uniquely driven by close understanding of customer needs, disciplined use of fact, data, and statistical analysis, and diligent attention to managing improving, and reinventing business processes (Pande, P., et. al. 2000). The Six Sigma methodology uses statistical tools to identify the factors that matter most for improving the quality of processes and generating bottom-line results. The Six Sigma DMAIC (Define, Measure,

Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimising variability in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organisation ("Black Belts", "Green Belts", etc.) who are experts in these methods. Each Six Sigma project carried out within an organisation follows a defined sequence of steps and has quantified targets. These targets can be financial (cost reduction or profit increase) or whatever is critical to the customer of that process (cycle time, safety, delivery, etc.).