Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as surveillance, fraud detection and scientific discovery.
Data mining in customer relationship management applications can contribute significantly to the bottom line. Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer.
Data mining techniques can also be used to discover segments or groups within a data set. In recent years, data mining has been widely used in area of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.
Data mining techniques are also used to identify terrorist activity; the National Research Council provides the following definition: “Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity these patterns might be regarded as small signals in a large ocean of noise.”
Data mining can be applied to a decision support system to enhance long and short term planning capabilities. The applications for an effective data mining system are becoming increasingly complex. Healthcare and military entities see the benefits of data mining and continue to call on the team at Marjau Systems for our expertise.
Data mining systems are often incorporated into the following:
- Decision Support
- Rule Based Systems
- ETL Process
A decision support system is a data based information system that enables faster decision making, identification of negative trends and better allocation of resources. A decision support system serves the management, operations and planning levels of an organization and helps to make decisions, which may be rapidly changing and not easily specified in advance.
While many people think of decision support as a specialized part of a business, most companies have actually integrated decision support systems into their day to day operating activities. The key to decision support systems is to collect data, analyze and shape the data and then make sound decisions or construct strategies from analysis.
A clinical decision support system can enable medical diagnosis and can perform selected cognitive decision making when based on artificial intelligence. Decision support systems are prevalent in most organizations that require a long planning time frame including the military, transportation and forest management industries. Decision support has also been effect in ecosystem protection and sustainability programs. The foundations of a compelling decision support system are often rule based systems.
Rule Based Systems
A rule based system consists of a set of permanent data for which each occurrence of temporary data can be compared. A rule based system analyzes the data and appears to reason like a human being. As a result a rule based system is often considered the basis of artificial intelligence.
A rule based system can be developed to process data and answer complex questions that would normally be asked of a professional. As a result a rule based system can act as a doctor, tax advisor or military planner.
A rule based system differs from standard procedural or object oriented applications because there is no clear order in which code executes. Instead, the knowledge of the professional is captured in a set of rules, each of which encodes a small piece of knowledge into the system.
The advantage to this type of approach, as opposed to a procedural approach, is that the knowledge can be maintained and expanded over time. As a result of its ability to simulate human reasoning the rule based system is an efficient way to maintain a progressive knowledge base.
You need to load your data warehouse regularly so that it can serve its purpose of facilitating business analysis. To do this, data from one or more systems needs to be extracted and copied into the warehouse. The process of extracting data from sources and bringing it into the data warehouse is commonly called ETL Process, which stands for extraction, transformation, and loading.
The ETL Process involves:
- Extracting data from outside sources
- Transforming it to fit operational needs
- Loading it into the end target
ETL Processes can involve considerable complexity, and significant operational problems can occur when designed improperly. Design analysts should establish the scalability of an ETL Process across the lifetime of its usage. This includes understanding the volumes of data that will have to be processed within service level agreements. The time available to extract from source systems may change, which may mean the same amount of data may have to be processed in less time.
ETL Process tools have started to migrate into enterprise application integration, or even enterprise service bus, systems that now cover much more than just the extraction, transformation, and loading of data.
Designing and maintaining the ETL Process is often considered one of the most difficult and resource-intensive portions of a data warehouse project. Marjau Systems has been the chosen source to design effective ETL Process for organizations just like yours.
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