Data Mining and Data Warehousing

Introduction to Data Warehousing and Data Mining

Today's economy is one big, continuous flow of data. One's ability to collect, analyze, and use data to benefit their company is an invaluable asset. The process of compiling and organizing data into one common database is data warehousing. The data mining process relies on the data compiled in the data warehousing phase in order to detect meaningful patterns.

Data Warehousing

A data warehouse is a database used to store data. It is a central repository of data in which data from various sources is stored. This data warehouse is then used for reporting and data analysis. It can be used for creating trending reports for senior management reporting, such as annual and quarterly comparisons. The purpose of a data warehouse is to provide flexible access to the data for the user. Data warehousing generally refers to the combination of many different databases across an entire enterprise.

Data warehousing emphasizes the capture of data from diverse sources for useful analysis and access, but does not generally start from the point-of-view of the end user who may need access to specialized, sometimes local databases. The latter idea is known as the data mart. There are two approaches to data warehousing: top-down and bottom-up. The top-down approach spins off data marts for specific groups of users after the complete data warehouse has been created. The bottom-up approach builds the data marts first and then combines them into a single, all-encompassing data warehouse.

Typically, a data warehouse is housed on an enterprise mainframe server or, increasingly, in the cloud. Data from various online transaction processing (OLTP) applications and other sources is selectively extracted for use by analytical applications and user queries. The term data warehouse was coined by William H. Inmon, who is known as the Father of Data Warehousing. Inmon described a data warehouse as being a subject-oriented, integrated, time-variant, and nonvolatile collection of data that supports management's decision-making process (Inmon, 1996).

Data Mining

Data mining is actually the analysis of data. It is the computer-assisted process of digging through and analyzing enormous sets of data that have either been compiled by the computer or have been inputted into the computer. In data mining, the computer will analyze the data and extract the meaning from it. It will also look for hidden patterns within the data and try to predict future behavior. Data mining is mainly used to find and show relationships among the data. The purpose of data mining, also known as knowledge discovery, is to allow businesses to view these behaviors, trends, and/or relationships and to be able to factor them within their decisions. This allows businesses to make proactive, knowledge-driven decisions.

The term ‘data mining’ comes from the fact that the process of data mining, i.e., searching for relationships between data, is similar to mining and searching for precious materials. Data mining tools use artificial intelligence, machine learning, statistics, and database systems to find correlations between the data. These tools can help answer business questions that traditionally were too time-consuming to resolve (Han, Kamber, & Pei, 2012).

Data mining includes various steps, including the raw analysis step, database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. In data mining, association rules are created by analyzing data for frequent if/then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is how frequently the items appear in the database, while confidence is the number of times if/then statements are accurate.

Other data mining parameters include Sequence or Path Analysis, Classification, Clustering, and Forecasting. Sequence or Path Analysis parameters look for patterns where one event leads to another later event. A Sequence is an ordered list of sets of items, and it is a common type of data structure found in many databases. A Classification parameter looks for new patterns and might result in a change in the way the data is organized. Classification algorithms predict variables based on other factors within the database.

Clustering parameters find and visually document groups of facts that were previously unknown. Clustering groups a set of objects and aggregates them based on how similar they are to each other. There are different ways a user can implement the cluster, which differentiate between each clustering model. Fostering parameters within data mining can discover patterns in data that can lead to reasonable predictions about the future, also known as predictive analysis.

Applications and Benefits of Data Mining

Data mining techniques are used in many research areas, including mathematics, cybernetics, genetics, and marketing. While data mining techniques are a means to drive efficiencies and predict customer behavior, if used correctly, a business can set itself apart from its competition through the use of predictive analysis. Web mining, a type of data mining used in customer relationship management, integrates information gathered by traditional data mining methods and techniques over the web. Web mining aims to understand customer behavior and to evaluate how effective a particular website is (Berry & Linoff, 2004).

Other data mining techniques include network approaches based on multitask learning for classifying patterns, ensuring parallel and scalable execution of data mining algorithms, the mining of large databases, the handling of relational and complex data types, and machine learning. Machine learning is a type of data mining tool that designs specific algorithms from which to learn and predict. In general, the benefits of data mining come from the ability to uncover hidden patterns and relationships in data that can be used to make predictions that impact businesses. Specific data mining benefits vary depending on the goal and the industry. Sales and marketing departments can mine customer data to improve lead conversion rates or to create one-to-one marketing campaigns. Data mining information on historical sales patterns and customer behaviors can be used to build prediction models for future sales, new products, and services.

Companies in the financial industry use data mining tools to build risk models and detect fraud. The manufacturing industry uses data mining tools to improve product safety, identify quality issues, manage the supply chain, and improve operations.

Conclusion

The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database, whereas data mining is the process of extracting meaningful data from that database. Data mining can only be done once data warehousing is complete.

References:

• Berry, M. J. A., & Linoff, G. (2004). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. John Wiley & Sons.
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Elsevier.
• Inmon, W. H. (1996). Building the Data Warehouse. John Wiley & Sons.