Data Warehousing VS Data Mining – 4 Awesome Comparisons

Difference Between Data Warehousing and Data Mining

Corporate data is scattered across different databases in different formats. In order to achieve the end result, these pieces have to combine together to get a complete picture. The process of combining data from these heterogeneous sources will be cumbersome in absence of a Data Warehouse. A Data Warehouse is an environment where essential data from multiple sources is stored under a single schema. It is then used for reporting and analysis.

Data Warehouse is a relational database which is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data.

While a Data Warehouse is built to support management functions, data mining is used to extract useful information and patterns from data. The data mining can be carried with any traditional database, but since a data warehouse contains quality data, it is good to have data mining over data warehouse system.

Data Mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction.

Let us understand the Difference between Data Warehousing and Data Mining in detailed

Key Features:

1. Data Warehouse:

The key features of a Data Warehouse are discussed below:

1. Subject Oriented: A data warehouse is subject oriented as it provides knowledge around a subject rather than the organization’s ongoing operations. These subjects can be a product, customers, suppliers, sales, revenue, etc. A data warehouse focuses on modeling and analysis of data for decision making.
2. Integrated: A data warehouse is constructed by combining data from heterogeneous sources such as relational databases, flat files, etc.
3. Time-Variant: The data present in data warehouse provides information with respect to a particular time period.
4. Non-volatile: Non-volatile means, data once entered into the warehouse should not change.

Benefits of Data Warehouse:

1. Consistent and quality data
2. Cost reduction
3. More timely data access
4. Improved performance and productivity

Data Mining:

The key features of Data mining are discussed below:

1. Automatic discovery of patterns
2. Prediction of likely outcomes
3. Creation of actionable information
4. Focus on large data sets and databases

Benefits of data mining:

1. Direct marketing: The ability to predict who is most likely to be interested in what products
2. Trend analysis: Understanding trends in the marketplace is a strategic advantage because it helps reduce costs and timeliness to market.
3. Fraud detection: Data mining techniques can help discover which insurance claims, cellular phone calls or credit card purchases are likely to be fraudulent.
4. Forecasting in financial markets: Data mining techniques are extensively used to help model financial markets.

Head To HeadComparison Between Data Warehousing vs Data Mining (Infographics)

Below is the Top 4 Comparision Between Data Warehousing vs Data Mining

Key Differences between Data Warehousing vs Data Mining

Some of the major differences between Data Warehousing and Data Mining are mentioned below:

• Data Warehousing is the process of extracting and storing data to allow easier reporting. Whereas Data mining is the use of pattern recognition logic to identify trends within a sample data set, a typical use of data mining is to identify fraud, and to flag unusual patterns in behavior. For Example, Credit Card Company provide you an alert when you are transacting from some other geographical location which you have not used previously. This fraud detection is possible because of data mining.
• 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.
• Data warehouse is the repository to store data. On the other hand, data mining is a broad set of activities used to uncover patterns, and give meaning to this data.
• Data warehousing is merely extracting data from different sources, cleaning the data and storing it in the warehouse. Whereas data mining aims to examine or explore the data using queries.

For example A data warehouse of a company store all the relevant information of projects and employees. Using Data mining, one can use this data to generate different reports like profits generated etc.

• Data warehouse is an architecture whereas, data mining is a process that is an outcome of various activities for discovering the new patterns.
• A data warehouse is a technique of organizing data so that there should be corporate credibility and integrity, but, Data mining is helpful in extracting meaningful patterns those are not found, necessarily by only processing data or querying data in the data warehouse.
• Data warehouse contains integrated and processed data to perform data mining at the time of planning and decision making, but data discovered by data mining results in finding patterns that are useful for future predictions.
• Data warehouse supports basic statistical analysis. The information retrieved from data mining is helpful in tasks like Market segmentation, customer profiling, credit risk analysis, fraud detection etc.
• Data warehousing is the process of pooling all relevant data together, whereas Data mining is the process of analyzing unknown patterns of data.
• Data warehouses usually store many months or years of data. This is to support historical analysis. Data mining is the use of pattern recognition logic to identify trend within a sample data set.

Data Warehousing vs Data Mining Comparision Table

Conclusion – Data Warehousing vs Data Mining

Differences between data mining and data warehousing are the system designs, a methodology used and the purpose. Data warehousing is a process that must occur before any data mining can take place. A data warehouse is the “environment” where a data mining process might take place. Lastly, it can be said that a data warehouse organizes data effectively so that the data can be mined.

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