Data Mining vs Data warehousing – Which One Is More Useful

Difference Between Data Mining and Data warehousing

Data are the collection of facts or statistics about a particular domain. Processing this data gives us the information and insights to add business values or to perform research. When the collected data are stored in a warehouse for processing, it is termed as Data Warehousing. Applying some logic to the data stored in the warehouse is called Data mining. let’s understand both Data Mining and Data warehousing in a detailed in this post.

Head To Head Comparisons Between Data Mining vs Data warehousing (Infographics)

Below is the top 4 comparisons between Data Mining and Data warehousing

Key Differences Between Data Mining vs Data warehousing

The following is the difference between Data Mining and Data warehousing

1.Purpose
Data Warehouse stores data from different databases and make the data available in a central repository. All the data are cleansed after receiving from different sources as they differ in schema, structures, and format. After this, it is integrated to form the integral and commonly available data store. It is performed in such a way that it handles and stores data periodically and systematically to organize the data from various sources.
Data mining is done on the transactional data or current data, to get knowledge about the present scenario of the business. The statistics generated as the result of mining give the clear picture about the trends. These trends can be pictorially represented using reporting tools.

2.Operations
Data warehouse Operations: OLAP
Online Analytical Processing is done on the data stored in the data warehouse.
Different categories of OLAP are ROLAP, MOLAP, HOLAP.
•ROLAP: Stores the Relational Database data for applying queries on the data stored.
•MOLAP: Stores the Multi-dimensional Data. E.g. Array can be stored and queried.
•HOLAP: Stores the Hybrid Data. This is generally for handling the raw data from multiple stores. It supports slice, dice, roll-up, drill-down operations for faster and optimized data mining.

OLAP (Data Warehouse)	Data Mining
It collects data and provides summary level insights about the data.	It identifies the hidden pattern and provides the detailed information.
It is used to identify the overall behavior of the system E.g.: overall profit attained in the year 2018	It is used to identify the behavior of the particular module. E.g.: profit attained in the Feb month in the year 2018
It is aimed at storing huge volume of data.	It is aimed at identifying the patterns present in the data to provide information.
It is used for improving operational efficiency.	It is used for improving the business and to make decisions.
Applied in reporting operations.	Applied in Business strategies.
Predictive Analysis cannot be performed.	Predictive Analysis is possible.

Data Mining Operation:
Generally, Data Mining is done on the data by compiling it using some logical operations. This is achieved by the implementation of Algorithms such as Associative Rules, clustering, and classification. It is used to identify the patterns from the data to identify the benefits and stats of the business.
1.Classification Analysis: It is used for classifying the data into different classes. Data Analyst classifies the data based on the knowledge acquired.
2.Association Rule Learning: It is used to identify the hidden pattern in data to reveal the customer behavior, change in business and all forecasting process.
3.Outlier Detection: The unmatched data sometimes shows up some pattern that may help in improving the business. Those data helps in detection of a fault, event and fraud identifications.
4.Clustering Analysis: The degree of association between the data is very high and they are clustered under same category or group. The data with similar behavior will fall into the same place.
5.Regression Analysis: The process of identifying the relationship among the data. All these data can be summarized to get some new information.
Both Data warehousing and Data mining help in analyzing the data and standardizing it.It improves the performance of the system with low latency for query processing and faster report generation process.

3.Benefits

Data Warehousing	Data Mining
Faster access to data	Faster data processing by use of algorithms
Increased System Performance	Increased throughput
Easy handling of huge data by distributed storage	Easy to generate reports for analysis
Data Integrity	Data Analytics

 Data Mining vs Data warehousing Comparision Table

Data Warehousing	Data Mining
Collecting and Storing of data from different sources.	Analyzing the patterns in the collected data.
Data are stored periodically	Data are analyzed regularly
Size of data stored is huge	Mining is performed with a sampling of data
Types: Enterprise Warehouse Data Mart Virtual Warehouses	Types: Machine Learning Algorithm Visualization Statistics.

Conclusion – Data Mining vs Data warehousing

•Warehousing helps the business to store the data, Mining helps the business to operate and take major decisions.
•Warehousing is started from the initial phase of any of the projects whereas mining is performed on the data as per demand.
•Warehousing ensures secrecy of data, on the other hand, mining sometimes leads to data leakage.
•Data Availability may differ based on the load supported by the warehouse; Mining does not have any issues related to data availability.
•Compiling of data requires special tools in data warehousing.
•There are so many algorithms available to mine the data if the analyst has the in-depth knowledge of data efficiently data can be handled and analyzed.

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