Differentiate between Data-Warehouse and Data-mining.

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 Differentiate between Data-Warehouse and Data-mining.

Data-Warehouse 

  • A data warehouse is a technique for collecting and managing data from varied sources to provide meaningful business insights. It is a blend of technologies and components which allows the strategic use of data.
  • Data Warehouse is the electronic storage of a large amount of information by a business that is designed for query and analysis instead of transaction processing. It is a process of transforming data into information and making it available to users for analysis.

Data-mining

  • Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets. Data Mining is all about discovering unsuspected/ previously unknown relationships amongst the data.
  • It is a multi-disciplinary skill that uses machine learning, statistics, AI, and database technology.
  • The insights extracted via Data mining can be used for marketing, fraud detection, and scientific discovery, etc.

DIFFERENCE
  • Data mining is considered a process of extracting data from large data sets, whereas a Data warehouse is the process of pooling all the relevant data together.
  • Data mining is the process of analyzing unknown patterns of data, whereas a Data warehouse is a technique for collecting and managing data.
  • Data mining is usually done by business users with the assistance of engineers while Data warehousing is a process that needs to occur before any data mining can take place.
  • Data mining allows users to ask more complicated queries which would increase the workload while Data Warehouse is complicated to implement and maintain.
  • Data mining helps to create suggestive patterns of important factors like the buying habits of customers while Data Warehouse is useful for operational business systems like CRM systems when the warehouse is integrated.
                                               OR,
Data Mining
  • Data mining is the process of analyzing unknown patterns of data.
  • Data mining is a method of comparing large amounts of data to find the right patterns.
  • Data mining is usually done by business users with the assistance of engineers.
  • Data mining is considered a process of extracting data from large data sets.
  • One of the most important benefits of data mining techniques is the detection and identification of errors in the system.
  • Data mining helps to create suggestive patterns of important factors. 
  • Like the buying habits of customers, products, sales. So that, companies can make the necessary adjustments in operation and production.
  • The Data mining techniques are never 100% accurate and may cause serious consequences in certain conditions.
  • The information gathered based on Data Mining by organizations can be misused against a group of people.
  • After successful initial queries, users may ask more complicated queries which would increase the workload.
  • Organizations can benefit from this analytical tool by equipping pertinent and usable knowledge-based information.
  • Organizations need to spend lots of their resources for training and Implementation purposes. Moreover, data mining tools work in different manners due to different algorithms employed in their design.
  • The data mining methods are cost-effective and efficient compares to other statistical data applications.
  • Another critical benefit of data mining techniques is the identification of errors that can lead to losses. Generated data could be used to detect a drop-in sale.
  • Data mining helps to generate actionable strategies built on data insights.


Data Warehouse
  • A data warehouse is a database system that is designed for analytical instead of transactional work.
  • Data warehousing is a method of centralizing data from different sources into one common repository.
  • Data warehousing is a process that needs to occur before any data mining can take place.
  • On the other hand, Data warehousing is the process of pooling all relevant data together.
  • One of the pros of Data Warehouse is its ability to update consistently. That’s why it is ideal for the business owner who wants the best and latest features.
  • Data Warehouse adds extra value to operational business systems like CRM systems when the warehouse is integrated.
  • In the data warehouse, there is a great chance that the data which was required for analysis by the organization may not be integrated into the warehouse. It can easily lead to the loss of information.
  • Data warehouses are created for a huge IT project. Therefore, it involves a high maintenance system that can impact the revenue of medium to small-scale organizations.
  • Data Warehouse is complicated to implement and maintain.
  • Data warehouse stores a large amount of historical data which helps users to analyze different time periods and trends for making future predictions.
  • In a Data warehouse, data is pooled from multiple sources. The data needs to be cleaned and transformed. This could be a challenge.
  • A Data warehouse allows users to access critical data from a number of sources in a single place. Therefore, it saves users time in retrieving data from multiple sources.
  • Once you input any information into the Data warehouse system, you will unlikely to lose track of this data again. You need to conduct a quick search, which helps you to find the right statistical information.



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