Difference between Data Warehouse and Data Mining

Difference between Data Warehouse and Data Mining

Data Warehouse:

Data Warehousing is a technique that is mainly used to collect and manage data from various different sources so as to give the business a meaningful business insight. A data warehouse is specifically designed for the purpose of support management decision. On the whole, a data warehouse consists of the following kinds of data.

1. Subject oriented data
2. Integrated data
3. Time variant data
4. Non- volatile data

In simple terms, data warehouse is defined as fast computer system which has a huge data storage capacity. The following diagram will be beneficial for understanding this term and how it functions.

There are three main types of Data Warehouses which are listed below:

1. Enterprise Data Warehouse (EDW): Enterprise Data Warehouse is a centralized warehouse. It is used for organizing and representing the data. With the help of EDW the user can classify data on the basis of the subject.
2. Operational Data Store: In Operational Data Store, Data warehouse is refreshed in real time. Therefore, it is more commonly used for routine activities such as storing records and so on.
3. Data Mart: A data mart can be defined as a subset of the data warehouse. It is designed sales, finance, and so on.

Advantages of Data Warehouse:

1. Consumes less time I.e., saves times: A data warehouse can preserve and store data from specific sources. Also, the critical data is available to all the users which allows them to make decisions on the basis of all the important aspects.
2. Allows Historical Insights: With the help of this feature, the business has the access to the historical data as well. The records are kept safe using data warehouse.
3. Boosts Efficiency: Data Warehouse helps in boosting the efficiency. It is very time- consuming for a user to collect data for different sources. With the help of data warehouse, the user can get all the data collected in a single place. Also, it helps in running the data quickly.
4. Scalability: The term “scalable” refers to the capability to cope and perform well under an increased workload and data warehouse in itself is scalable. On top of that, it also enables greater scalability in the business also.
5. Increase the power and speed of data analytics: With data warehousing, the users can get access to high powers and increased speed. This is helpful as it allows the users to have a major advantage in business.

Disadvantages of Data Warehouse:

1. Not Ideal for unstructured data: Data warehouse is not considered very ideal for unstructured data. The unstructured data refers to the data that is not organized in a pre- defined manner.
2. Cost and benefit ratio: This are one of the most commonly faced problems of data warehousing. The data warehousing is considered a very costly project and that is often not justified. As a result, data warehouse is not an ideal solution.
3. Compatibility with the existing system: The issue of compatibility with the existing data warehouse system is complicated. All the existing system functionalities that are engaged are regarded as complex.
4. Data kept online: In data warehouse, the software usually do not allow keeping the whole repository only after a specific duration. The data is then recorded and analyzed for future references as well.
5. Extra reporting: The data warehouse runs depending on the risk of the organization. It duplicates the data that existed in the sequencing of the database. The issue that arises in this case is that it takes a lot of time in case there is extra reporting to be done.

Applications of Data Warehouse:

Data warehouse is very common and popular. However, there are some areas or fields where data warehousing is mainly used. Those areas are given below:

1. Banking services
2. Airline services
3. Investment sector
4. Telecommunication
5. Retail sectors
6. Controlled manufacturing
7. Finance sector
8. Education sector
9. Government sector

Data Mining

Data mining can be defined as a process of collecting raw data and then turning that raw data into useful information so that it can be used further. In this process, the data patterns are analyzed in large parts of data with the help of using one or more software. Data mining is very useful and popular process and it is used in various different fields these days. In Data mining, the data mining tools are used for the process of building risk models and detecting the frauds.

With the help of the following diagram, one can understand how data mining works and the various processes it undergoes before producing the results.

There are two types of Data Mining tasks which are listed below:

1. Predictive: It is used for making predictions about values of data with the help of results from various data or on the basis of historical data.
2. Descriptive: The descriptive function only works with the general properties of data in a database. Below listed are descriptive functions:
3. Class/Concept Description
4. Mining of Frequent Patterns
5. Mining of Associations
6. Mining of Correlations
7. Mining of Clusters

Advantages of Data Mining:

1. Helps in establishing better relationship with the customer: With the help of data mining, the user can establish better relationship with the customer. It helps in knowing what is the type of approach needed with various products so that the customer will like it. With this, the sale of the product is guaranteed.
2. Finding and figuring out the market trends: Another benefit of data mining is that it helps to determine the correct trends using the market research. Moreover, it also gives the users access to predict trends and patterns. That way, the companies know what kind of products should be launched according to the popularity in market.
3. Anomaly detection: The results of the analysis are much more precise with the data mining. It is even possible to analyze databases with a large amount of data. With the help of the data mining, the banks and other financial institutions are able to receive information on loans and other important detail. On top of that, it also helps the credit card companies by giving them details about the frauds.
4. Benefits in Marketing/Retail: With the data mining, the various companies are able to build models on the basis of historical data and considering the results, marketers have an apt approach on how to sell the products profitably. In addition to that, it also helps in offering the discount for certain products in order to attract the customers in retail marketing.
5. Benefits in Government Department: The Government can analyze the records for any transactions so as to create and observe the patterns that helps in detecting the cases of money laundering with the help of data mining.

Disadvantages of Data Mining:

1. Security and Privacy Concerns: Data mining is not always the safest option. There are many security and privacy issues that arises in data mining. It even violates the privacy of a user. In addition to that, it has been noticed that there is a misuse of information several times. And on top of that, sometimes, inaccurate information is also uploaded.
2. Expensive: In the initial stages of Data mining, it is very expensive and hard on pockets of the users. It mainly draws expenses with its storage and maintenance facility. This is one of the main disadvantages of data mining.
3. Incorrect information is provided: From time to time in data mining, it is noticed that the information provided is often incorrect. In addition to that, the information sometimes also lacks the precision or accuracy. This may be because of the reason that when data mining tools analyze the data, they do not actually know it meaning. And the result produced is then presented in the form of various visualization. Also, sometimes the pre- processing errors are the reason behind the incorrect information.
4. Difficult to operate: This point is not witnessed a lot of time. But at some point, data mining analytics software are not very easy to operate. Moreover, it also requires the users to have knowledge based on the training.
5. Different tools, different algorithms: The different tools work in different ways. This is because of the different algorithms are used to make various designs. Therefore, it is very important to select the correct data mining tool.

Applications of Data Mining:

Data Mining is a very popular and commonly used process. So, it is evident that they are used in various fields. Some fields/areas where data mining is popularly used is listed below:

1. Financial Analysis
2. Telecommunication
3. Retail Industry or Sector
4. Higher Education
5. Fraud Detection in Banks, Government sectors
6. Various Scientific Applications
7. Spatial data mining
8. Energy Industry
9. Intrusion Detection

Key Differences between Data Mining and Data Warehouse:

Serial Number	Data Mining	Data Warehouse
1.	Data mining is a process of analyzing patterns and trends of data.	Data warehouse is a database system that does the analytic analysis and not the transactional work.
2.	Data is analyzed on regular basis.	Data is periodically stored.
3.	Data mining uses the logic of pattern recognition so as to correctly identify patterns.	Data warehouse extracts and then stores the data which ultimately allows easier reporting.
4.	It helps in extracting data from a large set of raw data.	It helps in retaining all the relevant and important data together.
5.	Data mining can never be 100% correct or accurate.	In data warehousing, due to some causes, the probability of losing information is very high.
6.	In data mining, business entrepreneurs or business users work together.	The process of data warehousing is only and entirely done by a group of engineers.
7.	The process mainly extracts useful patterns or trends from a big amount of data.	The process is mainly used for integrating data from multiple sources and it then combines it into a single database.
8.	Data mining techniques can be used with data warehouse for the purpose of discovering useful patterns.	It is used for providing a mechanism that can store huge amount of data without any problem.
9.	The main benefit of data mining is that it helps in detecting frauds, predicts trends, gives market analysis, and financial analysis.	Data warehouse is used for timely data access, enhanced response time, consistent data and easy access.

Conclusion:

In conclusion, data mining and data warehouse, both of them have their own set of advantages and disadvantages. Although, in spite of the fact that it is evident from this article that both the processes have very different pieces of task, it can be seen that data mining and data warehouse have many common applications, this draws the conclusion that the technologies are may be used together.

While, data mining is particularly used by business users or business entrepreneurs and engineers as well. Data warehousing is entirely carried out by a group of engineers. The main advantage of data mining is that it is beneficial to a great extent for fraud detection and hence, this process is popularly and commonly used in financial sectors and Government sectors. On the flip side, the main advantage of data warehouse is that it is scalable which is very helpful in this “cloud era”.

In general, it can be deduced that both the technologies give many features. What matters is the requirements of the user.