Uses of Data Warehousing
Data warehousing serves two purposes. First and foremost, it serves as a historical repository for integrating the information and data that the organisation needs, which may come from various sources. Second, it acts as a query execution and processing engine for that data, allowing end-users to interact with data in the database.
Complex queries are difficult to conduct without pausing database update processes for a short time period. Data errors and gaps will unavoidably result from a regularly interrupted transactional database. As a result, a data warehouse acts as a separate platform for aggregating data from numerous sources and then performing analytics on that data. Because of this separation of roles, databases may focus solely on transactional tasks without interruption.
The following are the most common areas where data warehouses are used:
- Airline: It is utilised in the airline system for operational purposes such as crew assignment, route profitability studies, frequent flyer programme promotions, etc.
- Banking: It is commonly utilised in the banking industry to properly manage the resources available on the desk. A few banks also used it for market research, product performance analysis, and operations.
- Healthcare: Data warehouses were also employed in the healthcare sector to strategise and anticipate outcomes, generate patient treatment reports, and communicate data with tie-in insurance companies, medical aid services, and other organisations.
- Insurance and Investment sector: Warehouses are generally utilised in this industry to examine data patterns, customer trends, and market movements.
- Hospitality Industry: This industry uses warehouse services to plan and budget for advertising and promotion campaigns that target specific clients based on their feedback and travel patterns.
- Retail chains: DWHs are largely utilised in the retail industry for tracking items, examining pricing policies, keeping track of promotional deals, and analysing customer purchasing trends. Retail chains commonly use EDW systems to meet their business intelligence and forecasting demands.
Features of Data Warehouse
The following are some of the essential characteristics of a data warehouse:
- Subject Oriented: Instead of providing information about the entire organisation's ongoing operations, it focuses on a specific subject. These subjects might include product information, sales data, customer and supplier information, etc.
- Integrated: It is created by merging data from various sources, such as flat files and relational databases, to provide a more comprehensive data analysis.
- Time-Variant: Because the data in a DWH provides information from a given historical moment in time, it is classed with a specific time frame.
- Non-volatile: Historical data that is not discarded when current data is added is called non-volatile data. Because a data warehouse is distinct from an operational database, any regular updates in the operational database are not visible in the data warehouse.
Frequently Asked Questions
What is Business Intelligence?
The procedural and technical infrastructure that gathers, stores, and analyses the data generated by a company's activities is known as business intelligence (BI). Data mining, process analysis, performance benchmarking, and descriptive analytics are all part of business intelligence. BI parses all of a company's data and displays it in easy-to-understand reports, metrics, and trends that help managers make better decisions.
What is data mining?
Companies utilise data mining to transform raw data into meaningful information. Companies might learn more about their customers by employing software to seek trends in massive batches of data. This helps them design more successful marketing campaigns, boost sales, and lower expenses. Effective data collection, warehousing, and computer processing are all required for data mining.
What are the types of data warehousing?
The types of data warehousing are as follows:
→Enterprise Data Warehouse (EDW): A centralised warehouse that provides decision-making support to various departments across an organisation is known as an enterprise data warehouse. It offers a unified approach to data organisation and representation. It also can classify data by subject and grant access based on such divisions.
→Operational Data Store: Operational Data Stores (ODS) are data stores that are used when neither a data warehouse nor an OLTP system can meet a company's regulatory requirements. The data warehouse in ODS does real-time updation. As a result, it's widely utilised for routine duties such as keeping employee details.
→Data Mart: Data Mart, as part of a data warehouse, is tailored to a specific business line, such as finance, accounts, sales, purchases, or inventory. You can collect data straight from the sources using the warehouse.
Name the advantages of data warehousing.
The benefits of data warehousing are:
→It aids in decision-making and provides fact-based analysis of prior organisations' performance.
→It acts as a repository for relevant facts from the past.
→For optimal utility, it can be shared among critical departments.
Name the disadvantages of data warehousing.
The disadvantages of data warehousing are:
→The warehouse requires a lot of resources to build and maintain.
→Input errors can potentially compromise the integrity of the data being archived.
→The utilisation of several sources can lead to data discrepancies.
Conclusion
In this article, we learned about the meaning and working of data warehousing. We also saw the uses and features of data warehousing.
We hope this blog has helped you enhance your knowledge. If you want to learn more, check out our articles on Data Warehousing - Coding Ninjas Coding Ninjas Studio, Data Warehouse Modeling - Coding Ninjas Coding Ninjas Studio, Architecture of the Data Warehouse - Coding Ninjas Coding Ninjas Studio, Data Warehousing Tools - Coding Ninjas Coding Ninjas Studio and Snowflake - Coding Ninjas Coding Ninjas Studio. Do upvote our blog to help other ninjas grow.
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