A Data Warehousing (DW) is the process for collecting and managing data from varied sources to provide meaningful business insights. It is typically used to connect and analyze business data from heterogeneous sources. It the core of the BI System which is built for data analysis and reporting.
It is an Electronic storage of a large amount of information by a business which 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 in a timely manner to make a difference.
Data Warehousing provides architectures and tools for business executives to systematically organize, understand, and use their data to make strategic decisions. It is a valuable tool in today’s competitive. fast evolving world. Many industry feel that data warehousing is a marketing weapon that help in retaining customer by learning more about their needs.
Data Warehousing provides architectures and tools for business executives to systematically organize, understand, and use their data to make strategic decisions. It is a valuable tool in today’s competitive.
fast evolving world. Many industry feel that data warehousing is a marketing weapon that help in retaining customer by learning more about their needs.
The basic operations of data warehousing are to extract data from the operational systems, to include relevant data from outside sources like magazines, journals, reports of other organizations in the same industry, to remove inconsistencies and transform and clean the data, to store the data in such a way so that it is for easy access for decision making etc.
According to William H. Inmnon, a leading architect in the construction of data warehouse systems, “A Data Warehouse is a subject-oriented, integrated, time- variant, and nonvolatile collection of data in support of management’s decision making process.”
These four characteristics of data warehouses distinguish them from other data repository systems, such as relational database systems, transaction processing systems, and file systems
Applications of Data Warehousing :
Data Warehousing can be applicable where we have huge amount of data and we want to see statistical results that help in decision making. There can be many applications in different sectors like E-Commerce, Telecommunication, Transportation Services, Marketing and Distribution, Healthcare and Retail. Let us discuss few of them.
Social Media Websites –
The social networking websites like Facebook, Twitter, Linkedin etc. are based on analyzing large data sets. These sites gather data related to members, groups, locations etc. and store it in a single central repository. Being large amount of data, Data Warehouse is needed for implementing the same.
Banking –
Most of the banks these days use warehouses to see spending patterns of account/card holders. They use this to provide them special offers, deals, etc. and to provide feedback to bankers regarding customer relationships and profitability.
Government –
Government uses data warehouse to store and analyze tax payment which is used to detect tax thefts. Also to maintain and analyze health policy records, to share data with other entities, like insurance companies, NGOs, and medical aid services.
Finance –
Used for evaluation of customer expenses trends, maintain transparency in transactions, predict defaulters and act accordingly, analyze and forecast different aspects of business, stock and bond performance etc.
Education –
Used to store and analyze information about faculty and students, to maintain student portals to facilitate student activities, to extract information for research grants and assess student demographics, to integrate information from different sources into a single repository for analysis and strategic decision-making etc.
Characteristics of Data Warehouse :
- Subject-oriented –
One of the key features of a data warehouse is the orientation it follows. Data warehouses focus on major subjects like customer, supplier, product, sales, revenue etc. and not on ongoing and current/day-to-day operations of organization data. This enables it to be used for data analysis which is a key element of decision making.
Data warehouses are designed to help you analyze data. For example, to learn more about your company’s sales data, you can build a warehouse that concentrates.on sales. Using this warehouse, you can answer questions like “Who was our best customer for this item last year?” This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented.
- Integrated/Collaborated –
A data warehouse’s core is its integration of data from several different sources which aren’t homologous (heterogeneous) in nature, for example, flat files, relational databases, on-line transaction records and other such sources. This plays a key role in enhancing the efficiency of data analysis.
A data warehouse’s core is its integration of data from several different sources which aren’t homologous (heterogeneous) in nature, for example, flat files, relational databases, on-line transaction records and other such sources. This plays a key role in enhancing the efficiency of data analysis.
- Time-variant –
What’s the importance of data without a time-stamp? Data uploaded into a warehouse can be identified with a certain timeline making it a multidimensional historical view (past few years) whenever you access data.
In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing (OLTP) systems, where performance requiremnents demand that historical data be moved to an archive. A data warehouse’s focus on change over time is what is meant by the term time-variant.
- Nonvolatile –
The data in a warehouse is of the non-volatile type which ensures that your previous data is not lost as new data is updated which separates them for operational databases which are subject to frequent changes.
Nonvolatile means that, once entered into the warehouse, data should not change. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.
Due to separation of physically stored data and application data from operational environment, a data warehouse does not require transaction processing, recovery and concurrency control mechanism.
Activities like delete, update, and insert which are performed in an operational application environment are omitted in Data warehouse environment. It usually requires only two operations in data accessing: data loading and data access.
- No Additional Controls –
As the warehouse is maintained separate and has a separate storage from the operational databases, it doesn’t require any concurrency controls, tweaks in processing, recovery mechanisms.
- How is the organization using the information from datawarehouses?
Many organizations use this information to support business decision making activities, including Increasing customer focus, which includes the analysis of customer buying patterns (such as buying preference, buying time, budget cycle).
Repositioning products and managing product portfolios by comparing the performance of sales by quarter, by year, and by geographic regions in order to find tune production strategies Analyzing operations and looking for sources of profit Managing customer relationships.
Data Warehousing the process of constructing and using data warehouses. The construction of a data warehouse requires data cleaning, data integration, and data consolidation.
The utilization of a data warehouse often necessitates a collection of decision support technologies. This allows “knowledge workers” (e.g., managers, analysts, and executives) to use the warehouse to quickly and conveniently obtain an overview of the data, and to make sound decisions based on information in the warehouse.
The term data warehousing is used to refer only to the process of data warehouse construction, while the term warehouse DBMS is used to refer to the management and utilization of data warehouses.
Data warehousing provides an alternative of traditional approach. Rather than using a query-driven approach, data warehousing employs an update-driven approach in which information from multiple, heterogeneous sources is integrated in advance and stored in a warehouse for direct querying and analysis.
Unlike online transaction processing databases, data warehouses do not contain the most current information. However, a data warehouse brings high performance to the integrated heterogeneous database system because data are copied, preprocessed. integrated, annotated, summarized, and restructured into one semantic data store.
Additionally, query processing in data warehouses does not interfere with the processing at local sources. Moreover, data warehouses can store and ìntegrate historie information and support complex multidimensional queries.
Data warehousing requires both business and technical expertise and involves the following activities:
Accurate identification of business information that must be stored in the warehouse. Identification and prioritization of subject areas to be included in it. Defining the scope of each subject area.
Development of a scalable architecture. Selection of the hardware/software/middleware components needed. Extracting, cleansing, aggregating, transforming, and validating the data to ensure accuracy and consistency. Providing user-friendly, powerful tools to the users with which they can gain access to the data warehouse.
Giving adequate training to the users. Establishing a data warehouse helpdesk to support the users in their day-to-day tasks. Establishing procedures for maintenance and enhancement of the data warehouse.
