How Data Warehouse Is It Improves The Productivity By Redesigning Business Process And Work

This data is collected and organized in order to process orders and maintain good customer service. The logical view of data would allow a knowledge worker to arrange and access information based on the needs of the business separating it from the physical view of how information is arranged and stored. The ability to do this allows for an employee to create detailed reports in order to determine information such as customer information and their order numbers and dates. This is imperative for a company like Comcast who has over 27 million customers in order to have a system to keep important data to analyze. Using a data warehouse allows them to gather from several databases and then the company can use the information to determine for example how many units of voice products are sold to create the necessary business intelligence to make future decisions and remain

One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

One crucial thing that organizations need to consider in today’s unstructured data world is to successfully integrate data warehouses. For this, the companies need to re-consider their enterprise data architecture and classify the governance strategy that can be talented through such efforts. There lies a need for data managers

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

Data warehousing is defined as the design and operation of processes and tools to manage and deliver complete, timely, accurate, and understandable data for decision making. It includes all the activities that make it possible for an organization to create, manage, and maintain a data warehouse or data mart. Data warehousing majorly deals with managing the development, the implementation, and the operation of a data warehouse or data store. It includes data management, data acquisition, data archiving, data cleansing, storage management, data integration, data distribution, security management operational reporting, analytical reporting, backup and recovery planning,

The data warehouse comes ready for use, but an organization has to get prepared to use it. The main factor is data warehouse usage. A data warehouse can be used for decision making for management staff.

Data warehouse are multiple databases that work together. In other words, data warehouse integrates data from other databases. This will provide a better understanding to the data. Its primary goal is not to just store data, but to enhance the business, in this case, higher education institute, a means to make decisions that can influence their success. This is accomplished, by the data warehouse providing architecture and tools which organizes and understands the

As your business evolves, the data warehouse may not meet the requirements of your organization. Organizations have information needs that are not completely served by a data warehouse. The needs are driven as much by the maturity of the data use in business as they are by new technology.

Data warehouse is a central repository integrating data from various operating systems for validation of data, prediction etc .Data Warehouse is a relational database used for analysis and query rather than transactional database. It is used to collect historical data from various sources, integrate, analyze a particular subject, report. Data warehouse is time variant i.e one can retrieve any older data and once data enters data warehouse it cannot change [1]. According to Ralph Kimball Data warehouse is “copy of transaction data constructed for analysis and query”[5]. Data is taken from various sources like marketing, sales, ERP etc.

The success of the database and data warehouse (DW) project really depends on the quality of data. If data quality is not good enough, the information will logically be unreliable when the business users retrieve it from the database/DW environment. Good quality of data will be useful for the decision maker to make the right decision, gain more trust and make the organization more efficient. In contrast, the bad quality of data will drive the decision maker to make a wrong decision.

Data warehouse (DW of DWH) also called enterprise data warehouse (EDW) refers to the system utilized in the analysis and reporting of data. The can be described as the main component making up business intelligence. Normalized data warehousing describes the repositories containing integrated data form several dissimilar sources. It contains information which can be utilized in creating investigative reports for the various users within an organization. Examples of reports that can be retrieved from these repositories include annual and periodic trends of sales within the organization. The data contained in these sources is uploaded form the operational systems and hence can be utilized in making accurate reports regarding the operations. Before the data can be used for reporting purposes it could pass through operational data stores. This reports presents summaries of researches conducted in topics seeking to describe various normalized models of data warehousing. The research covers the topics indicated in the table below

A Data Warehouse is a database-centric system of decision support technologies used to consolidate business data from many disparate sources for use in reporting and analysis (Data Warehouse). Data Warehouses and Data Warehouse systems are primary used to server executives, senior management, and business analysts with accurate, consolidated information from various internal and external sources to aid in the process of making complex business decisions (Data Warehouse Process).

A Data WareHouse is a type of database normally used by large companies to store large amounts of data in and have the data be easily accessible. They are normally set up in one of three set-ups. The basic model that takes data straight from it sources, such as operational systems and flat files. The Staging Mode that has a staging area that takes the data, from the systems and files before moving it to data warehouse. The Final type adds data marts, a small database that takes specific information from the data warehouse, to the previous model between the data warehouse and the end users. Data Warehouses are also really useful because they make it easy to pull data from either queries or data mining. Data warehouses are a useful tool when dealing with large amounts of data.

Every organizations have their own independent Data Warehouse and due to increase in the number of transactions, the size of the data is also increasing. Data warehouse is the central repository of information for an organization. There are multiple data sources like OLTP, excel, csv, txt, xml, etc, that are generated from various systems and are populated to data warehouse by ETL and thus Data Warehouse stores the summarized integrated business data in a central repository. The Data Warehouse is used for the analytical applications (OLAP – On-Line Analytical Processing), decision making, data mining and user applications.

The data in a data warehouse comes from operational systems of the organization as well as from other external sources. These are collectively referred to as source systems. The data extracted from source systems is stored in a area called data staging area, where the data is cleaned, transformed, combined, deduplicated to prepare the data for us in the data warehouse. The data staging area is generally a collection of machines where simple activities like sorting and sequential processing takes place. The data staging area does not provide any query or presentation services. As soon as a system provides query or presentation services, it is categorized as a presentation server. A presentation server is the target machine on which the data is loaded from the data staging area organized and stored for direct querying by end users, report writers and other applications. The three different kinds of systems that are required for a data warehouse are: