What Is The Data Used In A Data Warehouse?

Warehousing includes managing relationships of data owners, data collectors, and data end-users to ensure that all aware of the available data in the inventory and accessible systems. This also helps to reduce redundant data collection

If I were to design Ben & Jerry's data warehouse I would use several dimensions of information. The first dimension would consist of the company's products; ice cream, frozen yogurt or merchandise. The marketing department has to know which products are selling, if Ben & Jerry's didn't know that their T-shirts are selling out as soon as they hit the stores, then they wouldn't be able to take advantage of the opportunity to sell the shirts. The second dimension would consist of the different areas of sales; US, Canada, Mexico, or Europe. I am not sure if they sell their ice cream in Mexico, but with data collection they can find out if their ice cream would be a better seller in the hot climate, rather than pushing for greater

1.     If I were to design Ben & Jerry’s data warehouse I would use several dimensions of information. The first dimension would consist of the company’s products; ice cream, frozen yogurt or merchandise. The marketing department has to know which products are selling, if Ben & Jerry’s didn’t know that their T-shirts are selling out as soon as they hit the stores, then they wouldn’t be able to take advantage of the opportunity to sell the shirts. The second dimension would consist of the different areas of sales; US, Canada, Mexico, or Europe. I am not sure if they sell their ice cream in Mexico, but with data collection they can find out if their ice cream would be a better seller in the hot climate,

"A data warehouse is a subject oriented, integrated, time variant, non-volatile collection of data in support of management 's decision making process". Source

Database management systems require that the organization acknowledges the strategic role of information by treating it as a corporate resource. Decision makers need concise, reliable information about current operations, trends and changes. Data, however are

In data warehousing, there is a distinction between a data warehouse and a data mart. A data warehouse collects information about subjects that span the entire organization, such as customers, items, sales, assets, and personnel, and thus its scope is enterprise-wide. For data warehouses, the fact constellation schema are commonly used since it can model multiple, interrelated subjects.

These changes could also drive a change in the culture of the company to become more data driven. If there was confidence in the data warehouse and the reporting coming from it as well as trust between the business departments and the data team this could drive improvement across the company. Discovering opportunities for improvement using data is the currently unrealized goal of the data warehouse. All improvements and changes and improvements outlines in this document have that goal in mind.

It helps an organization consolidate data from several sources by separating analysis workload from transactional workload. Additionally, a data warehouse environment includes ETL which is Extraction, Transportation and Loading solution, an OLAP which is Online Analytical Processing Engine, analysis tools and other tools so as to look over the process of gathering data and finally delivering it to business users. The data stored in these warehouses must be stored in a way which is reliable, secure, and easy to process and manage. The need for data warehousing arises as businesses become more complex and start generating and gathering huge amount of data which were difficult to manage in the traditional way.

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 is aggregation of subject-oriented, integrated databases, which is designed to confirm DSS support. Now days these repository has become a focal point for DSS in organisation. These data repository used for online analytical Processing (OLAP), data mining and support queries. Decisions which are pending from a long time get resolved by analysing data warehouses. Another benefit of data warehouse is it improves the productivity by redesigning business process and work. It is challenging and technical undertaking because data comes from different sources and systems. There are some other organisational issues like sponsorship maintenance, scope avoidance and political issues. Because of these reasons data warehouse project get

Data are a vital organizational resource that needs to be managed like other important business assets. Today’s business enterprises cannot survive or succeed without quality data about their internal operations and external environment. This growth drives corporations to analyze every bit of information that is extracted from huge data warehouses for competitive advantage. This has turned the data storage and management function into a key strategic role of information age.

A data warehouse (DW) can be acknowledged as one of the most complex information system modules available and it is a system that periodically retrieves and consolidates data from the sources into a dimensional or normalized data store. It is an integrated, subject-oriented, nonvolatile and a time-variant collection of data in support of management’s decisions (Inmon, 1993).

This database provides general reports for. E.g. Daily Sales report by product or by customer or by route. These reports are helpful— particularly for real-time reporting —but they don’t allow in-depth analysis. The possibilities for reporting and analysis are endless. When it comes to analyzing data, a static list is insufficient. There’s an intrinsic need for aggregating, summarizing, and drilling down into the data. A data warehouse enables you to perform many types of analysis:

Data is the raw materials of any information system. With the revolution of Information Technology we are improving our decision making process more quick and smart. Data warehouse technology is the process of collection, sorting, structural formation, analysis, storing and presentation of data. So we say that data warehouse is the technology is overall data management system in the organization.