The Return on Investment of Data Warehousing Essay

The purpose of data warehousing is to combine all of a company 's data and allows users to access the data directly, create reports, and obtain responses to

The data warehouse contains all the information that both the chain managers as well personnel can access. This information helps them see which products are selling, how much, where more important points of sales are, which are needed in inventory and which items needs to be checked for quality etc. Similarly these databases also contain solid information about consumers such as what is the ratio of repeat customers, what age group needs to be targeted for advertising, which new group is emerging and how to stay in touch with consumers about new products and sales.

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.

Data management is vital to any business as this is a key tool to an organisations business improvement, as you can refer back to data, and compare them against benchmarks. Analysing data can provide evidence for possible future structure such as identify trends, as well as indicate where improvements can be made. However there are strict procedures to be followed when collecting and storing data.

What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data

A data warehousing is defined as a collection of data designed to support management decision making. Data warehouses contains a wide variety of data that present a coherent picture of the business conditions at a single point in time. Development of a data warehouse includes development of the systems that extract data from operating systems plus the installation of the warehouse database system that provides managers flexible access to the data. The term data warehousing generally refer to the combination of many different databases across an entire enterprise. (webopidia)

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.

The state of affairs in the field of data warehousing and offers a variety of approaches to

Enterprise Data Warehouses (EDW) have become the foundation of many enterprises' systems of record, serving as the catalyst of strategic initiatives encompassing Customer Relationship Management (CRM), Supply Chain Management SCM) and the pervasive adoption of analytics and Business Intelligence (BI) throughout enterprises. The role of databases continues to be an ancillary one, supporting the overall structural and data integrity of the EDW and increasing its value to the overall enterprise (Phillips, 1997). The advances made over the last decade in the areas of Extra, Transact & Load (ETL) have made it possible to create EDW frameworks and platforms more efficiently, creating greater accuracy in overall database and data warehouse performance as a result (Ballou, Tayi, 1999). The creation and use of an EDW to further drive an organization to its objectives requires that the differences between databases and data warehouses be defined, in addition to a clear, concise definition of just what data warehouse technologies are. Finally, the relationship between data warehouses and business intelligence (BI) including analytics needs analysis and validation. Each of these three areas are discussed in this analysis.

In the future, the development is more focused on Big Data where the requirement of availability of information increase directly with the complexities of decision making increase, thus the requirement of data infrastructure need larger and more analytically to align with knowledge and decision-supporting technologies (Hosack et al., 2012). Increasing information available to KMDSS through data warehouse capabilities may be useful to the several industries. DSS has been on the forefront not only of new technologies, but of new ways to address existing business problems and processes. The nature of DSS is to continuously improve the decision-making processes that, in turn, improve the efficiencies of

In Philip Russom’s webinar he provides an overview of what a Data Warehouse (DW) modernization is, why many users’ DWs need modernization. The top five most common reasons for DW modernization including: Advanced Analytics, Scale, Speed, Productivity and Cost Control, what is the result from modernization, and his recommendations

For example, if we consider their Data warehouse, the Nike maintains the Teradata with extended configuration and purchased space more than their data needs. Also at the same time, they built individual Datamart with different technologies like SQL Server, Oracle based on the business user’s choice and level of reports they run. This improves their reporting speed, which is capable of running complex business scenarios and generating the data over millions of records. These reports will be used by the business users to take critical business decisions, sometimes required to take overnight or over couple of hours.

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

Data warehousing is an efficient system which store the past as well as current data used for creating reports. Data warehousing system is used for decision making by analyzing the reports. A data warehouse is a relational database, which is designed for analysis and query. It helps an organization to consolidate and analyze data from different sources and make decision. A data warehouse environment consists of OLAP (On-Line Analytical Processing) engine, ETL (Extraction, Transformation and Loading) process, client analysis tools and other applications that manage gathering and delivering the data.

Mainly, in the data warehouse analyzing the large data helps the decision-making process. Indeed, in the data warehouse, the integration of the data from the