The Core Concepts Of Data Warehouse System

Real-time data warehousing creates some special issues that need to be solved by data warehouse management. These can create issues because of the extensive technicality that is involved for not only planning the system, but also managing problems as they arise. Two aspects of the BI system that need to be organized in order to elude any technical problems are: the architecture design and query workload balancing.

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.

Business Intelligence (BI) is the consolidation and analysis of internal data and / or external data for the purpose of effective decision-making. At the core of all BI initiatives is a data warehouse to hold the data and analytics software. The data warehouse stores data from operational systems in the organization and restructures it to enable queries and models to extract decision support reports.

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)

The value of Business Intelligence increases as the delivery of information is embedded in the processes and systems of the enterprise which results from all data being store for end users. The BI software seems to be very efficient in its

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 Base 2 (DB2) Warehouse is IBM’s BI software. The BI software focuses on data warehousing, consolidating data from unrelated sources and forms a “single version of the truth” (IBM.com, 2007), available to users through a variety of analyses. DB2 Warehouse is built to manage mixed workload, run queries concurrently, and also pre-aggregate related data for improved query performance. DB2 Warehouse capabilities include modeling, data mining and visualization, and embedded analytics and database management tools. DB2 has an integrated compression that has proven a savings of 45-69% disk space, and a workload control that automatically prioritizes and schedules queries. In addition to basic data warehousing and mining, IBM has prepackaged solutions for specific industries: banking, retail, insurance, healthcare, telecommunications and law enforcement. DB2 Warehouse will serve clients running Microsoft® Windows® XP or 2000, and run on servers with any of the following operating systems: IBM AIX® 5L™, Red Hat Enterprise Linux® 3 and 4, SUSE Linux Enterprise Server 9, Sun Solaris 9, Microsoft® Windows® Server 2003. It is compatible with two web browsers: Microsoft® Internet Explorer and Mozilla Firefox.

Business Intelligence is all about decision making with a data driven approach. It is a broader term and a super set containing tools, architectures, technologies and techniques that leverage insights from data (various problems or business processes) for analysis, querying, reporting and performance improvement. It is based on measuring or analyzing the historical data and past performance for driving insight and answers to questions like “What occurred?”, “How frequently is occurred?” and “Its impact?”. It includes processes and techniques for data collection, data analysis, data sharing, and reporting using dashboards, illustrative reports and interactive visualizations that laymen understand, all in the service better data driven decision making.

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

The concept of data warehousing goes back to the late 80s when IBM researchers Barry Devlin and Paul Murphy developed "business data warehouse". To summarize, the concept of data warehousing was created to provide an architectural model for the flow of collection of data from various operational systems to the decision supporting environments. The concept attempted to solve the various technicalities associated with this flow of data, primarily the high costs associated with it. In the absence of a data warehousing, an enormous amount of redundancy was needed to support multiple decision support environments. In larger organizations it was usual for multiple decision support environments to operate on their own. Even

Data Warehousing also known in many industries as an Enterprise Data Warehouse is a system that contains a central repository of integrated data, often collected from multiple sources and is used to perform data analysis enabling the creation of detailed reports that contribute significantly to a corporation’s business intelligence. Data Warehousing emerged as a result of advances in the field of information systems over the last several decades. There are two major factors that drive the need for data warehousing in most organizations. First and foremost, businesses require an integrated, company-wide view of high-quality information to maintain and improve upon their strategic position. Secondly, information systems departments must separate information from operational systems to improve performance dramatically in managing company data. Critical to the success of a Data Warehousing system, Data mining allows for companies to create customer profiles, manipulate information easily, and provide knowledgeable access to the current state of their company. However, a reality that many companies often find out the hard way is that data mining and data warehousing does not work for them. As with many new tools or technology, companies may jump on the bandwagon without fully contemplating its potential weaknesses. In order to remain competitive in today’s business world, companies should consider implementing data warehouses, but only with

The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the “business data warehouse”. Data warehouse (DW) is an application which allowed you to execute ad-hoc queries; multi-dimensional analysis and query information by

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.

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