Analysis: Data Warehouses and Business Intelligence

An active data warehousing, or ADW, is a data warehouse implementation that supports near-time or near-real-time decision making. It is featured by event-driven actions that are triggered by a continuous stream of queries that are generated by people or applications regarding an organization or company against a broad, deep granular set of enterprise data. Continental uses active data warehousing to keep track of their company’s daily progress and performance. Continental’s management team holds an operations meeting every morning to discuss how their

Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an entity-relationship model. Operational system designers generally follow the Codd rules of database normalization in order to ensure data integrity. Relational databases are efficient at managing the relationships between these tables. The databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. Finally, in order to improve performance, older data are usually periodically purged from operational systems.

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.

This report is an analysis of business intelligence systems currently available to our business. As an introduction, I will address in general terms why we need to purchase a business intelligence system and how it will aid our business. Then I will discuss several applications in detail, paying particular attention to the information and analysis capabilities of each, and the hardware and software required for each. Finally, I will conclude with a short evaluation of the products discussed and offer a recommendation as to the best application for our business. I will pay particular attention to IBM, Microsoft, SAP, and Oracle.

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 report focuses on data mining approach to predict human wine taste preferences. A large data set is considered with white and red wine samples (“Vinho Verde” wine from Portugal). The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

A data warehouse and business intelligence application was created as part of the Orion Sword Group project providing business intelligence to order and supply chain management to users. I worked as part of a group of four students to implement a solution. This report reflects on the process undertaken to design and implement the solution as well as my experience and positive learning outcome.

Today businesses are investing many resources in building data warehouses and data marts to obtain timely and actionable information that will give them better business insight. This will enable them to achieve, among other things, sustainable competitive advantage, increased revenues and a better bottom line.

when required. At the end of the report, useful recommendations are also made which can be

A data warehouse is unique kind of a database where current and historical data about a certain group of people such as customers, is stored. Information from operational systems, such as transaction processing systems, is extracted and summarised then stored in in a data warehouse. This type of information includes records about customer interaction patens, customer purchasing history or trends and current customer records. The information in a data warehouse is used for management analysis and decision making.

complicated. It is a major mutual concern for all business and IT sector companies to change the existing situation of "mass data, poor knowledge" and support better business decision-making and help enterprises increase profits and market share. Business intelligence technologies have emerged at such challenging times. Business today has compelled the enterprises to run different but coexisting information systems.

This paper will discuss the main difference between the relational database optimized for on line transactions and a data warehouse optimized for processing and summarizing large amounts of data. Next this author will outline the difference database requirements for operational data for decision support data. Next this paper will describe three example in which databases could be used to support decision making in a large organizational environment. Lastly this author will describe three other examples in which data warehoused and data mining could be used to support data processing and trend analysis in a large organizational environment.

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

The process of where a data warehouse is fed with extracted source data is largely known as ETL (Extraction, Transforming and Loading). ETL is a critical process in the construction of a data warehouse project.

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.