Data Warehouses, Decision Support and Data Mining

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.

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 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.

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).

A data warehouse (DW) is the collection of processes and data whose primary purpose is to support the business with its analysis and decision-making. In other words, it is not just one thing, but a collection of many different parts. Data Warehousing has become an essential part of a successful company. Data is constant and is advantageous when utilized in the correct way. It has become evident within the company the need for encompassing the concept of data warehousing, and how data warehousing and analytics, once incorporated as part of business intelligence for within the company, would be lead to optimal solution. When introducing a new product to market it is important to develop a strategy that can blueprint the potential for success. Elements to consider are design, development, and implementation of the data warehouse as to collect information for various data mining projects.

Data Warehouse Systems “DWHs” are platforms used to report and analyse the data by integrating data from different disparate sources and creating a central repository of data "data warehouse". This is achieved through storing the current and the historic data to create reports for management or technical reporting based on some pre-defined configurations.

What drives the OLAP system is the multidimensional nature of the business problem. These problems are characterized by retrieving a very

In the early '90s, data warehousing applications were either strategic or tactical in nature. Trending and detecting patterns was the typical focus of many solutions. Now, companies are implementing data warehouses or operational data stores which meet both strategic and operational needs. The business need for these solutions usually comes from the desire to make near

Answer: The term data warehouse is often used to refer to a system that extracts data from one or more sources, in order to transform and store in a model suitable for presentation and analysis. It can also be used to refer to just the database used in the aforementioned type of system. There are two main approaches to building a data warehouse, the Kimball approach and the Inmon approach.

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.

Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone, Ph.D. White Paper No. Three March 27, 1997

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

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

Businesses today continue to strive and grow in the industry to keep up with the never ending changes in the business they need the tools to obtain information that can be used to make decisions for the business. The decisions to make in a business can consist of knowing what geographic region to focus on, which product lines to expand, and what markets to strengthen in the industry. To obtain the type of information that has the proper content and format that can assist with strategic decisions they turned to data warehousing. It became the new paradigm intended specifically for vital strategic information.

Data mart is a simple form of a data warehouse that is focused on a single subject, such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data. De-normalization is the norm for data modeling techniques in this system. Online Analytical Processing or OLAP is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. OLAP system response time is an effectiveness measure that is used by Data Mining techniques. OLAP databases store aggregated, historical data in multi-dimensional schemas. OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day. Online Transaction Processing or OLTP is characterized by a large number of short on-line transactions. OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments. OLTP systems the number of transactions per second measures effectiveness; this contains detailed and current data. The schema used to store transactional databases is the entity model. Normalization is the norm for data modeling techniques in this system. Predictive Analysis is about