Data Mining Case Study

An enterprise data warehouse (EDW) makes information accessible to the applications utilized as a part of offices all through the association including engineering, human resources (HR), and strategic planning. Norfolk Southern assembled a TOP dashboard

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)

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.

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.

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.

Many organizations want to implement an integrated enterprise warehouse that collects information about all subjects (e.g., customers, products, sales, assets, personnel) spanning the whole organization. However, building an enterprise warehouse is a long and complex process, requiring extensive business modeling, and may take many years to succeed. Some

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 warehousing is defined as the design and operation of processes and tools to manage and deliver complete, timely, accurate, and understandable data for decision making. It includes all the activities that make it possible for an organization to create, manage, and maintain a data warehouse or data mart. Data warehousing majorly deals with managing the development, the implementation, and the operation of a data warehouse or data store. It includes data management, data acquisition, data archiving, data cleansing, storage management, data integration, data distribution, security management operational reporting, analytical reporting, backup and recovery planning,

Moreover, its relation to the data warehouse turns the first pattern of development on its head. Here multiple data marts are parents to the data warehouse, which evolves from them organically. The third pattern of development attempts to synthesize and remove the conflict inherent in the first two. Here data marts are seen as developing in parallel with the data warehouse. Both develop from islands of information, but data marts don’t have to wait for the data warehouse to be implemented. It is enough that each data mart is guided by the enterprise data model developed for the data warehouse, and is developed in a manner consistent with this data model. Then the data marts can be finished quickly, and can be modified later when the enterprise data warehouse is finished. These three patterns of data mart development have in common a viewpoint that does not explicitly consider the role of user feedback in the development process. Each view assumes that the relationship between data warehouses and data marts is relatively static. The data mart is a subset of the data warehouse, or the data warehouse is an outgrowth of the data marts, or there is parallel development, with the data marts guided by the data warehouse data model, and ultimately superseded by the data warehouse, which provides a final answer to the islands of information problem. Whatever view is taken, the role of users in the dynamics of data warehouse/data

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

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

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).