Assignment is due on 30 May. It has two sections one essay written document and video presentation . I want you to make written document which is worth of 15% and i want you to help me in video as...

Introduction
The interest in business intelligence and decision support has gained momentum owing to the improvements these concepts have introduced in organization performance. BI systems offer support for collecting and transforming data and analyzing it for improved decision making. This paper aims at discussing the basic concepts of BI and DSS technologies. It further discusses the Data warehousing (DW) technology and its impact on consumer applications, creating new opportunities for adopting organizations to grow. The technology of DW has been explained using the case study of Starwood Hotels and Resorts. The company faced a problem due to which its profitability was impacted and DW has been discussed as an appropriate solution for the problem.
The following sections discuss the technology od Data Warehousing in detail. This includes the components of DW, architectures and processing approaches used in this technology. The benefits of the technology for offering support in decision and enhancing business intelligence are further mentioned. The problem faced by managers of Starwood Hotels is clearly stated along with proposed solution through which data is analyzed in real time and the managers are able to take operational decision without any time delays thus increasing overall profitability of the organization. The report further includes a reflection of the research on the technologies mentioned above and the learnings through this course.
Data Warehousing
Data Warehousing is a process of collection and management of data from varied sources in order to offer meaningful business insights into the data. A business intelligence system built for data analysis and reporting, needs data warehousing at its core. Large amount of data is electronically stored by businesses in a way so that it can be used for query and analysis rather than transaction processing. Such databases help in decision support. DW receives data from different sources like transactional system and other relational databases in structured, semi-structured or unstructured form. This data is extracted, transformed and loaded for the users into a comprehensive database to be accessed using Business Intelligence tools. Components of DWs include Enterprise Data Warehouse (EDW), Operational data store (ODS), Data Mart and metadata. EDW is a centralized warehouse that organizes and represents data in a unified manner. It holds historical and current data in an organized and cleansed form, which can potentially be of interest to organizations. Data Mart is a subset of DW with some relevant data. Metadata represents the background information that is used by every industry in different ways. Metadata can be created to select what should be included or it can be automatically generated on the basis of data. The third component of DW, operational data store is a central database that provides data for operational reporting from multiple transactional systems. Data from various sources can be combined in its original form into a single destination so that it can be made available for business reporting. DW architecture can be constructed using three approaches, single tier, two-tier and three-tier architectures. The 3-tier architecture is most widely used in which the bottom layer is RDBMS where data is cleansed, transformed and loaded. The middle tier is an Online Application Processing server which provides abstracted view of DB and mediates between the end-user and the DB. The top tier includes the API from where data is extracted from the data warehouse using tools like data mining tools, reporting tools, etc.
Data Warehousing uses data sourcing, transformation and migration tools to make changes to the data and transform it into a unified format in DW. These ETL tools anonymize data as per regulatory stipulations, remove unwanted data from getting loaded into DW, calculates summaries and derived data and removes duplicated data received from different sources.
An active data warehouse is a DW designed to provide real time operational decision support and involves event driven actions and queries designed to operate on big data continuously. A traditional DW stores historic data from heterogeneous sources...