Data Warehouse, Data Mart and Business Intelligence - Essay Example

?Running head: Computer Sciences and Information Technology Data Warehouse, Data Mart and Business Intelligence Insert Insert Grade Insert Tutor’s Name 13 March 2012 Data Warehouse, Data Mart and Business Intelligence Introduction Many organizations are increasingly adopting data warehousing to enhance reporting and decision making. A data warehouse facilitates the integration of data from various sources, data sharing, and provides consistent, organized, relevant and timely information for decision-making. This discussion explores the differences between data warehouses and databases, data warehouse technologies, and the relationship between data warehousing and business Intelligence. Data Warehouses, Data Marts and Databases A data warehouse refers to a data storage location used to secure, archive, and analyze data. It comprises of many integrated databases in an organization. Data stored in a data warehouse must be easily accessible to facilitate the daily operations of an organization. There are several types of data ware houses. There are offline operational data warehouses where data is copied from real time data networks and stored offline. Offline data warehouses store integrated data that is frequently updated and can be easily accessed. Real-time data warehouses are updated whenever new data comes in, for example in point of sale systems. Integrated data warehouses can be accessed by other systems (Jensen, Pedersen, and Thomsen, 2010). Data marts refer to smaller data warehouses covering a specific department or subject. They differ from data warehouses in that they are less complex, and are easier to develop and maintain. Data warehouses also focus on many subject areas and collect their data from various sources while data marts deal with one subject and collect data from few sources. There are dependent and independent data marts. Dependent data marts source their data from a functional central data warehouse while independent data marts get data from external sources. A data mart can be a small division of a data warehouse (Jensen, Pedersen, and Thomsen, 2010). A database refers to a collection of organized information for easy access. There are several types of databases such as relational, distributed, and object-oriented programming databases. Databases contain records of data that can be easily accessed. While databases are designed to record and store data, data warehouses are designed to respond to critical business queries. All data warehouses are databases but few databases can be considered to be data warehouses. Databases are usually online transaction processing systems for recording transactions while data warehouses are online analytical processing systems for querying and analyzing data (Jensen, Pedersen, and Thomsen, 2010). Data Warehouse Architectures and Tools Data warehouses are developed using several steps including data collection, data cleansing, data aggregation, and analysis and presentation. Data collection involves identifying the suitable data for the warehouse and where it can be sourced from. In data cleansing and transformation, the collected data is restructured to make it usable for reporting, querying, and analysis. Data aggregation and analysis involves the use of query tools to transfer data from the central data warehouse and processing it to produce the required results. Presentation involves giving end results to the users in form of text, charts or tables (Barry, 2003). There are various data warehouse architectures varying from one organization to another depending on their data. These architectures include independent data marts, hub-and-spoke, federated, centralized data warehouse and data mart bus architecture that has linked dimensional data marts. Independent data marts architecture involves developing autonomous marts with different data definitions, measures and dimensions. Data bus mart with linked dimensional data marts architecture is designed to meet the needs of a specific business process. It involves the development of one data mart using specific dimensions and measures and other data marts are developed later using those dimensions and measures. Then they are all integrated logically. The hub-and-spoke architecture is designed to meet the needs of an extensive enterprise. Data is stored in the warehouse based on subject areas and dependent data marts are used to draw data from the data warehouse. The centralized data warehouse architecture resembles the hub-and-spoke but has no dependent data marts. Data is accessed from dimensional and relational views. Federated architecture involves the use of existing data from data marts and data warehouses, which is integrated to develop a new data warehouse (Barry, 2003). In developing a data warehouse, several tools are used. These tools have different purposes and they include Extraction, Transformation and Loading (ETL) tools, On-line Analytical Processing (OLAP) tools, data mining tools, report tools and database management systems. Some ETL tools include IBM WebSphere DataStage, Informatica PowerCenter, Teradata Parallel Transporter and SAS ETL Studio. OLAP tools include DB2 OLAP Server, Oracle Discoverer, SQL Server Analysis Services, BusinessObjects OLAP Intelligence and SAS OLAP Server. Some of the report tools include Oracle Reports, Cognos ReportNet, Crystal Reports Server, and SQL Server Reporting Services. Data mining tools include Oracle Data Miner, IBM Intelligent Miner, SAS Enterprise Miner and Teradata Warehouse Miner. Database management systems include DB2, Microsoft SQL Server, Oracle Database, Teradata Database and Sybase IQ (Barry, 2003). Data Warehousing and Business Intelligence Business Intelligence is the use of technologies and applications to collect and analyze data, and provide information about the operations of an organization to facilitate decision-making. Business Intelligence is the ability of an organization to easily access and analyze information to facilitate effective and strategic decision making and therefore, have an edge over the competitors. Business Intelligence enables managers, analysts, and executives to make effective decisions quickly. A data warehouse is storage for an organization’s historical data. Business Intelligence and data warehousing are related in that the former refers to available information for decision-making while the latter facilitates the achievement of Business Intelligence. Business Intelligence is achieved by analyzing data in a data warehouse. Data warehousing enables data storage and Business Intelligence involves the management of this data for decision-making. Business Intelligence tools are used to query data that is meaningfully stored in a data warehouse (Simon and Shaffer, 2001). Conclusion It is clear from the above discussion that data warehousing has various benefits to organizations including providing timely information to support decision-making. Data warehouses differ from databases in that the latter is mainly concerned with data storage while the former is involved in responding to business queries. Business Intelligence is dependent on data warehouses to access and analyze information for decision-making. Reference List Barry, D. K. (2003). Web Services and Service-Oriented Architecture: The Savvy Manager's Guide. USA: Morgan Kaufmann. Jensen, C. S., Pedersen, T. B. and Thomsen, C. (2010). Multidimensional Databases and Data Warehousing. USA: Morgan & Claypool Publishers. Simon, A. R. and Shaffer, S. L. (2001). Data Warehousing and Business Intelligence for E-Commerce. USA: Morgan Kaufmann. Read More