Lecture 1_ Introduction (W1).pdf

CS-ELEC1C: Data Warehousing Introduction to Data Warehouse What is a Data Warehouse? Why is it relevant?  What is a Data Warehouse? Why is it relevant? It is a central repository of integrated data from one or What is a Data more disp...

CS-ELEC1C: Data Warehousing Introduction to Data Warehouse What is a Data Warehouse? Why is it relevant?  What is a Data Warehouse? Why is it relevant? It is a central repository of integrated data from one or What is a Data more disparate sources across an organization. It is the Warehouse? backbone for reporting, data analytics, decision support. “One of the most important assets of any organization is its information.” Why is a Data Warehouse important? Ensure consistency. Data warehouses are programmed to apply a uniform format to all collected data. Standardizing data from different sources also reduces the risk of error in interpretation. Make better decisions. Successful leaders develop data-driven strategies and rarely make decisions without consulting the facts. Data Warehousing improves the speed and efficiency of accessing different data sets Improve their bottom line. Data warehouses allow leaders to quickly access their organization's historical activities and evaluate initiatives that have been successful or unsuccessful in the past.  What is a Data Warehouse? Why is it relevant? Over time, there are a few recurring themes that organizations have when it comes to data… Some organizational pain points: “We collect tons of data, but we can’t access it.” “We need to slice and dice the data every which way.” “Business people need to get at the data easily.” “Just show me what is important.” “We spend entire meetings arguing about who has the right numbers rather than making decisions.” “We want people to use information to support more fact-based decision making.”  What is a Data Warehouse? Why is it relevant? If you convert the previous statements into requirements… Goals and Objectives of a Data Warehouse The DW/BI system must make information easily accessible. The contents of the DW/BI system must be understandable. The data must be intuitive and obvious to the business user, not merely the developer. The DW/BI system must present information consistently. The data in the DW/BI system must be credible. Data must be carefully assembled from a variety of sources, cleansed, quality assured, and released only when it is fit for user consumption. The DW/BI system must adapt to change. User needs, business conditions, data, and technology are all subject to change. The DW/BI system must be designed to handle this inevitable change gracefully so that it doesn’t invalidate existing data or applications  What is a Data Warehouse? Why is it relevant? If you convert the previous statements into requirements… Goals and Objectives of a Data Warehouse The DW/BI system must present information in a timely way. As the DW/ BI system is used more intensively for operational decisions, raw data may need to be converted into actionable information within hours, minutes, or even seconds. The DW/BI system must be a secure bastion that protects the information assets. An organization’s informational crown jewels are stored in the data warehouse. The DW/BI system must serve as the authoritative and trustworthy foundation for improved decision making. The data warehouse must have the right data to support decision making. The most important outputs from a DW are the decisions that are made based on the analytic evidence presented The business community must accept the DW/BI system to deem it successful. It doesn’t matter that you built an elegant solution using best-of-breed products and platforms. What is a Data Warehouse? Why is it relevant?  How is it different from databases? By Definition… Databases use Online Transactional Processing (OLTP) to delete, insert, replace, and update large numbers of short online transactions quickly. Data Warehouses use Online Analytical Processing (OLAP) to analyze massive volumes of data rapidly. This process gives analysts the power to look at your data from different points of view. How to build a Data Warehouse?  What are we going to study this semester? Main Topics: Concepts and Architectures for Data Warehouses Data Modelling Life Cycle Dimensional Data Modelling Extraction, Transformation, and Load Visualization and Descriptive Analytics Emerging Technologies