Welcome to the Business Intelligence and data warehouse course. We will complete it in six weeks, and supported with videos and exercises that will allow you to learn how to create an OLAP database system from multidimensional data model. You will learn how to extract, transform, and load data to a warehouse, program analytical queries with SQL, and execute predictive analysis. You will learn how to load relational or unstructured data to a Hadoop Distributed Systems, and how to execute MapReduce jobs to query data. This week, we will have an introduction to Business Intelligence and its main components. Let's start. Many organizations are analyzing current and historical data to identify useful patterns and support business strategies. Emphasis is on conflicts interactive exploratory analysis of very large datasets created by integrating data from across all parts of an enterprise. Decision support systems are part of a Business Intelligence project, and they use data and models to support management decision-making in different ways. Nowadays, what is used to be called decision support systems often comes under the umbrella of Business Intelligence. Then, we can say that Business Intelligence is a technology infrastructure with the purpose of improving business processes. The typical software components are those solutions for gathering, cleansing, integrating, analyzing, and sharing data. Business Intelligence produces analysis and provides believable information to help making effective and high quality business decisions. The most common kinds of Business Intelligence systems are; executive information systems, decision support systems, management information systems, geographic information systems, online analytical processing, and multidimensional analysis, and also customer relationship management systems. The Business Intelligence systems are based on data warehousing technology. A data warehouse gathers information from a wide range of company's operational systems, Business Intelligence systems based on it. Data loaded to a warehouse is usually good integrated and cleaned that allows to produce credible information which reflected an approach to the so-called 'one version of the true.' Now, we will see what are the components of a model development of a BI system. Business understanding, selection of data sources, multi-dimensional modeling, and data warehouse creation, data integration by extraction, transformation, and load process, analysis of data and visualization of reports from the analysis to make decisions. In general terms, the process of Business Intelligence begins with business understanding, answering questions such as how will I make money? How will I sell products? How will I manufacture the product or deliver the service? These answers allow to model the business process as the first step. Once you find the business model and business strategy, the second step would be to identify which information systems could fit the new OLAP system in terms of which data will allow taking business decisions. Data can come from questionnaires, transactions, census, bar codes, etc. Store or any functional areas such as sales and marketing or operations management, human resources, finances, etc. The third step is focused on multidimensional modelling. Data modeling includes designing a data warehouse in detail. It follows principles and patterns established in architecture for data warehousing and Business Intelligence. The term data warehousing refers to the process of analysis and design of a data warehouse to consolidate data from many sources in one large repository. The most common OLAP data models are Star and Snowflake. The fourth step corresponds to data integration, which is achieved by an ETL process, and it stands for extraction, transformation, and load. The main problem that arises during ETL is data quality. Because, as data goes from different data sources: redundancy, inconsistency, missing data are these kind of problems. They shall be addressed during ETL, where data are gathered from relevant sources, filtered and stored, and analyzed and arrange into meaningful patterns using different tools. Once useful and cleaned data has been installed on the already designed data warehouse, the fifth general step corresponds to analysis. In the case of basic Business Intelligence implementation, analysis is carried out with SQL queries. However, how is data analyzed? Data can be analyzed through OLAP complex SQL queries based on a spreadsheet-style operations and multidimensional view of data. This type of analysis is called descriptive analysis. As I said before, there are more analytic techniques and tools such as those provided by data mining with exploratory search for interesting trends and anomalies to achieve predictive analysis. Some examples of techniques are simulation, decision analysis, statistics such as average or correlations, linear programming like optimization, queuing theory for "waiting line" analysis, network analysis to maximize flow through a network such as supply chain, multi-criteria decision making for scoring models, predictive analysis, etc. Now, we know how data can be analyzed. They will need to present information in an easy way to be understood by users in order to take decisions. See you in the next video.
