Business Intelligence Presentation 1 (15th March'16)
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Business intelligence (BI) involves methods, processes, technologies, and tools to convert data into useful information that helps organizations make better plans and decisions. It has evolved from executive information systems and decision support systems in the 1980s to include data warehousing, dashboards, analytics, and big data capabilities today. BI provides benefits like improved management and operations, better adjustments to trends, and the ability to predict the future. It has applications across private and public sector organizations. The BI process involves requirements analysis, data modeling, ETL, analytics, and presentation. Key components are the data warehouse, OLAP, data mining, and visualization tools like reports, dashboards, and scorecards. The global BI market is expected to grow significantly
Business Intelligence Presentation 1 (15th March'16)
- 2. Agenda What is business intelligence (BI)? Values, capabilities, potential BI evolution and trend -Chronological hierarchy, history, trend Why BI? -Benefits, Necessities, Application areas BI Technical Overview -Process, Components, servers BI process and system Components, technologies, and applications BI tools, products, industry, and market 2
- 4. What is Business Intelligence? Business Intelligence is an umbrella term for a set of - methods, - processes, - technologies, and - tools that help us to convert data into information, information into knowledge and knowledge into plans that guide the organizations for its very betterment, traditionally known as Decision Support System (DSS). 4
- 5. Evolution of BI The search for the perfect business insight system 1980s Executive information systems (EIS), decision support systems (DSS) 1990s Data warehousing (DW), business intelligence (BI) 2000s Dashboards and scorecards, performance management 2010+ Analytics, big data, mobile BI, in-memory cache, data science 5
- 6. Why BI? BI is for answering following business related questions technically- 6 What happened? Why did it happen? What is happening? What will happen? What do I want to happen? Past Present Future
- 7. Benefits of Business Intelligence Improve Management Processes planning, controlling, measuring and/or changing results in increased revenues and reduced costs. Improve Operational Processes fraud detection, order processing, purchasing.. Better Adjustment settings Competitor analysis, adjustments settings to changing trends. Predict The Future Predictive analysis, Forecasting. 7
- 8. BI Application Areas BI can be applied in all businesses both private and public sector Private Retail, manufacture, real-estate, sports, media, publication, etc. Public (non-profit) Education, government, healthcare, association, etc. 8
- 9. Sample BI Application Areas Business management 1. Strategic planning 2. Benchmarking IT management 1. Web analytics 2. Security management Logistics 1. Supplier & vendor management 2. Shipping and inventory control City planning 1. Traffic management 2. Urban Analytics Education 1. Learning analytics 2. Institutional effectiveness Internet and web 1. Social analytics 2. Sports and games analytics 9
- 10. BI: Complete Technical Overview 10
- 11. 11 BILC (Business Intelligence Life Cycle)
- 12. BILC (Business Intelligence Life Cycle) Analyze Business Requirements - reviewing business requirements to determine the types of analysis user need to perform. Design Data Model - Based on the business requirements, design the logical data model, which shows the information that users want to analyze and the relationships that exists within the data. Design the Physical Schema - Using the data model design physical schema (creating dimension and fact table hence star schema) which defines the content and structure of the data warehouse. Build the Data Warehouse - Build the data warehouse according to the schema design and load data into the warehouse (Developing RPD with 3 layers accordingly- Physical, Logical/BMM, Presentation layers) from source systems through ETL. Create the Project Structure (Metadata) - Create the metadata and begin to connect and map the metadata to table in the data warehouse e.g. Develop The BI Objects - Develop object, like reports, scorecards and dashboard. Administer and Maintain the Project - Administer and maintain the project as it undergoes continued development and changes, monitor performance and make adjustments to improve it, manage security, and perform other ongoing administrative tasks. 12
- 13. BI system 13
- 14. Data Management A special database system called Data Warehouse or Data Mart (a subset of Data Warehouse) is often used to store enterprise data The purpose of a data warehouse is to organize lots of stable data for ease of analysis and retrieval. Large databases that aggregate data collected from multiple sources Enterprise level data are coming from multiple different sources, but finally combined into Data Warehouse Operational databases Spreadsheets Text, CSV PDF, Paper 14
- 15. Data Warehouse CRM ERPHR Call Center Web Apps Finance Inventory 15
- 16. 16
- 18. BI - Analysis Tools Basic querying and reporting - Tell me what happened. Structured and fixed format reports Based on simple and direct queries Usually involves simple descriptive analysis and transformation of data, such as calculating, sorting, filtering, grouping, and formatting Ad hoc query and reporting - Tell me what happened when they need. Similar to operational reporting but on a need basis Business analysis(OLAP) - Tell me what happened with why. A multi-dimensional analysis and reporting application for aggregated data Great for discovering details from large quantities of data Business analytics (BA) is the practice of iterative, methodical exploration of an organizations data with emphasis on statistical analysis. Data mining - Tell me what might happen or Tell me something interesting. Data mining techniques are a blend of statistics and mathematics, and artificial intelligence and machine-learning. 18
- 19. OLAP CUBE OLAP is an acronym for online analytical processing, refers to multi-dimensional array which is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than 3. OLAP slicing: Slice is the act of picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimensions.The picture shows a slicing operation: The sales figures of all sales regions and all product categories of the company in the year 2004 are "sliced" out of the data cube. 19
- 20. OLAP Functionalities OLAP dicing: The dice operation produces a sub cube by allowing the analyst to pick specific values of multiple dimensions. The picture shows a dicing operation: The new cube shows the sales figures of a limited number of product categories, the time and region dimensions cover the same range as before. OLAP Drill-up and drill-down: Drill Down/Up allows the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down).The picture shows a drill-down operation: The analyst moves from the summary category "Outdoor- Schutzausr端stung" to see the sales figures for the individual products. 20
- 21. Roll-up: A roll-up involves summarizing the data along a dimension i.e. processes data on the level of sub totals and totals (aggregation) within a database,. The summarization rule might be computing totals along a hierarchy or applying a set of formulas such as "profit = sales - expenses". OLAP pivoting: Pivot allows an analyst to rotate the cube in space to see its various faces. For example, cities could be arranged vertically and products horizontally while viewing data for a particular quarter. Pivoting could replace products with time periods to see data across time for a single product. The picture shows a pivoting operation: The whole cube is rotated, giving another perspective on the data. 21 OLAP Functionalities
- 22. OLAP vs OLTP 22
- 23. Data Mining Data mining (or, knowledge discovery in database - KDD) Processes and techniques for seeking knowledge (relationship, trends, patterns, etc.) from a large amount of data Extremely large datasets Data mining applications use for sophisticated statistical and mathematical techniques to find patterns and relationships among data Classification, clustering, association, estimation, prediction, trending, pattern, etc. Common techniques Neural network, genetic algorithm, machine learning 23
- 24. Presentation Reports A report is the presentation of data transformed into formatted and organized information according to specific business requirements. Based on simple and direct queries: usually involves simple analysis and transformation of data (sorting, calculating, filtering, filtering, grouping, formatting, etc.) Reports can be static or interactive. But most reports are ready for printing. Visualization An essential way for human understanding and sense making In the forms of table, charts, diagrams Visualization can also be part of the analysis process (visual analytics) Dashboard A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance. Ability to identify trends and Gain total visibility of all systems instantly at one place 24
- 27. Dashboards 27
- 28. 28 Dashboards
- 30. 30 Dashboards (Maps & Multimedia)
- 31. Different Users of BI 31
- 32. BI Future Market 32 The global Business Intelligence and Analytics Software Market is expected to grow from $17.90 billion in 2014 to $26.78 billion by 2019, at a Compound Annual Growth Rate (CAGR) of 8.4%.