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BA Framework, Anaytics and types newest Farmeowrk.pptx

  1. BUSINESS ANALYTICS FRAMEWORK John Christopher V. Reguindin, MIS Faculty STI-West Negros University
  2. 4 Layers of Business Analytics Framework: 1. Data Layer 2. Analytics Layer 3. Reporting and Visualization Layer 4. Access Layer
  3. Framework for Business Analytics 3
  4. Data Layer 4 Sources of Data Where data is being transformed Data warehouse is a copy of transaction data specifically structured for query and analysis localized data warehouses, are small-sized data warehouses, typically created by individual divisions or departments to provide their own decision support activities.
  5. Analytics Layer 5 In this layer, data from Data Warehouse/Data Mart are analyzed by using descriptive, predictive, or prescriptive analytics.
  6. Analytics Layer 6 Various techniques used in this layer: A. Data Mining – The process of exploration and analysis, by semi-automatic or automatic means, of huge quantities of data in order to discover meaningful patterns and rules The technique that includes management science, statistical, mathematical and financial models and methods, used to find the vital relationships between variables in the historical data, perform analysis on the data or to forecast from data.
  7. Analytics Layer 7 Various techniques used in this layer: B. Multidimensional Data Analysis Also known as Online Analytical Processing (OLAP), it is part of the wider variety of business intelligence software that enables managers, executives, and analysts to gain insight into data through rapid, reliable, collaborative access to a wide range of multidimensional views of information. It also allows business analysts to rotate data, changing the relationships to get more detailed insight into corporate information.
  8. Reporting/Visualization Layer 8 Various tools used in this layer: Dashboards, Balance Scorecards, Reports, Ad hoc Reports and Alert
  9. TYPES OF ANALYTICS John Christopher V. Reguindin, MIS Faculty STI-West Negros University
  10. 4 Types of Analytics : 1. Descriptive 2. Diagnostic 3. Predictive 4. Prescriptive.
  11. Descriptive Analytics 11 - Explains what happens - Gives information about the past performance or state of a business and its environment by using existing data - Helps companies to gain insight from historical data with reporting, scorecards, clustering and to look at the facts like, what has happened, where, and how often.
  12. Diagnostic Analytics 12 - Explains why it happens - focuses on past performance to determine the answer to the questions like why it is happening or why something happened. - gives companies deep insight into a problem by techniques such as drill-down, data discovery, data mining, etc. to find out dependencies and to discover patterns from the historical data.
  13. Predictive Analytics 13 - Forecast what may happens - determine the probable future outcome for an event, or the likelihood of the situation occurring and identify relationship patterns. - Its objective is to understand the causes and relationships in the data to make accurate predictions.
  14. Prescriptive Analytics 14 - Recommends an Action based on Forecast - helps to choose the best possible outcome by evaluating a number of possible outcomes. - Combination of descriptive and predictive models together with probabilistic and random methods such as Bayesian models or Monte Carlo Simulation to assist in the determination of the best course of action based on various “what if” scenario assessments.