Business Intelligence and Decision Making Notes

Decision Making Using Business Intelligence

Objective of Business Intelligence

The primary purpose of Business Intelligence is to support:

  • Timely decisions
  • Data-driven decisions
  • Objective Business Decisions

Business Decisions

  • What is the business question you are trying to answer?
  • Recalibrate and Reinforce Often!

Decision Making Process

The decision-making process involves several steps:

  • Identify and define the Problem
  • Determine possible solutions
  • Determine the criteria to evaluate the alternatives
  • Evaluation through Analysis
  • Choose the best alternative
  • Communicate your findings effectively

Business Decision Making

The types of business decisions can be categorized as:

  • STRATEGIC
  • TACTICAL
  • OPERATIONAL
Strategic Decisions
  • Typically Unstructured.
  • Involve higher-level issues concerned with the overall direction of the organization.
  • These decisions define the organization’s overall goals and aspirations for the future.
Tactical Decisions
  • Typically Semi-Structured.
  • Concern how the organization should achieve the goals and objectives set by its strategy.
  • They are usually the responsibility of mid-level management.
Operational Decisions
  • Typically Structured.
  • Affect how the firm is run from day to day.
  • They are the domain of operations managers, who are the closest to the customer.

Effective Communications

  • If you fail to effectively communicate your analysis and recommendations, you have failed to answer the business question!
  • Key elements: Audience, Message, Media, Timing

Business Decision Approaches

There are two main approaches:

Objective
  • Data-Driven
  • Quantitative
  • Measurable
  • Keywords: Numbers, Structured, Analytical
Subjective
  • Qualitative
  • Perception
  • Intuition
  • Keywords: Unstructured, Opinion, Exploratory

Subjective Decision Making

Involves:

  • Knowledge
  • Experience
  • Intuition
  • Intangibles
  • Ethical Issues
  • Facts
  • Risk
  • Luck

Objective Decision Making

Involves:

  • Analysis and Inferences
  • Data
  • Quantitative Analytics
  • Descriptive
  • Predictive
  • Prescriptive (Proactive)

Components of Business Intelligence

The components of Business Intelligence efforts include:

  • Business intelligence systems and platforms
    • Infrastructure for collecting, storing, analyzing data produced by business
    • Databases, data warehouses, data marts
  • Business analytics
    • Tools and techniques for analyzing data
    • OLAP, statistics, models, data mining
  • Business intelligence service providers
    • Companies and contractors that create business intelligence and analytics purchased by firms

Functional Scope of Business Intelligence

Business Intelligence Infrastructure includes:

  • Data from Business Environment (Call centers, Web data, Mobile devices, Social media data, Stores, etc.)
  • Databases, Data Warehouses, Data Marts, Analytic platforms
  • Business Analytics Toolset (Models, Data mining, OLAP)
  • Managerial Users and Methods (Reporting and query tools, Big Data analytics, Business strategy, Performance management, Balanced score card, Forecasts)
  • User Interface (Reports, Dashboards, Scorecards, MIS, Desktop, DSS, Mobile, Web portal, ESS, Social media)

Business Analytics

Categories of Business Analytics:

  • Descriptive
    • Describe past performance, based on established data
    • Examples: Financial Reports, Statistics
  • Prescriptive
    • Analysis of current data and situations
    • Examples: Optimization, Simulation
  • Predictive
    • Predict future trends and behavior based on past performance
    • Examples: Credit Scoring, Sales Projections

Categories of Business Analytics

The categories of Business Analytics (from lower to higher complexity) are:

  • Standard Reporting
  • Data Query
  • Data Visualization
  • Descriptive Statistics
  • Data Mining
  • Forecasting
  • Predictive Modeling
  • Simulation
  • Decision Analysis
  • Optimization

Big Data Analytics

  • Big data: Massive datasets collected from social media, online and in-store customer data, and so on
  • Help create real-time, personalized shopping experiences for major online retailers
  • Smart cities
    • Public records
    • Sensors, location data from smartphones
    • Ability to evaluate the effect of one service change on the system

Operational Intelligence and Analytics

  • Descriptive and/or Prescriptive in Nature
  • Operational intelligence: Business activity monitoring
  • Collection and use of data generated by sensors
  • Internet of Things
    • Creating huge streams of data from web activities, sensors, and other monitoring devices
  • Software for operational intelligence and analytics enable companies to analyze their big data

Location Analytics and Geographic Information Systems

  • Location analytics
    • Ability to gain business insight from the location (geographic) component of data
    • Mobile phones, Sensors, scanning devices, Map data
  • Geographic information systems (GIS)
    • Ties location-related data to maps
    • Example: For helping local governments calculate response times to disasters

Intelligent Techniques

  • Artificial Intelligence emulates human behavior
  • Expert Systems – Capture tacit knowledge as set of rules, using knowledge base and inference engine
  • Case Based Reasoning – Past experience stored in knowledge base.
  • Fuzzy Logic – Process imprecision like linguistics
  • Machine Learning – Absence of explicit programming
  • Neural Networks – Learn by example
  • Genetic Algorithms – Examine extremely large number of possible solutions, using DNA-like approach.
  • Intelligent Agents – Search external sources for solutions, such as Siri and Priceline.com.

Data Warehouse Components

The components include:

  • Operational Databases, Internal Data, External Data
  • Data Extraction/ Cleaning/ Preparation Programs
  • Data Warehouse DBMS
  • Metadata
  • Business Intelligence Tools
  • Business Intelligence Users

Loading a Data Warehouse - ETL

The ETL process:

  • EXTRACT: Obtain data from operational, internal and external databases.
  • TRANSFORM: Cleanse and translate data, Normalize.
  • LOAD: Organize and relate data, Catalog data using metadata.

Data Warehouses vs Data Marts

The differences are:

  • Data Warehouse stores data for the entire organization.
  • Data Marts store data for a specific business area.

Reporting Applications

  • Create meaningful information from disparate data sources.
  • Deliver information to the user on time.
  • Basic operations:
    1. Sorting
    2. Filtering
    3. Grouping
    4. Calculating
    5. Formatting
  • To maintain data warehouse integrity, NEVER use the Transform function in ETL for manipulating report data.

Primary Actions in BI Analysis

The actions include:

  • Acquire Data: Obtain, Cleanse, Organize & relate, and Catalog data from Operational databases, Social data, Purchased data, Employee knowledge, Web servers.
  • Perform Analysis: Reporting, Data mining, BigData Analysis, Knowledge management, Automation
  • Publish Results: Print, Knowledge Workers, Report servers, Feedback Results

Support for Semi-structured Decisions

  • Decision-support systems support for semistructured decisions Use mathematical or analytical models Allow varied types of analysis
    • “What-if” analysis
    • Sensitivity analysis
    • Backward sensitivity analysis
    • Multidimensional analysis / OLAP
    • For example: pivot tables

Decision Support for Senior Management

  • ESS: decision support for senior management
  • Help executives focus on important performance information Data Sources
    • Internal data from enterprise applications
    • External data such as financial market databases
    • Drill-down capabilities
  • Business Performance Management (BPM)
    • Translates firm’s strategies (e.g., differentiation, low-cost producer, scope of operation) into operational targets
    • KPIs developed to measure progress toward targets
  • Balanced Scorecard Method Measures outcomes on multiple dimensions
    • Financial
    • Business process
    • Customer
    • Learning and growth
  • Key performance indicators (KPIs) measure each dimension

Balanced Scorecard

The balanced scorecard considers strategic objectives and measures across four key perspectives:

  • Financial: "To succeed financially, how should we appear to our shareholders?"
  • Customer: "To achieve our vision, how should we appear to our customers?"
  • Internal Business Process: "To satisfy our shareholders & customers what processes must we excel at?"
  • Learning & Growth: "To achieve our vision, how will we sustain our ability to change and improve?"