bs analytics

• Business analytics


  Uses data to drive decisions<|>Provides valuable insights to decision-makers<|>Recommends a solution to a business problem
• Data


  Facts & statistics collected together for reference or analysis<|>A set of values of qualitative & quantitative variables<|>Pieces of data are individual pieces of information<|>Data is collected by a huge range of organisations & institutions<|>Data can be visualised using graphs, images or other analysis tools
• Analytics provide insights into data
• The era of big data
• Transactions: 150 million per day, 1667 per second
• Total Tweets: 500 million per day, 350,000 per minute, 6,000 per second
• From DATA to DECISIONS

  1. Data

  2. Information

  3. Insight

  4. Decision
• Business analytics is the process of analysing and using data to make smarter business decisions
• Generally there are three dimensions to big data: volume, variety, velocity and veracity
• Business analytics categories


  Prescriptive<|>Predictive<|>Descriptive
• Prescriptive analytics


  Analytics that suggest a prescribed next step
• Predictive analytics


  Analytics that help you forecast future performance
• Descriptive analytics


  Analytics that help you to understand the state of things
• Descriptive analytics


  • Explains what has happened within the business
  • Describes/summarises raw data into something meaningful
  • Leverages analytics to look at past data – insights into the past
  • Finds meaningful patterns and insights
  • Commonly applied to structured data
• Data query


  Requests for information from a database<|>Query provides description information, e.g. mean, standard deviation, % distribution<|>SQL is used to communicate with the database
• Reports


  Provide a historical view of the data outcomes
• Data visualisation


  Includes maps, tables, graphs, images<|>Dashboards are an Information Management tool to visually track, analyse & display KPI's, metrics & key data points to monitor performance

  
• Predictive analytics


  • Seeks to predict the unknown
  • Uses forecasting techniques to predict the future
  • Estimates the likelihood of a future outcome (based on probabilities)
  • Provides actionable insights based on data
  • Combines historical data to identify future patterns
  • Can identify any risks or opportunities for the future
• Forecasting


  Technique used in predictive analytics
• Data mining


  Technique used in predictive analytics
• Prescriptive analytics


  • Allows users to prescribe a number of different possible actions toward a solution
  • These actions are all about providing advice on what will happen, and why
  • Suggests the best decision for business in the present
  • Quantify the effect of future decisions, before they are made
• Programming


  Technique used in prescriptive analytics
• Optimisation


  Technique used in prescriptive analytics
• IT Infrastructure


  • Includes all the hardware, software, support staff
  • Manages all the systems that enable data storage, collection, analytics
• Corporate culture


  • Must be compatible with business analytics
  • Companies that don't change or rely on intuition rather than evidence will find it difficult to incorporate business analytics
• A company's sales volume

  Rises or falls depending on its advertising expenditure
• The manager should use PREDICTIVE ANALYTICS to estimate the sales expected next year for the planned level of advertising
• Business analytics can be subdivided into three general categories: descriptive, predictive and prescriptive
• Two aspects of business that are ancillary to business analytics, but nevertheless crucial to its success: its IT infrastructure and its culture
• Data measurements


  The evaluation of a feature (e.g. height, weight, intelligence, age, etc)
• Variables (data items)


  Characteristics, numbers or quantities that can be measured (e.g. age, gender, country of birth, income, class grades, etc)
• Numerical variables


  • Discrete: Whole numbers (counts)
  • Continuous: May contain any value within some range
• Categorical variables


  • Nominal: Qualitative, no natural order
  • Ordinal: Qualitative, ordered
• Numerical variables


  • AGE - INCOME
  • NUMBER OF CHILDREN
• Categorical variables


  • NAME
  • SEX - MARITAL STATUS - SMOKING
• Descriptive statistics


  Summaries statistical information<|>Techniques include: Frequency distributions, Percentage distributions, Cumulative distributions<|>Searches for patterns or common themes in the data distributions
• Numeric variables can be continuous and discrete
• Descriptive statistics


  Helps describe, show or summarize data in a meaningful way<|>Very important because raw data is hard to visualise