Analytics Overview and Phases

Analytics Overview

• Analytics uses arithmetic, statistical, data mining, and advanced methods like machine learning to find new parameters and information that add value to data.
• It enables building models based on selecting the right data, which are then tested and used for services and processes.

Analytics Phases

• Analytics has three phases: descriptive, predictive, and prescriptive analytics.
• These phases derive new facts and provide business intelligence.

Descriptive Analytics

• Descriptive analytics answers questions about what happened in the past.
• It involves finding aggregates, frequencies, mean values, and variances in values or groupings using selected properties.
• It enables:
  • Online Analytical Processing (OLAP) for analytics
  • Reporting or generating spreadsheets
  • Visualisations or dashboard displays of the analysed results
  • Creating indicators, called key performance indicators

Descriptive Analytics Methods

• Spreadsheet-based reports and data visualisations:
  • Present results in a spreadsheet format
  • Enable user visualisation of "what if" scenarios
  • Use tables with values in cells that can be related to other values through formulas or statistical analysis
• Descriptive statistics-based reports and data visualisations:
  • Use statistical analysis to find peaks, minima, variance, probabilities, and statistical parameters
  • Use formulae to enable data showing variations
• Data mining and machine learning methods in analytics:
  • Use algorithms to extract hidden or unknown information from large amounts of data
  • Model specific tasks using machine learning
• Online analytical processing (OLAP) in analytics:
  • Enables viewing of analysed data up to the desired granularity
  • Enables rollup or drill down of data
  • Provides summarized information and automated reports from large volume databases

Predictive Analytics

• Uses algorithms, such as regression analysis, correlation, optimisation, and multivariate statistics
• Uses techniques, such as modelling, simulation, machine learning, and neural networks
• Enables:
  • Predicting trends
  • Undertaking preventive maintenance
  • Managing campaigns with integrated marketing strategy
  • Predicting by identifying patterns, clusters, and anomalous characteristics
• Results of predictions need verification from domain knowledge and multiple angles

Prescriptive Analytics

• Answers not only what is anticipated or what will happen but also why it will happen
• Suggests actions for deriving benefits from predictions
• Shows the implications of decision options or optimal solutions
• Provides new resource allocation strategies or risk mitigation strategies
• Suggests the best course of actions in a given state or set of inputs and rules

Event Analytics

• Events are definable options that are unique, non-interaction, or interaction options
• Event analytics uses event data for events tracking and event reporting
• Enables tracking and reporting of events