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Data Analytics – eData Talk

Data Analytics

Descriptive Analytics

  • Summarize the given datasets to define the outcomes to stakeholders
  • Design key performance indicators (KPIs,) to track successes or failures
  • Deliver vital insights into the current and past performance

Diagnostic Analytics

  • Enhancement the offering of descriptive analytics
  • Retrieve more information from the discoveries of descriptive analytics and to find the root causes
  • Examine the KPIs further to understand the changes (positive / negative)

Predictive Analytics

  • Use historical data to identify trends to determine the likely hood of it repeating
  • Use advanced analytical techniques to predict insight into what may happen in the future
  • Use of variety of statistical and machine learning techniques, such as: decision trees, regression and prediction etc

Prescriptive Analytics

  • Allow business to make informed Data-driven decisions from the insights out of predictive analytics
  • Apply machine learning strategies which can identify patterns in the datasets
  • Analyze past conclusions, results, and events to determine the probability and possibility can be estimated in relation to the current outcomes