The Four Stages of Analytics

Data analytics is the next big thing, or so they say.

The first step in using data analytics is understanding its four stages:

  1. “Descriptive analytics” – “Rear-view mirror” data describe what occurred in the past. Companies traditionally use these analytics to define how many people attended a seminar or the number of hires in the past year. Reports and dashboards often include this information. Many collection platforms automatically provide notice when a descriptive metric goes above or below a predetermined level. Most daily business units use descriptive analytics.
  2. “Diagnostic analytics”  Answering the question “why,” diagnostic analytics involves statistical and analytical models that weigh certain variables or Key Performance Index (KPIs) to define or reveal relationships in data sets and to seek possible reasons and solutions for a problem.
  3. “Predictive analytics” – These provide insight into possible future events by integrating several processes – statistics, modeling, machine learning, data mining – to find further causal relationships with active and past data sets.
  4. “Prescriptive analytics” – Building on descriptive and predictive analytics, prescriptive analytics dives deeper, using advanced analytical and mathematical models to find the best options for action in a given situation. Prescriptive analytics potentially can offer ideas on implementing new solutions and dealing with their possible implications or any corollary issues.

“Doubling down on what we know, or preferring the status quo to the unknown, may lead us to manage with stale data or ignore indications of shifting metrics or performance drivers.”

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Categories: Personal Development

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