Needle in the Hay Stack

Big Data Techniques

Most big data techniques have been around for many years. What’s new is their availability to more people, the speed with which they run (so that many variations can be processed), the variety of data they can process (to provide richer and deeper context), and the volume of data they can handle.

The various types of analytics in big data are

  1. Descriptive analytics
  2. Diagnostic analytics
  3. Prescriptive analytics
  4. Predictive analytics

Descriptive analytics – What happened?

Descriptive analytics aims to provide insight into what has happened. There are various methods and technologies that are involved in descriptive analytics like A/B testing, dashboards, business activity monitoring, complex event processing, content analytics, geospatial analytics, graph analytics, pattern/anomaly detection and clustering/classification.

Diagnostic analytics – Why did it happen?

Diagnostic analytics focuses on analysis of data to find out the causes of the event and relates to Root-cause analysis. The method/technologies include online analytical processing, data mining and interactive visualization.

Predictive analytics – What will happen?

Predictive analytics helps model and forecast what might happen.  Predictive analysis involves technologies like crowdsourcing, data mining, forecasting, machine learning and simulations.

Prescriptive analytics – Make it happen

Prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters. The methods and technologies include fuzzy logic, optimization, rules engines and decision analysis.

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Comments on: "Big Data Techniques" (1)

  1. From the four big data tecniques, for you what´s the most important and what´s the less. For me Predictive Analysis is the most important. As Systems Engineering, you learned at the University Descriptive analytics and Diagnostic analytics; when you design an Information System.

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