Thursday, April 2, 2015

The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015

Predictive analytics uses algorithms to find patterns in data that might predict similar outcomes in the future. A common example of predictive analytics is to find a model that will predict which customers are likely to churn. For example, telecommunications firms can use customer data such as calls made, minutes used, number of texts sent, average bill amount, and hundreds of other variables to find models that will predict which customers are likely to change mobile carriers. If a carrier can predict the reasons why customers are likely to churn, it can try to take preemptive action to avoid this undesirable outcome.

This isn’t a one-time operation; firms must rerun their analysis on new data to make sure the models are still effective and to respond to changes in customer desires and competitors. Many firms analyze data weekly or even continuously. Game-changing insights start with asking creative, deep questions. Once the question has coalesced, use these six steps to answer them in a continuously improving predictive discipline

  • Identify data from a variety of sources.
  • Wrangle the data.
  • Build a predictive model.
  • Evaluate the model’s effectiveness and accuracy.
  • Use the model to deliver actionable prescriptions to your business peers.
  • Monitor and improve the effectiveness of the model.

SAS, IBM, SAP lead The Pack in ‘The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015’.


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