What Is Agile Analytics?

This post is the second in a four-part series.

Agile Analytics is an approach to predictive analytics that emphasizes:

  • Client satisfaction through rapid delivery of usable predictions
  • Focus on model performance when deployed “in market”
  • Iterative and evolutionary approach to model development
  • Rapid cycle time through radical reduction in time to deployment

The Agile approach focuses on the client’s end goal: using data-driven predictions to make better decisions that impact the business.  In contrast, conventional approaches to predictive modeling (such as the well-known SEMMA[1] model) tend to focus on the model development process, with minimal attention given to either the client’s business problem or how the model will be deployed.

Since Agile Analytics is most concerned with how well the predictive model supports the client’s decision-making process, the analyst evaluates the model based on how well it serves this purpose when deployed under market conditions.  In practice, this means that the analyst evaluates model accuracy in production together with score latency, deployment cost and interpretability – a critical factor when building predictive analytics into a human process.   Conventional approaches typically evaluate predictive models solely on model accuracy when back-tested on a sample, a measure that often overstates the accuracy that the model will achieve when deployed under market conditions.

Agile analysts stress rapid deployment and iterative learning; they assume that the knowledge produced from tracking an initial model after it is deployed enables enhancements in subsequent iterations, and they build this expectation into the modeling process.  An Agile analyst quickly develops a predictive model using fast, robust methods and available data, deploys the model, monitors the model in production and improves it as soon as possible.  A conventional analyst tends to take extra time perfecting an initial model prior to deployment, and may pay no attention to in-market performance unless the client complains about anomalies.

Reducing cycle time is critical for the Agile analyst, since every iteration produces new knowledge.  The Agile analyst aggressively looks for ways to reduce the time needed to develop and deploy models, and factors cycle time into the choice of analytic methods.  Conventional analysts are often strikingly unengaged with what happens outside of the model development task; larger analytic teams often delegate tasks like data marshalling, cleansing and scoring to junior members, who perform the “grunt” work with programming tools.


[1] Sample, Explore, Modify, Model, Assess

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