What Is Driving Interest in Agile Analytics?

Part three in a four-part series.

A combination of market forces and technical innovation drive interest in Agile methods for analytics:

  • Clients require more timely and actionable analytics
  • Data warehouses have reduced latency in the data used by predictive models
  • Innovation directly impacts the analytic workflow itself

Business requirements for analytics are changing rapidly, and clients demand predictive analytics that can support decisions today.  For example, consider direct marketing:  ten years ago, firms relied mostly on direct mail and outbound telemarketing; marketing campaigns were served by batch-oriented systems, and analytic cycle times were measured in months or even years.  Today, firms have shifted that marketing spend to email, web media and social media, where cycle times are measured in days, hours or even minutes.  The analytics required to support these channels are entirely different, and must operate at a digital cadence.

Organizations have also substantially reduced the latency built into data warehouses.  Ten years ago, analysts frequently worked with monthly snapshot data, delivered a week or more into the following month.  While this is still the case for some organizations, data warehouses with daily, inter-day and real-time updates are increasingly common.  A predictive model score is as timely as the data it consumes; as firms drive latency from data warehousing processes, analytical processes are exposed as cumbersome and slow.

Numerous innovations in analytics create the potential to reduce cycle time:

  • In-database analytics eliminate the most time-consuming tasks, data marshalling and model scoring
  • Tighter database integration by vendors such as SAS and SPSS enable users to achieve hundred-fold runtime improvements for front-end processing
  • Enhancements to the PMML standard make it possible for firms to integrate a wide variety of end-user analytic tools with high performance data warehouses

All of these factors taken together add up to radical reductions in time to deployment for predictive models.  Organizations used to take a year or more to build and deploy models; a major credit card issuer I worked with in the 1990s needed two years to upgrade its behavior scorecards.  Today, IBM Netezza customers who practice Agile methods can reduce this cycle to a day or less.

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