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Michael Zeller is the CEO and co-founder of Zementis, Inc., a software company focused on predictive analytics and advanced decisioning technology.

Zementis recently launched the world�s first predictive analytics scoring engine as a service on the Amazon Elastic Compute Cloud, delivering a quantum leap in agility for the deployment, integration, and total-cost-of-ownership (TCO) of predictive analytics.

Dr. Zeller received a Ph.D. in Physics from the University of Frankfurt (Germany), with emphasis in the development of neural networks, robotics, and human-computer intelligent interaction. As a physicist, his mission is to combine science and software to create superior business and industrial solutions that leverage predictive models and rules in real-time.

Which analytical fields are likely to experience growth, and why?

Driven by a challenging global economic climate, we will see a surge of interest in operational predictive analytics as part of a more comprehensive Enterprise Decision Management (EDM) strategy. Organizations increasingly recognize the value that predictive analytics offers to their business, enabling them to anticipate and respond to shifts in market conditions, risks, customer behavior changes.

To date, the complexity of development, integration, and deployment of predictive models, however, is often considered cost-prohibitive for projects due to complex development cycles, skill requirements and a general lack of standardization.

However, in light of today�s mature open source solutions, open standards, and SOA (Service Oriented Architecture) principles, we can allow users to quickly leverage predictive analytics in operational environments. By accelerating time-to-market for decision models and reducing cost with SaaS (Software-as-a-Service) options, predictive analytics will become accessible to a much larger market across various industries.

Which methodologies might become obsolete, which ones are likely to entertain growth?

Proprietary methods come and go, while open standards can propel a field to the next level. In the field of predictive analytics, we are fortunate that Robert Grossman at the University of Illinois at Chicago envisioned the Predictive Model Markup Language (PMML) standard. Under his direction, the Data Mining Group (www.dmg.org) emerged as a vendor-led group which develops data mining standards. PMML is now a mature standard, continuously evolving, and supported by major industry players. Having one common model exchange format will fuel innovation for the application of predictive analytics.

Full story at www.analyticbridge.com/group/interviews/forum/topics/interview-with-michael-zeller