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Information for Chief Analytics Officers

A page of resources for Chief Analytics Officers and those responsible for using advanced analytic or data science to deliver business value.

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If you haven’t seen our video on why Chief Analytics Officers shouldn’t think about data and analytics, check it out below:


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Learn More

To learn more about the DecisionsFirst™ approach to analytics, check out some of these great resources:

  1. See the value of decision modeling in analytics and data science by downloading our Case Study, Bringing Clarity to Data Science Projects with Decision Modeling
  2. Watch our webinar on Delivering the Business Value of Analytics
  3. See how decision modeling and Decision Management can be applied in a repeatable framework with our brief on CRISP-DM and Decision Management.
  4. Help your analytics teams to find the 6 Questions to Ask Your Business Partner Before You Model and the 5 Things You Need to Know Before You Deploy Your Model.
  5. Learn more about Framing Analytic Requirements with Decision Modeling by reading our white paper.
  6. See how you can start Building an Analytic Enterprise with these great videos and white paper.


Decision Modeling Pilot for Analytics Teams

The biggest challenge facing organizations investing in advanced analytics – data mining, predictive analytics, data science, machine learning and all forms of analytics – is closing the gap between business value and analytics results. Adopting decision modeling for developing the business understanding and requirements keeps the focus on business problems, revives lost projects and brings clarity to analytic problems – even ones long thought too hard.

Decision modeling focuses the analytic effort, brings clarity to analytic problems and builds a shared understanding with your business partners, helping to ensure successful deployment and adoption. We can help you quickly and cost effectively integrate DMN decision modeling into your analytics project in a fast, affordable and fixed price engagement.

Key Benefits

  • Accurately frame and scope analytic projects before committing resources.
  • Ensure analytics generate business value by evaluating them in business terms.
  • Reduce implementation time and cost by pre-defining deployment context.

What Analytics Teams Are Saying

“This is the critical path to monetizing advanced models.” – Head of Analytics, North American Insurance Company

“Clearly an effective way to get more understanding of a problem prior to modeling.” – Analytic Modeler

Get Started

Ready to get started? Contact Us today.