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Improving Performance Outcomes via Sound Data Science Practices

RIIS is a team of experienced Data Scientists that work at the intersection of engineering, algorithms, and domain knowledge - to deliver meaningful production-ready information to our customers. We specialize in large, complex, compliance-heavy environments and help our customers improve information accuracy to achieve and expand solution productivity. Our customer’s success is a result of our unique and proven Data Science approach which blends Service Design Principles with proven Data Science Practices.


When you don’t know where you are going - any road with take you there


The path to successful Data Science outcomes is problematic for most organizations. Gartner predicts that through 2022, 85 percent of AI projects will fail due to bias in data, algorithms or lack of competency of the teams responsible for managing them. The Economist reports that 70% of business executives rated analytics as “very important”, however only 2% said that they had achieved “broad positive results”. Unfortunately, many attempt to engage Data Science and lose sight of the north star and attempt the effort with unqualified staff.


Data Science isn’t the end result - it is a cross functional practice to achieve better performance results. To get there we recommend emphasizing (1) talent and (2) outcomes. This means bringing together talented cross-functional data science agile teams to maximize learning and adapting to uncertain conditions. Additionally, following Service Design principles that focus on outcomes provides the necessary understanding of the holistic environment with a focus on domain understanding and accounting for the humans, organization, devices, and governance.


How RIIS Does Data Science - Talent and Outcome Focused


Team Level Strategy

  • Talented Cross-Functional Team: Service Designer, Data Scientist, and Engineer

  • Product Thinking: Use Value Driven and Outcome Based Program Management

  • Modern Secure Infrastructure: Reliable AI/ML and Data DevSecOps Infrastructure

  • Iterate and Learn: Lean/Agile Delivery Methodologies

  • Default to Open: Embrace Open Tools and Technologies

Portfolio/Enterprise Level Strategy

  • Holistic Approach: Service Centered Design Principles to Map to Value Delivery

  • Default to Open: Embrace Open Tools and Technologies

  • Data Science Coaching: Help Others Understand the Realistic Value of Data Science

  • Re-Use: Embrace Re-Use Data Science Services for Maximized Efficiency



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