Do you need a data scientists on demand?
Many of our assessments result in projects portfolios that demonstrate significant operational performance improvements and annual cost reductions. Our approach to data science challenges is very pragmatic and cost effective. We say this because our practitioners are skilled at understanding fundamental business processes and best practices. An important differentiator is to understand what questions to ask based on Key Performance Indicators and or metrics not being on target. It is also very important to understand the root-causes that can impact a performance metric. It is this basis of understanding that sets the stage for asking the right questions.
All too often, we have seen so-called data scientist ask "What question do you want answered?" and then spend weeks gathering, cleaning and building mathemetical models that in-the-end fails to provide a meaningful answer.
Our relationship with a client usually starts with our analyzing the issue the individuals that best undertsand the problem. We then work with them to identify all relevant and missing data that are necessary to address the issue. It is at that point, we develop a quantitative business case for pursuing the project. The business case lays out the cutting-edge methods from statistics:
- Lean Six Sigma
- Operations Research
- Machine Learning
- Decision Science
- Cognitive Science
- Software Engineering
- Busness Intelligence
- Human Factors
- Organizational Behavior
in order to produce a decisions-support or decision-automation system, we adhere methodically to agile/lean design and development lifecycles that include robust testing at every level, to ensure the most reliable and scalable solution possible. We’re equally systematic about involving customer executives and staff in each stage of a project, to ensure enthusiastic executive sponsorship, appropriate organizational support, and graceful technology and business-process transitions.
We use non-proprietary tools to help us build highly realistic fast-time simulations to support effective modeling, verification, and validation. We have likewise mastered state-of-the art approaches to improving cooperative interactions among semi-autonomous organizations that must support development and deployment of decision support systems.