Enabling a self-service architecture at each layer allows end users to get what they need quickly. That means easy access to insights for business stakeholders, metrics for business intelligence developers, critical business and data definitions for data scientists, and data sources for engineers.
We treat analytics and AI solutions as products, ensuring each initiative includes the necessary level of user experience research and design. Whether it’s a dashboard or a model that is deployed and feeding a downstream application, each product has requirements, design elements and a plan to manage its operational quality throughout its lifespan.
Data and model pipelines typically support critical operations within an organization. It’s important for the right testing criteria to be in place throughout the deployment process and that quality is monitored overtime. Enabling practices like DataOps and MLOps can ensure teams are proactively making adjustments instead of reactively firefighting. The more pipelines and models are in production, the more important these capabilities are to maintain quality and continuously provide value to the business.