We focus on four core areas that are critical for organizations to unlock capabilities and access the full value of their data.

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Services Overview

Learn more about each of the service offerings we provide to our customers.

Data Enablement

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.

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AI/Analytics Portfolio & Project Management

Managing and prioritizing analytics and AI initiatives across an organization is complicated. Each part of the process is dependent on another, making delivery estimates, feasibility assessments and hand offs between teams difficult. All this while also adjusting to ever changing business priorities. Setting up the right process architecture will allow teams (data science, data engineering, analytics) to adjust quickly to the business and stay in sync.

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AI/Analytics Product Management & Design

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.

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DataOps & MLOps

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.

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