Moria AI Labs - Data Readiness Engine

Trust is the data practitioner’s most important asset. Moria’s Data Readiness Engine provides an intuitive way to understand the gap between source data and the business most actionable insights. Data readiness goes beyond just data quality. It’s about understanding your problem and leverage your data to solve it and demonstrate real impact

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In a nutshell

In a typical data requirement session, we start the data journey at the (often aspirational) insight derivable and connect with the (often harsh) data reality. Finding the best strategy to connect the dots goes beyond just data quality; Data Readiness focuses on relationships between data domains and helps the data practitioner (CxO, decision makers, data engineers, analysts) to create a constructive dialog and define the best outcome-oriented strategy to solve the problem.

It is an old problem, attacked from a different perspective: the “brain” of the engine is based on graph theory (1700s tech) to solve for a need to build the infrastructure for a 2030 future. 

What is Data Readiness?

Data Readiness is an outcome-centered evaluation of the journey from insights to data sources.

What’s the difference between data quality and Data Readiness?

In most cases, data quality is table/asset centered. Current data quality solutions provide assessments of the asset data quality without considering how data sources connect with each other. More importantly, data quality alone does not answer the question: “how might we understand [insert business performance outcome]?”

Our solution at a glance

Find more (WIP)

• 🚀 Check out our Data Readiness Engine!
• 📄 Read the documentation!
• ✉️ Contact me