Remove Data Ingestion Remove Data Management Remove High Quality Data Remove Raw Data
article thumbnail

AI Implementation: The Roadmap to Leveraging AI in Your Organization

Ascend.io

Visual representation of Conway’s Law ( source ) Read More: The Chief AI Officer: Avoid The Trap of Conway’s Law Process: Ensuring Data Readiness The backbone of successful AI implementation is robust data management processes. AI models are only as good as the data they consume, making continuous data readiness crucial.

article thumbnail

Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

article thumbnail

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. What is Data in Use?