In the webinar “Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms,” James Royster, Vice President of Commercial Operations, Insights, and Analytics at Karuna Therapeutics, shared his insights on leading efficient and effective data analytics teams.
You spend all day helping your customers leverage analytics for improved business performance, so why are you so un-analytic about how you run your data analytics teams? Where is your data and analytic team metrics report?
The real problem in data analytics is that teams need to
deliver insight to their customers without error
put new ideas into production rapidly
minimize their ‘insight manufacturing’ expenses
… all at the same time
Gartner released the article “5 Ways to Enhance Your Data Engineering Practices.” By Robert Thanaraj, Ehtisham Zaidi, and 2 more. DataKitchen gives its perspective.
What will the world of data tools be like at the end of 2025? The crazy idea is that data teams are beyond the boom decade of “spending extravagance” and need to focus on doing more with less.
We asked an AI Chat Bot (https://chat.openai.com/chat) a few questions about DataOps. It got a bit silly at the end.
Gartner released its ‘Market Guide to DataOps Tools’ The document they wrote is exceptionally close to what DataKitchen sees in the market and what our products do! Here is our take on the guide.
Wayne Eckerson discusses the critical capabilities required for DataOps success and how to compare different vendors’ DataOps offerings
Industry analysts who follow the data and analytics industry tell DataKitchen that they are receiving inquiries about “data fabrics” from enterprise clients on a near-daily basis. Forrester relates that out of 25,000 reports published by the firm last year, the report...
Download the 2021 DataOps Vendor Landscape here. Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps is a hot topic in 2021. This is not surprising given that DataOps enables enterprise data teams to generate...
The Race For Data Quality in a Medallion Architecture
DataOps Observability: Monitor every Data Journey in an enterprise, from source to customer value, and find errors fast! [Open Source, Enterprise]
DataOps Data Quality TestGen: Simple, Fast Data Quality Test Generation and Execution. Trust, but verify your data! [Open Source, Enterprise]
DataOps Automation: Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change. [Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Pharma Agile Data Warehouse
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
Data Observability
DataGovOps
Self-Service Operations
Hybrid Cloud Architectures
Cloud Architectures
Kubernetes Architectures
Monitor every Data Journey in an enterprise, from source to customer value, in development and production.
Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.
Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.