Without DataOps, a Bad System Overwhelms Good People When enterprises invite us in to talk to them about DataOps, we generally encounter dedicated and competent people struggling with conflicting goals/priorities, weak process design, insufficient resources, clashing...
One of the main goals of analytics is to improve decision-making. The CDO DataOps Dashboard puts information at the fingertips of executives, so they have a complete picture of what is happening in the data analytics domain.
A summary and recommendations for further reading
Data scientists work tirelessly to build and train a model then face the daunting challenge of deploying it into production. The model itself is only a fraction of the overall ML system.
Data teams using inefficient, manual processes often find themselves working frantically to keep up with the endless stream of analytics updates and the exponential growth of data. If the organization also expects busy data scientists and analysts to implement data...
Enterprises have adopted self-service analytics in order to promote innovation – self-service tools are ubiquitous. While data democracy improves productivity, self-service analytics also bring a fair amount of chaos. Enterprises are searching for ways to control...
Cloud computing does NOT always deliver increased agility. Migrating from an on-prem database to a cloud database may produce cost, scalability, flexibility, and maintenance benefits. However, the cloud initiative will not deliver agility if the data scientists,...
In a previous blog, we talked about aligning technical environments to facilitate the migration of analytics from development to production. In this post, we will introduce the concept of “Kitchens” and illustrate how they simplify the deployment of data analytics. In...
Data analytics is performed using a seemingly infinite array of tools. In larger enterprises, different groups can choose different cloud platforms. Multi-cloud or multi-data-center integration can be one of the greatest challenges to an analytics organization....
Announcing Open Source DataOps Data Quality TestGen 3.0
DataOps Data Quality TestGen:
Simple, Fast, Generative Data Quality Testing, Execution, and Scoring.
[Open Source, Enterprise]
DataOps Observability:
Monitor every date pipeline, from source to customer value, & find problems fast
[Open Source, Enterprise]
DataOps Automation:
Orchestrate and automate your data toolchain with few errors and a high rate of change.
[Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Data & Analytics Platform for Pharma
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.