As DataOps activity takes root within an enterprise, managers face the question of whether to build centralized or decentralized DataOps capabilities. Centralizing analytics brings it under control but granting analysts free reign is necessary to foster innovation and...
DataOps Facilitates Remote Work
Remote working has revealed the inconsistency and fragility of workflow processes in many data organizations. The data teams share a common objective; to create analytics for the (internal or external) customer. Execution of this mission requires the contribution of...
Improve Business Agility by Hiring a DataOps Engineer
It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change. - Leon C. Megginson on Charles Darwin “Origin of Species” Adapt or face decline. The agile alliance defines “business agility”...
Infographic – 6 Dimensions of DataOps Maturity
Curious about what DataOps capabilities your organization already has or how you stack up? DataKitchen published a white paper Launch Your DataOps Journey with the DataOps Maturity Model (which you can download here) to guide you on benchmarking your org's DataOps...
Infographic – 7 Steps to Implement DataOps
So you've learned about DataOps and want to get begin implementing it? Look no further than DataKitchen's white paper The Seven Steps to Implement DataOps (which you can download here)! We've taken the seven steps to implement DataOps and included them in the...
Improving Teamwork in Data Analytics with DataOps
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...
What is a DataOps Kitchen?
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...
Environments Power DataOps Innovation
Production engineers and analytics developers sometimes row the enterprise boat in different directions. Perhaps it is their roles and objectives which keep them at odds. It’s the production engineer’s duties to keep data operations up and running. Innovation is good,...
Gartner: DataOps Enables Remote Work
Although work restrictions are being lifted around the country, the office work environment will probably never be the same. Many companies are allowing employees to work remotely through the end of the year, until they are comfortable returning, or even forever...
Reducing Organizational Complexity with DataOps
Organizational complexity creates significant problems, but executives in a McKinsey Survey showed little understanding of the types of complexity that create or destroy shareholder value. In their research, McKinsey identified two types of complexity: Institutional...