This blog builds on earlier posts that defined Kitchens and showed how they map to technical environments. We’ve also discussed how toolchains are segmented to support multiple kitchens. DataOps automates the source code integration, release, and deployment workflows...
Every DataOps initiative starts with a pilot project. How do you choose a project that matters to people? DataOps addresses a broad set of use cases because it applies workflow process automation to the end-to-end data-analytics lifecycle. DataOps reduces errors,...
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...
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...
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”...
Since the term was coined, DataOps has expanded the way that people think about data analytics teams and their potential. 2020 was a huge year in DataOps industry acceptance. Media mentions of DataOps are on track to increase 52% over the prior year. To date in 2020,...
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.
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...