Implementing DataOps requires a combination of new methods and automation that augment an enterprise’s existing toolchain. The fastest and most effective way to realize the benefits of DataOps is to adopt an off-the-shelf DataOps Platform. Some organizations try to...
Rajesh Gill, Associate Director of Commercial Insights, discusses how Celgene used DataOps to build a huge success with the Otezla brand.
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...
Most projects naively assume that most of the time and resources will be spent in the “black box,” building the machine learning (ML) model, whereas a majority of the project time is actually needed in the green boxes – the ML system.
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...
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