Empowering enterprise cloud journey from devOps to dataOps: View

Source:- cio.economictimes.indiatimes.com

Enterprises in their respective journey to the cloud are shifting gears to migrate mission-critical and core workloads to the hybrid cloud. Chapter one of the cloud journey represented about 20% of the workload opportunity, it was more about moving the infrastructure and fringe applications and achieve incremental cost benefits. In chapter two of the cloud story, it is about gaining business dominance through differentiated offerings, operations, and customer experience. The digital journeys of customers play an important role in catalyzing the transformation associated with infrastructure, data through cloud-native architectures.

One of the key components of the modern day hybrid cloud architecture is the DevOps. It readily brings about the agility much needed for an enterprise and fully utilizes the benefits arising from the deployment of container runtimes and orchestration thereof. A transformed DevOps framework provides continuous integration and continuous delivery platform including support for developer tools in a multi-cloud scenario. It is easy to deduce that such an attribute is essential to quickly develop and deploy applications that reliably operate their services at massive scale.

Present day developers want to use languages and frameworks that help them seamlessly develop, test and deploy and monitor applications that connect with existing applications integrating the back-end services, either on-premise and/or across multiple public clouds. This phenomenon of the multi cloud is also complemented by the need to integrate data elements as part of the transformation. Chapter 2 of cloud journey introduces the concept of ‘inside out’ view for enterprises: essentially this means that companies with a large corpus of data can leverage the same to really build a truly cognitive enterprise. In the world of analytics, data quality and currency play a major role in determining the accuracy of the inferences. This, in turn, has a bearing on the business decisions made using the outcome of analytics. Therefore, it is supremely critical to have analytics pipeline which helps organizations to manage and monitor data throughout its analytics lifecycle. Ergo DataOps.

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Whereas DevOps focused on the agile development and delivery of customer application modules digitally, DataOps focusses on the data quality and currency. No doubt, the principles of DevOps is borrowed and applied for DataOps. This is pivoted around building sound data management process and practices: therefore data access, data quality, and automation are critical focus areas for DataOps pipeline.

In a typical data-driven enterprise, one would find business applications integrated with AI and/or analytical models. To begin with, to build a model, the data scientist would need access to the right data at the right abstraction levels, then using the various tools that are available, an AI or analytical model can be built, all within the data security guidelines laid out by the enterprise. To validate, train and score the model, a sizeable set of data is required. Needless to say, the quality of the data transformation will depend on the quality of the data. In addition, stale data can influence the accuracy levels of the outcome.

Take, for example, a life event of an individual that has altered the risk profile. If a business service is being offered to that individual (such as an offer of a loan), consideration of the newer risk profile will be far more relevant. Therefore, continuous access to the right data in a secure manner, frequent refreshes of the model to use the most current data set available and integration of the current inferences into the business application – all form the core of how a company can effectively leverage data. And this is what DataOps is all about.

It is easy to surmise that the benefits are all across the enterprise. From an IT perspective, this helps achieve better and stronger data management. For the operations team, this enables bringing in better governance without compromising on security. And of course, for the data scientist, this enables ready access to right data and tools. Finally, the business benefits through more accurate and timely insights. Enterprises today need to plan their DataOps journey in line with their Cloud journey to achieve business growth.