While the term "DataOps" implies that it is highly influenced by DevOps, the intellectual background of DataOps is made up of three approaches — Agile, DevOps, and statistical process control. SPC orchestrates and monitors the data factory, while Agile governs analytics development. DevOps improves code verification, builds, and delivery of new analytics.
DataOps is a collaborative practice that improves the enterprise's data integration, reliability, and delivery. It is built on a solid foundation of DevOps techniques. DataOps, like DevOps, encourages collaboration among business areas such as data platforms, IT operations, business analytics, engineering, and data science. Throughout the data lifecycle, it focuses on simplifying and automating the data pipeline:
| Integration of data: Connecting to several data sources has never been easier. |
| Validation of data: Data is tested to verify that business decisions are based on correct data. |
| Management of metadata: keeping a clear grasp of the data estate's topology, origins, dependencies, and how the data has evolved through time |
| Observability: collecting granular data system insights with rich context to aid DataOps teams in better understanding system behavior and performance |
DataOps opens the path for efficient data operations and a dependable data pipeline, delivering trustworthy data in faster development and delivery cycles. Data and analytics professionals must connect DataOps with how data is consumed in their organizations rather than how it is created. Enterprises will be able to design adaptive data management systems for modern data delivery by adapting DataOps to three primary value propositions. Let us look at how leaders across the world are leveraging DataOps to drive their customer value in a faster way. Here are a few ways that stood as fruitful in recent times.
Have a Clear Path on what to focus while Adapting DataOps for Utility Value Propositions :
A utility value proposition's ecosystem focuses on removing hurdles to data access and administration, allowing easy access to a variety of data sources regardless of downstream use cases. The implementation of DataOps for the utility value proposition resembles that of its DevOps, but it comes with its own set of obstacles. Because data is given as a generic utility, the team developing the data product will most likely be detached from the many users, shifting the focus of collaboration away from what data is needed and toward how varied consumption patterns may be accommodated. Continuous integration and deployment of new data sources, as well as operational excellence in the form of automation, are the focus. Data quality, SLA compliance, and pipeline resiliency should be automated to the greatest extent possible, which includes automated testing as part of the deployment cycle, which wasn't the normal way earlier.
Support your plans with Adaptable Governance
An ecosystem built on rapid, impromptu access to data for discovery, but not limited to analytics, underpins the driver value proposition. Combining information from previous systems for a short-term project or loading it into another application or system are examples of discovery goals. The dynamic nature of data consumption and use must be the emphasis of DataOps supporting this type of value offering. Adaptive Governance is critical for all of the data analytics value propositions, but it poses a distinct challenge for the driver value proposition. Governance must be tailored to the goals you wish to achieve, with performance indicators to back it up. Reinvest in your governance to quickly reach your path intended.
Promote cross-team collaboration to Enhance your Value Proposition :
Operational and analytic applications consume data from utility architectures in enabling value proposition frameworks, but with more specialized data integration needs to match a given use case. DataOps must focus and frequently cooperate with business unit users who are customers for a specific solution servicing their use case in order to deliver this value proposition. The enabling value proposition relies heavily on metrics. A data availability service index, or statistics on how quickly freshly created data is made accessible for consumption by your aggregate metrics, could be used to support those aggregate metrics. Beyond the definition of requirements, DataOps implementation for enabling value propositions necessitates the constant involvement of several roles. Data product managers and data engineers must collect feedback from data users on a regular basis and make modifications to the data as rapidly as possible.
The term "DataOps" does not refer to a product, service, or solution. It's a process that entails a technological and cultural shift to improve your company's data use through improved cooperation and automation.