DataOps: Your path to a culture of inclusion and innovation
In a digital world, where companies of all sizes are processing more data than ever before, data operations (DataOps) can help ensure that data is managed in the most effective way possible, delivering accelerated value to the business. But what is DataOps?
Not to be confused with DevOps
You’re probably already familiar with DevOps – a set of practices combining software development and IT operations to make the development lifecycle as fast and efficient as possible. DevOps improves interaction between developers and operational IT staff, and works particularly well with agile communication techniques, which tighten interactions between developers and customers – never usually a natural combination.
Both DevOps and agile practices, try to mitigate the risk of building the wrong thing. They help developers respond to customer feedback as quickly as possible, and deploy bug fixes as soon as possible, amongst other things. As DevOps teams mature, they gravitate towards standardised platforms. Typically, organizations start off with a developer-led culture and siloed teams. As they integrate DevOps practices, they become more service-oriented, creating cross-discipline teams and standard operational definitions which are applied across the company.
Ultimately, DevOps can lead to organizations developing platform features for all stakeholders to work from, so that shared scalability services are in operation across the business.
DevOps helps organizations to accelerate the benefits of technology and data by harnessing all the necessary skills, supporting tools and working methodologies and focusing them on delivery. DataOps aims at the same outcomes – but where DevOps is focused on improving interaction between developers and operational IT, DataOps is focused on improving interaction between customers, analysts and engineers.
DataOps blends some DevOps and agile concepts to ensure analytical and operational data is a high-quality asset. It seeks to answer questions like:
DataOps can be a mechanism for self-service data innovation, enabling platforms for the entire data lifecycle and empowering users to interact and enrich their organizational data. This means that practitioners can leapfrog the decades of DevOps introspection and create stable, dependable data platform services. There is even a DataOps Manifesto, which states that the concept aims to provide an environment for emergent data use and allow as many people as possible to use that environment.
