Over the years, companies rely on data for business planning and decision making. Today, as the world becomes more connected to the internet through the rise of Internet of Things (IoT) as well as IT-OT convergence, data management and analysis had become crucial than ever. An organisation needs a more effective data management, hence this brought about a new terminology particularly data operations or in short DataOps. What is DataOps?
19th June 2014 marked the date of the birth of the term ‘DataOps’. First coined by Lenny Liebmann in his blog post on IBM Big Data & Analytics Hub entitled ‘3 reasons why DataOps is essential for big data success’, Data Ops has become the buzzword in the data management environment. In 2015, DataOps is popularised by Andy Palmer on the Tamr website through a blog post entitled ‘From DevOps to DataOps, By Andy Palmer’. Since then, the evolution of DataOps continues where in 2017, DataOps began to grow its importance as it successfully bring DevOps approach to data. Subsequently, DataOps enters the limelight when Gartner added it on the Hype Cycle for Data Management in 2018.
The definition of DataOps varies as its practice evolves with implementation.
Lenny Liebmann, the originator of DataOps, defines it as the set of best practices that improve coordination between data science and operations, while
Andy Palmer discusses DataOps as data management method that focuses on communication, collaboration, integration, automation and measurement of cooperation between data engineers, data scientists and other data professionals, whereas
according to Gartner, DataOps refers to a collaborative data management practice that centered around enhancing communication, integration and automation of data flows between data managers and data consumers across an organisation.
With organisations constantly accelerate digital transformation through adoptions of network related technologies that lead to the generation of sea of data, the answer to ‘What is DataOps?’ may differ as DataOps continues to evolve to improve data management approach that we applied today. Nevertheless, the DataOps now that run on Agile development, DevOps and automation is every organisation essential as these approaches are the foundation of the future implementation.
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