BUT, THERE ARE SOME CHALLENGES TO ADOPTING A DATAOPS FRAMEWORK
Banks and financial services firms can encounter some technical and behavioral challenges when embarking on a DataOps journey.
Technical challenges: As DataOps is not a technology innovation but rather a process innovation, establishing a formal process of using DataOps tools and software effectively can be challenging.
Behavioral challenges: People’s behavior toward changing the way they work to adopt agile development is not easy. Getting business and technical users to buy into the process can also be difficult.
Since it is an evolving concept, organizations must consider these technical and behavioral challenges when establishing DataOps practices. This will prevent failures in the longer term.
To implement DataOps practices, organizations should start with a few use cases and scale up gradually so that ongoing processes remain undisturbed. To overcome DataOps adoption challenges, banks should focus their efforts on three areas: team structure, tools and technologies, and processes and methodologies.
A collaborative team structure always helps
Collaboration within and across various teams is an integral part of the DataOps framework. Along with the traditional roles of data modelers, data architects, and data engineers, new responsibilities must be added. These are data scientists, data analysts (for self-service and predictive analytics), and IT operations specialists, as DataOps emulates DevOps with continuous integration (CI) and continuous deployment (CD) for sprints and minimum viable product (MVP) releases. The product owner plays the critical role of being the owner of the data product. The scrum team comprises all the above-mentioned roles under the product owner to carry out the product development activities.
Tools and technologies are key
DataOps brings the entire data lifecycle into play, from data preparation to data delivery, and involves the interconnected data analytics team and data-related operations. DataOps technologies are a mix-and-match of data engineering and DevOps tools. Depending on the architectural pattern, a data pipeline can be built by utilizing various data management and engineering tools. Metadata tools for metadata-driven data management and governance play an essential role in DataOps deployment. In addition, test automation tools are required to automate the test functionality and improve the quality of the code. Hence, code repositories are essential, especially in agile development environments with stringent version control procedures and continuous integration processes.
Don’t forget processes and methodologies
The DataOps process is a series of steps, methodologies, guidelines, and metrics that have to be monitored. People are ineffectual without practices in place to support their decisions. There must be standard processes for data requirement gathering, data pipeline development, testing and tuning, orchestration and automation, production move, and continuous iteration. Moreover, the teams must be trained as part of the DataOps framework.
Lately, several product vendors are offering tools that address some or all of the capabilities across the DataOps lifecycle. DataOps products provide solutions to orchestrate people, processes, and technology for delivering a trusted data pipeline. Several cloud providers also offer DataOps services by assembling different types of data management software into a single integrated environment.