Written by Ted Friedman, Gartner
The COVID-19 pandemic has accelerated the need for data and analytics leaders to deliver insight faster, with higher quality and resiliency in the face of constant change. Organizations need to make better-informed and faster decisions with a focus on automation, real-time risk assessment and mitigation, continuous value delivery and agility.
Yet, despite massive investment in data and analytics technologies, getting products and projects into production remains a challenge. Deployment into business processes and applications has been cited as the main bottleneck for data and analytics leaders who have production deployment difficulties.
As a result, data and analytics leaders are increasingly applying DataOps as a solution to data deployment and consumption difficulties. DataOps techniques provide a more agile and collaborative approach to building and managing data pipelines.
What is DataOps?
DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization.
The purpose of DataOps is to change how people collaborate around data and how it is used in the organization. Rather than simply throwing data over the virtual wall, where it becomes someone else’s problem, the development of data pipelines and products becomes a collaborative exercise with a shared understanding of the value proposition.
Champion successful DataOps practices
To implement DataOps successfully, data and analytics leaders must align DataOps with how data is consumed, rather than how it is created in their organization. If those leaders adapt DataOps to three core value propositions, they will derive maximum value from data.
First, map DataOps strategy to a value proposition by treating data as a utility that focuses on removing silos and manual effort when accessing and managing data. As such, data and analytics is readily available to all key roles. Because there are many relevant roles and not a single owner of the data, assign a data product manager to ensure data consumers’ needs are being met.
Then, use DataOps to support data’s use as a business enabler. For this value proposition, data and analytics supports specific use cases such as fraud detection, analysis of supply chain optimization or interenterprise data sharing. DataOps must drive collaboration with the business-unit stakeholders who are the customers for a specific product serving their use case.
Finally, support the data and analytics driver value proposition. Use data and analytics to create new products and services, generate new revenue streams or enter new markets. For example, an idea for a new connected product emerges from your lab and must evolve into a production-quality product for use by your customers. Use DataOps to link “Can we do this?” to “How do we provide an optimized, governed data-driven product to our consumers?”
The question you may have is, “Which type of DataOps value proposition is most relevant for my organization?” There is no single answer. Every business will have all three, either in a centralized or decentralized deployment model.
Delivering DataOps using each value proposition will foster collaboration between stakeholders and data implementers delivering the right value proposition with the right data at the right time.