DataOps (data operations) supports cross-functional data analytics teams with an agile methodology to streamline the delivery of trusted, reliable analytics
Automate tedious, repetitive tasks to keep your data pipeline healthy—improve collaboration, prevent defects, speed up incident resolution, reduce the cycle time of data analytics, and increase the value of analytics
What is DataOps?
Gartner defines DataOps as "a collaborative data manager practice, really focused on improving communication, integration, and automation of data flow between managers and consumers of data within an organization."
DataOps embraces the core ideas of DevOps - an agile approach to software development that emphasizes communication and collaboration to build cross-functional teams that can deliver software quickly and reliably. For data analytics, the intention is to bring together the data scientists, data analysts and data engineers that, together, are able to activate data for business value.
Improve communication between stakeholders and developers
Collaboration is a key principle of DataOps. The use of a value-driven data catalogue, like Data360, ensures that decision makers' requirements are clearly understood and linked to business goals and objectives. The development team, whether from the engineering or analytics perspective, can quickly and easily find the data sets that they need to do their jobs, understand the scope and impact of required changes, communicate decisions, and reduce the cycle time for delivery.
Agile pipeline delivery
Agility is key to DataOps. The complexity of modern data architectures means that data pipelines can become the bottleneck for analytics delivery. Data sets may come from legacy platforms (like the mainframe or RDMS solutions), from enterprise applications (via API) or from external sources including social media and cloud-based analytics platforms. And, in a rapidly evolving technology space, we need to future proof our technology investment as today's preferred analytics platform may become tomorrow's old news.
Avoid Production Defects with Better Data Pipeline Observability
With the growing complexity of data systems, data pipeline observability is very time-consuming and nearly impossible to achieve. Worse still, without this deep understanding of data pipeline and agile development requirements, every change to the environment carries a high risk of broken releases. To deal with this complexity, data teams allocate up to 40% of their resources to carry out manual impact analyses. MANTA cuts down on manual effort by enabling agile change management with fully automated impact analysis, incident resolution, and debugging.
Secure your analytics environment
Last, but certainly not least, we need to ensure that the sensitive data of our customers and other key stakeholders are protected, both on-premise and in the cloud. The DataOps team need access to the data that they need for insights, without compromising privileged information. Role- and location-based security policies must be defined and enforced from ingestion to consumption. Audit trails must be maintained to satisfy regulators, particularly when information is moved to the cloud.