The new standard in DataOps Services

DataOps is a modern approach to managing and delivering data analytics and data management processes in a collaborative and agile manner. It is a set of practices, and methodologies that aim to streamline and automate the end-to-end data operations process, from data acquisition and management to data analysis and visualization.

DataOps is critical for any business due to the following reasons:

• Streamlined data management
• Improved data quality
• Advanced insights
• Increased agility
• Higher ROI

DataOps and DevOps are two related but distinct approaches to software development and operations. DevOps is an approach to software development that emphasizes collaboration and communication among developers, operations teams, and other stakeholders involved in the software development life cycle (SDLC).

DataOps, on the other hand, is an approach to managing and delivering data analytics and data management processes in a collaborative and agile manner. It emphasizes the same principles as DevOps, including collaboration, automation, and continuous delivery, but it focuses specifically on the data-related aspects of software development and operations.

Turing offers comprehensive DataOps services that include data ingestion and integration, data quality management, data storage and management, data governance, and data analytics & reporting among others.

Yes, you can implement DataOps at any stage of product development. It is a flexible and adaptable methodology that focuses on improving collaboration, integration, and automation of data workflows across your organization. You can introduce it at any point in the product development lifecycle, whether you're in the planning stages, actively developing features, or refining and optimizing existing processes.

There are several steps involved in implementing DataOps in your organization. Here are the key steps involved in the implementation of DataOps:

• Assess your organization’s data readiness
• Define your DataOps objectives
• Implement DataOps processes and tools
• Build a DataOps culture
• Continuous monitoring & optimization