DataOps

Establishing an AI-first approach and supporting infrastructure with DataOps

Data science, machine learning, big data, and AI adoption require an organizational mind shift that treats data as well as algorithms, as crucial infrastructure pieces. This emerging approach is the methodology known as DataOps, utomated, process-oriented data analytics that reduces insight-delivery-time and improves insight quality. It With AI-defined goals at its core, DataOps streamlines data pipelines and affirms the unified transparent app development and delivery processes, enabling an AI-first approach.
Our team of architects and data scientists will work with you to develop the right strategy, introduce AI into existing processes, and implement it across the organization.

DataOps Strategy

Our process starts with your business goals and the right AI approach. As the complexity of your AI and data increases, we help move you toward your goals with iteration cycles. This allows for smaller, early wins validating the approach and bringing confidence to more complex phases. Together, we’ll develop a roadmap for a modern, DataOps-oriented architecture and AI-first approach based on your unique requirements.

DataOps Strategy Action Plan

  • Determine business goals and a corresponding AI approach
  • Define data requirements
  • Assess enterprise data sources and infrastructure
  • Define AI / DataOps roadmap
  • Toolkit recommendation

DataOps Implementation

Building a robust system based on reliable integrations will avoid common issues that can slow down your project. By starting with a ‘Minimal Viable Prediction’ process that focuses on small chunks of data that align with your challenge, you can realize actionable insight while reducing cost and risk.
It’s important to use integrated data governance processes to ensure that data fed into the system is clean and accurate – a critical yet often overlooked aspect of analytics.

DataOps Implementation Action Plan

  • Identify a simplified yet focused question
  • Iterate to build up the required complexity
  • Implement data governance and integrate DataOps toolkit
  • Enforce a required level of data quality and version control
  • Build flexible visualization tools