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
