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. That thinking, an emerging approach, is at the core of DataOps. It’s a methodology with automated, process-oriented data analytics that reduces insight-delivery-time and improves insight quality. It streamlines messy data pipelines and affirms the unified transparent app development and delivery processes. AI-defined goals are at its core, 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. We help move you toward your goals with iteration cycles as the complexity of your AI and data increases. This allows for smaller, early wins validating the approach that bring 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.

Your organization’s maturity will determine whether to start with simple reporting — easily achieved with tools like Microsoft PowerBI — or more sophisticated, ad-hoc analysis of real-time data requiring specialized tools and skills.

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 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 avoids common reoccurring issues that slow your project down. Integrated data governance processes ensure that data fed into the system is clean and accurate – a critical yet often overlooked aspect of analytics. Unique user-visualization preferences allow for customized views.

The key to success is a focus on continuous results. Start small and iterate. A ‘Minimal Viable Prediction’ focuses on the number one problem you want to solve, and assembles data that correlates with that problem. Small, actionable insights validate your approach, before you broaden your search, to reduce risk and cost.

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
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