DataOps for Better and Faster Analytics

[Video introduction] Adopting the DataOps Methodology is helping agile teams deliver data and analytics faster and more manageable in modern data infrastructure and ecosystems. DataOps is critical for companies to become resilient with data and analytics delivery in a volatile and uncertain global business environment. Going beyond DevOps for continuous deployments, DataOps leverages principles from other disciplines to evolve data engineering and management.

Companies need data and analytics more than ever to be agile and competitive in today’s fast-changing environment. DataOps can be an enterprise-wide initiative or an independent agile delivery team working to improve how they deliver data analytics for their customer. Gaining traction takes time and ongoing support.

This seminar will cover:

• The challenges in current data environments and IT
• What DataOps is and how it differs from other approaches
• Which principles and technologies to focus on initially
• How to adopt DataOps to speed analytics development and delivery
• How to continuously engineer, deploy, and operationalize data pipelines with automation and monitoring
• Setting expectations and planning for DataOps maturity.

Course Description

1. Understanding why we need to change

• How business Analytics has changed from diagnostic to predictive
• How data sources are increasing
• The impact of data integration on Data Management
• Changes in IT development methodologies and organizations
• Supporting new data products
• How DataOps is emerging as the next era
• Reviewing the Agile Manifesto
• Important aspect of DevOps
• Review statistical process control for DataOps
• How DataOps can embed Data Quality and Data Governance
• Defining DataOps and the DataOps Manifesto
• Comparing DevOps to DataOps

2. Making DataOps Work

The 7 key concepts to focus on for DataOps

• How Connectors can make a difference
• How engineered data pipelines will work
• How “data drift” will impact data work
• Set up repositories for Data Governance and Data Quality
• The role of data hubs and MDM
• How to set up measurements correctly
• Leveraging DataOps Platform instrumentation

The 2 key processes to focus on for DataOps

• Components needed to deliver on business ideation
• Building data and Analytics deliverables with DataOps

3. Managing DataOps: defining Metrics and Maturity Models

• Defining Metrics for Data and Analytics delivery
• Key DataOps metrics
• How to leverage reusability metrics
• Reviewing metrics for process improvement
• Maturity stage of DataOps adoption
• CMMI-based Maturity Model
• IBM Maturity Model.