DataOps Services
"DataOps is a collaborative data management practice that emphasizes communication between, among, and across various IT roles, from data scientists to data managers to developers, to improve the speed and accuracy of analytics. It's inspired by the DevOps movement and focuses on the entire data lifecycle."
Amol Malpani (CTO, Cloudaeon)
Why choose us?
DataOps brings a holistic approach to data management, ensuring that data workflows are agile, collaborative, and of high quality, thus facilitating businesses to make data-driven decisions efficiently and effectively.
If you're thinking about how to make AI drive better business outcomes, start with a free readiness assessment and let us know when you're ready to start the conversation.
Key Use Cases
- Accelerated Data Delivery: Faster delivery of cleaned and processed data for analytics or machine learning.
- Improved Data Quality: Enhanced accuracy and reliability of data through monitoring and feedback loops.
- Collaborative Development: Smoother collaboration between IT roles leading to more consistent and well-understood data products.
- Compliance and Governance: Ensuring data is handled according to regulations and best practices.
Key Benefits
- Data Silos: Breaking barriers between teams and data sources.
- Slow Iteration: Accelerating the time from data development to deployment.
- Data Quality Issues: Proactive monitoring and immediate fixes ensure higher data quality.
- Operational Inefficiencies: Streamlining operations and improving collaboration reduces redundancies and errors.
What We Offer
- Agile Data Development: Adapting agile methodologies for data-related developments.
- Collaborative Data Management: Enhancing collaboration between data professionals and other IT roles.
- Automated Data Delivery: Leveraging automation tools for delivering data.
- Integrated Tools and Technologies: Combining different data tools seamlessly.
- Continuous Data Governance: Ongoing data quality and metadata management.
- Monitoring and Alerting: Keeping an eye on data pipelines and ensuring data quality.
- Feedback Loop: A mechanism to ensure continuous feedback for continuous improvement.
How We Work
- Define Objectives: Understand the business and analytics goals.
- Plan: Identify data sources, required tools, and define data workflows.
- Collaborate: Bring together data scientists, engineers, and other stakeholders to ensure smooth development.
- Develop and Test: Build data pipelines, models, or applications in a continuous integration environment.
- Deploy: Move changes to production while ensuring there are mechanisms for rollback in case of issues.
- Monitor: Ensure data quality, pipeline health, and model accuracy.
- Feedback: Continuously gather feedback from end-users and iterate on the solutions.
Managed Services
- Data Platform Setup & Management: Setting up and maintaining the underlying data infrastructure.
- Data Integration Services: Offering tools and expertise to integrate data from diverse sources.
- Automation Tools: Tools that automate data workflows, from ingestion to analytics.
- Data Governance and Quality: Implementing and maintaining tools for data quality checks, metadata management, and lineage tracking.
- Monitoring & Alerting Solutions: Providing platforms to monitor data health, pipeline statuses, and AI model performance.
- Collaboration Platforms: Tools that foster better communication between teams.
- Training & Consultation: Offering training on best practices in DataOps, data management, and AI.
- Security and Compliance: Ensuring that data operations are secure and comply with industry regulations.
Readiness Check
In 10 minutes, get a score to assess your Readiness & Maturity. You'll get a clear score to help your identify areas of improvement.
Getting Started
If you are ready to engage with us and would like do dive deeper into the subject, go ahead and book in a Discovery Workshop with our Practice Leads.