Establish future-ready data governance
The rapid increase in the flow and pace of data has put pressure on enterprises to rethink their governance strategy. Without an agile DataOps methodology in place, you may end up running expensive, complex, and time-consuming governance programs that fail to adequately capitalize on data assets. Also, with lackluster data quality and poor discoverability mechanisms continuing to torment enterprises, your teams may struggle to discover the right data assets at the right time.
Tiger Analytics focuses on enabling you to directly align lean data governance practices with your enterprise’s business growth goals. You can harness our Data Engineering expertise to evolve your traditional policy-driven governance programs into automation-led enablement practices. We help establish a lean governance framework that puts your teams in a position to make intelligent data-backed decisions.
Discover data with ease
Enable the quick and secure discoverability of all your curated data assets.
Proactively ensure data quality
Detect data quality issues early and with high reliability to prevent business risks.
Optimize data footprint
Reduce and optimize the data footprint while decluttering workflows.
Minimize the cost of governance
Reduce the overheads of repetitive and ineffective manual governance tasks.
Our Unique Approach
Tiger Analytics aims to give you full control over the data lifecycle in your enterprise. Our customizable lean data governance frameworks are powered by years of experience in helping clients rethink the speed of data-led innovation. We simplify how you deal with internal access controls and privacy-related compliance rules while accelerating the drive towards data quality.
Consulting and Advisory
- Lean data governance strategy and roadmap
- DataOps adoption strategy and roadmap
- Data catalog technology and vendor selection
- Data fabric solutions
- DataOps service implementation
- Integrated and rich data catalogs
- Automated data harmonization and data quality automation
- ML-driven microservices
- Data rationalization and automated data lifecycle management
- Automated capturing of data lineage