Trusted, governed, AI ready data
Data lakes without governance become data swamps. Gartner puts the average cost of poor data quality at US$12.9 million a year; MIT Sloan Management Review research estimates 15–25% of revenue. AI programmes fail on the same inputs. A governed lakehouse, with ownership, quality rules and lineage designed in, is the foundation for every analytics and AI ambition.
of poor data quality
impact of bad data
time
Trusted data built for analytics and AI
Architecture and Platform
Lakehouse design on Snowflake, Databricks, Azure, AWS or Google Cloud.
Ingestion and Pipelines
Batch and streaming from ERP, CRM, operational, IoT and external sources.
Governance
Catalogue, ownership, access policy, data quality monitoring and lineage, operationalised through Nucleus.
Security and Compliance
Encryption, masking, retention and controls aligned to DPDP, GDPR and sector regulation.
AI Readiness
Feature and vector stores for CognitiveCore, BigBrain and GenSolve AI.
FinOps
Cost monitoring and optimisation of the platform.
Platforms We Work With
US$12.9m
Average annual cost of poor data quality (Gartner)
15–25%
Of revenue lost to bad data in many companies (MIT Sloan Management Review)
45–80%
Of analyst time consumed by data preparation without governed platforms (industry surveys)
80%
Less preparation time and 5× faster decisions once the foundation is in place [PP claim]
