How we help our clients
We know poor data quality is the silent killer of AI projects, with legacy mess, inconsistent schemas, and governance gaps leading to garbage models, wasted spend, and frustrated stakeholders who question the hype. Leading Point’s Data & Analytics service transforms chaos into AI-ready gold: clean, traceable, scalable datasets that power reliable outcomes every time.
We start with deep audits, roll out governance at pace, and optimise pipelines for ML scale, all informed by our track record in regulated data estates. Expect 4-8 week sprints yielding quality scorecards, automated flows, and benchmarks that boost model accuracy by 20-40%. No more data debt holding back your AI ambitions, just high-fidelity assets ready to deliver business value.
Our data & analytics services
Data Quality & Maturity Audit
We know hidden data issues cause AI failures. Over 4-6 weeks, we profile 10-20 key datasets using automated tools, score quality across accuracy/duplication/timeliness (target: 95%+), and prioritise fixes via heatmaps. Deliverables: audit report with root causes, remediation backlog in Jira/Excel, and quick-win scripts, lifting data readiness from 60% to 90% in months.
Data Governance Implementation
We know siloed data slows innovation. We define stewards/lineage owners, deploy a data catalogue (e.g., OpenMetadata / Collibra integration), and automate metadata flows with policy enforcement. Outputs include governance charter, role assignments, and dashboard for compliance KPIs, streamlining access requests by 70% while meeting GDPR/FCA standards.
AI-Optimised Pipelines
We know inefficient pipelines bottleneck models. We design/build feature pipelines using tools like Feast/DBT, handle versioning/schema evolution, and test for scale (e.g., 1M rows/min). You get deployable code, monitoring alerts, and cost optimisation guide, cutting data prep time from days to hours.
Data Readiness Benchmarking
We know fitness-for-purpose is key. We evaluate datasets against AI benchmarks (e.g., bias checks, volume for training), simulate model impacts, and recommend augmentations like synthetic data. Deliverables: benchmark report, gap closure plan, and prototype clean dataset, ensuring 85%+ model accuracy uplift potential.
