Verily Data combines automated data profiling with expert human review to assess whether you are ready for machine intelligence, analytics, or predictive initiatives — before you commit budget to building.
Organizations investing in AI and analytics — predictive models, operational dashboards, risk scoring — routinely hit blockers that could have been caught in advance.
Missing fields, inconsistent formats, and sparse historical records silently undermine any model or analytics initiative you try to build on top of them.
Teams don't know whether their data exports and systems have enough signal and structure to support predictive analytics — until it's too late.
Without an upfront assessment, data engineering projects hit unexpected blockers weeks or months in — when the cost to fix them is highest.
You provide your goals and your data. Our pipeline runs a structured assessment, and our team reviews and contextualizes the findings before you receive them.
Share your business objectives and case management data exports (CSV or database). We define what success looks like.
Automated quality scoring, statistical profiling, anomaly detection, and cross-table relationship analysis across your dataset.
We map your goals against your actual data, identify what's missing, and score feasibility (High / Medium / Low) for each objective.
Industry research and best practices are applied to generate a prioritized remediation roadmap — concrete actions ranked by impact and effort.
Receive a PDF audit report and an interactive dashboard. Our team walks you through findings and next steps.
Every engagement produces concrete deliverables you can act on immediately.
A one-page overview of data health, top risks, feasibility scores per goal, and prioritized quick wins — designed for decision-makers.
Comprehensive findings including data health scorecard, gap analysis by goal, feasibility scorecard, and severity-sorted issues with specific field-level detail.
Explore your audit results interactively — filter gaps by severity, run what-if scenarios to see how fixing specific issues affects feasibility, and drill into any table or column.
Per-objective evaluation of whether your data can support each analytics initiative, with a HIGH / MEDIUM / LOW confidence rating and the reasoning behind it.
A 0–100 trust score for each table in your dataset, with a breakdown across completeness, consistency, and anomaly rate — so you know exactly where the weak spots are.
A ranked action plan showing which data gaps to fix first, with effort and impact estimates, risk flags, and the specific changes needed to unlock each blocked goal.
No commitment required. Tell us about your situation and we'll suggest the right approach.
We'll start with a short conversation to understand your goals and what data you have. From there, we'll scope the audit and run it — you'll get findings you can act on, reviewed by a human who understands legal data.