Jade Global — AI Data Readiness Checker
AI & Data Science Intern — Jade Global, enterprise technology consulting
Role
AI & Data Science Intern
Timeline
Jun 2025 – Aug 2025
Context
Jade Global, enterprise technology consulting
Type
AI Product / Data Engineering
Problem
Enterprise clients were asking Jade Global how to start their AI initiatives — and the honest answer was that most of them weren't ready. Their data had quality, consistency, and governance problems that would make any ML model unreliable. But there was no structured, scalable way to assess readiness, communicate it clearly, or give clients a concrete path forward.
Context
Jade Global works with mid-to-large enterprises on technology and digital transformation. As AI adoption accelerated, nearly every client engagement included some version of 'where do we start with AI?' The answer required an honest assessment of their data infrastructure — not a generic maturity model, but a specific evaluation of what they actually had.
Why It Mattered
Organizations that move to AI without fixing their data problems waste significant investment on models that can't perform reliably on their actual data. The failure mode is predictable and expensive. An accurate readiness assessment — one that's honest about gaps and specific about what to fix — saves clients from that outcome and gives them a credible starting point.
My Role
I defined and scoped the AI Data Readiness Checker as a product, designed the feature set, engineered the Snowflake-integrated Python pipelines, built the supporting dashboard, and presented the prototype to senior leadership.
What I Did
Defined the product scope by translating 14 data quality dimensions — including completeness, bias, consistency, freshness, and lineage — into measurable, automatable features that could run against enterprise datasets. Each dimension had to be specific enough to be evaluated programmatically, not just assessed subjectively.
Partnered with stakeholders to prioritize which dimensions to surface first. This wasn't just a technical decision — it required balancing enterprise usability (what clients can act on), technical feasibility (what's automatable in our stack), and time-to-value (what surfaces meaningful signal fastest).
Engineered Snowflake-integrated Python pipelines to profile 100+ large-scale client tables across the prioritized dimensions. Built a supporting dashboard that surfaced findings in a format analysts and engineers could use to identify and remediate issues directly. The pipelines replaced a manual QA process, reducing that effort by approximately 30%.
Presented the prototype to senior leadership. The presentation covered what the tool does, how it works, and the business case for developing it as a client-facing product to accelerate ML project launches. Got the green light.
Key Decisions & Tradeoffs
Decision 1
Decision 2
Decision 3
Outcome
AI Data Readiness Checker prototype built and presented to senior leadership. Snowflake-integrated pipelines profiling 100+ large-scale tables, reducing manual QA effort ~30%. Leadership secured support to develop it as a client-facing product.
Reflection
“This project clarified something I'd suspected but not fully understood: scoping a data tool is a product problem, not just an engineering problem. Deciding which dimensions to measure, how to surface results, and who the primary user is — those decisions determine whether the tool gets used or just built. Getting stakeholder alignment on prioritization before building anything was the move that made the rest of the project work.”