# BI233 Clean Narration Script Generated for audio prep. Intended tone: clean conference presentation, concise, confident, not over-rehearsed. ## Slide 2 — Title: Data Relationship Chemistry The title is relationship chemistry because the value is not just storing financial-services data. The value comes from understanding the relationships inside that data well enough to support review, risk, and future AI workflows. ## Slide 4 — Agenda I’m going to keep this grounded in the operating problem: PE tax data is already connected in the business, but historically it has not been connected in the systems. We’ll start with the fragmented data landscape, then talk about the outcomes we designed for, the Snowflake foundation we built, where AI starts to become practical, and the three patterns we’ll keep building on. ## Slide 6 — Why PE tax data is different Private equity tax data is hard because the business itself is layered. Investments sit inside entities, entities roll up through funds, funds have investors, and each investor may have different tax attributes, jurisdictions, agreements, and expectations. A single dollar of income can flow through multiple entities before it reaches an investor, and its tax treatment can change based on that path. That is why this data is not just transactional. It is relationship data. ## Slide 7 — The data is connected. The tools are siloed. The data was always connected. Capital calls change ownership. Ownership affects allocations. Entity structure affects tax treatment. Legal terms can change investor outcomes. But the work was spread across accounting systems, entity systems, CRM, file shares, fund-admin spreadsheets, and local-country processes. So the business relationships existed, but reviewers had to reconstruct them manually every time they needed to analyze risk or prepare filings. ## Slide 8 — Target outcomes Before we designed the architecture, we aligned around outcomes. We needed transparency across the business, confidence in the data through quality checks and lineage, and better decision-making by focusing reviewer attention where risk is highest. The goal was not another data store. The goal was faster, better-targeted tax review, with enough trust in the data that teams could act on the signals the platform surfaced. ## Slide 9 — Built to principles, not just to spec These principles kept us from building technology for technology’s sake. Compliance required lineage and data quality gates. Risk management required materiality rules, anomaly scoring, and reviewer dashboards. And knowledge capture required a common data model that preserves the relationships between funds, entities, investors, and jurisdictions. Architecture choices mapped to principles, and those principles mapped back to business outcomes. ## Slide 11 — A busy quarter. Two signals. This is where the operating model changes. Without a connected model, each reviewer is rebuilding context in spreadsheets. They wait for fund-admin data, reconcile partner percentages, cross-check capital account records, and may not catch issues until late in K-1 review. With the platform, known issues are caught as data moves through the gates — for example, ownership totals that do not equal 100%. Then scoring helps reviewers find the less obvious risk: the fund or entity that deserves heightened attention during a busy quarter. So the pattern is simple: data quality catches known failures, and scoring helps reviewers find unknown or material risk. ## Slide 12 — Four governed layers on Snowflake Each layer solves a tax-data problem, not just a data-engineering problem. Raw preserves source evidence. Cleanse standardizes and validates. The common data model defines what each data point means and how it relates to the business. The consume layer gives reviewers, dashboards, client systems, and AI services governed access to the same trusted context. The key point is that governance travels with the data. Lineage and quality are built into the platform, not bolted on afterward. ## Slide 13 — Connecting functional areas, not just files This slide is the center of the story. Centralized means the data is in one place. Connected means the model understands how the business works. Entity structure validates allocations. Legal terms explain tax outcomes. Portfolio events become risk signals. Tax filing history gives us comparability across years and jurisdictions. The same governed model can support preparation, review, audit sharing, client systems, and natural-language querying without rebuilding the business logic each time. The value is governed connectivity, not centralization. ## Slide 14 — Analytics dashboard in practice The practical use cases follow from the model. For investor withholding, anomaly detection can flag when one investor is being treated differently from similar investors. That does not automatically mean it is wrong, but it focuses human review where the impact could be material. For documents, LLM extraction becomes useful when extracted terms land in governed fields connected to entities, investors, and tax outcomes. For natural-language querying, the answer has to be traceable back to governed data and lineage. AI is the payoff of the foundation, not the protagonist. ## Slide 15 — Risk-driven review in practice The goal is not to remove judgment. It is to aim judgment better. If a team has a hundred filings to review, the platform helps identify which five may deserve the sharpest attention based on materiality, anomaly signals, historical outcomes, and business context. That changes the review from a rote checklist into a risk-informed process. ## Slide 16 — Three patterns to build on The three lessons are simple. Connected models outlast individual workflows because the relationships are reusable. Governance travels with the data because lineage and quality are built into the platform. And AI becomes operational only when it is grounded in connected, validated, governed data. Without that foundation, AI is a demo. With it, AI can become part of the operating model. ## Slide 17 — Closing chemistry line The title of this session is about data relationship chemistry. For us, the chemistry is not just the data. The data points are the atoms. The value comes from the bonds: how entities connect to allocations, how legal terms affect tax outcomes, how source events become risk signals, and how all of that can be traced through governance and lineage. That is the foundation we need for better review today, better risk decisions tomorrow, and AI use cases that are grounded in the way the business actually works.