Breaking the Data Barrier: Turning Compliance and Complexity into Alpha

Every investment firm wants to move faster, but most are slowed by frictions in their data velocity. Provisioning delays, lack of access to data sources, and compliance gatekeeping create decision latency that collectively work to kill alpha. The edge now goes to teams that operationalize data intelligence in financial services with governed, universal access, achieving true data readiness for analytics and AI.

How Starburst provides universal data access

This is where Starburst changes the equation. With secure, federated access to all your data, wherever it lives, you can achieve governed analytics and AI without the need for unnecessary data centralization. Starburst provides built-in governance, lineage, and fine-grained security, giving compliance teams confidence while keeping innovation alive. And critically, your data stays securely in place.

Why data access in financial services is a special problem

In financial markets, milliseconds matter. By eliminating data friction, Starburst helps firms uncover alpha generation faster, act with precision, and scale securely across clouds and regions.

Let’s look at how this happens. 

Checklist for adopting AI in financial services

To help teams turn these principles into action, we’ve created a practical checklist for delivering governed analytics and AI without moving data, purpose-built for financial services.

This checklist is designed to help hedge funds, asset and wealth managers, and fintech innovators eliminate decision latency with governed, federated access, so analytics and AI move at the speed of opportunity.

1) Align on outcomes and metrics

Before you get started, it’s best to determine your goals and how you’ll determine whether you’ve met them. In practice, that often involves the following steps. 

• Define business outcomes, including trade analytics speed, faster strategy testing, AI onboarding, and client personalization.
• Set operational KPIs, including data-source onboarding speed, cross-source query latency, self-service access rate, model deployment speed, number of models deployed, and compliance targets achieved.
• Document KPI trees, including baselines, targets, and dashboards.

2) Map and classify your data

Next, map and classify all of your data. This will allow you to understand what you’re working with, and should involve the following steps.  

• Inventory sources, including market or ticker data, reference, alternative, and auxiliary data, CRM, OMS or EMS, risk, pricing, and research notes.
• Classify sensitivity and residency, including PII, strategy IP, regulated data, and regional constraints.
• Standardize tags, including domain, sensitivity, PII, owner, retention, residency, and quality.

3) Design governance you can actually use

For the next stage, it’s time to think about data governance. Importantly, this needs to come early in the process. If you treat it as an afterthought, data governance quickly becomes unmanageable. Instead, you should think of adopting the following steps. 

• Define roles/entitlements, including traders, quants, analysts, data science, risk, compliance, and operations.
• Implement Role-based access control (RBAC) and + Attribute-based access control (ABAC) for row and column policies. 
• Enable dynamic masking using natural SQL.
• Require lineage for certified datasets.

How does this look in practice? The following example walks through the pseudo-code policy:

policy: client_positions_masking
when:
  user.role not_in: ["Risk", "Compliance"]
apply:
  columns:
    account_id: hash
    client_name: redact
rows:
  allow_if: region == user.region

4) Stand up the federated access layer

Now it’s time to begin federating your data. To do this, you’ll create a federated access layer that connects to multiple data sources as if they were a single data source. To do this, follow these steps. 

• Deploy a federated query layer (Starburst can help here) and connect priority sources first. These might include object storage data lake, databases, data warehouses, or streaming data.
• Integrate IdP/SSO, while centralizing auth, roles, and group sync, and enforcing fine‑grained policies at the access layer.
• Enable performance guardrails, including predicate pushdown, caching/materialized views, and workload management.

5) Publish governed data products for self‑service

The next step is to consider optimization. You’ve connected the data sources. Now it’s time to make them easy to access using data products. This will facilitate both self-service and cross-team collaboration. It also provides a foundation for easy access by AI models, creating a clear technological onramp to AI adoption. To do this, consider the following steps. 

• Curate certified views, this might include positions, P&L, exposures, client 360, tick+alt joins, and sanctions‑filtered entities.
• Define shared metric semantics and time windows by connecting BI and notebooks and shipping starter queries/templates.

How will you do this in practice? The following SQL example shows how you might accomplish this step using a cross‑market join. 

SELECT
  t.symbol,
  t.event_time,
  t.last_price,
  a.sentiment_score,
  p.position_size,
  p.exposure_usd
FROM market.tick_us t
JOIN altdata.news_sentiment a
  ON t.symbol = a.symbol
 AND a.event_time BETWEEN t.event_time - INTERVAL '5' MINUTE AND t.event_time
JOIN risk.positions p
  ON p.account_id = CURRENT_USER_ACCOUNT()
WHERE t.event_time >= CURRENT_DATE - INTERVAL '7' DAY
  AND policy_allow_row(t.customer_segment) = TRUE;

6) Enable AI/ML with compliant, production‑grade data

With the foundational access layer provided by data products, it’s now time to introduce AI and ML workflows. You’ll want to do this slowly and thoughtfully, starting with production-grade data first, and moving towards additional datasets over time. The following steps are worth keeping in mind during this stage. 

• Create curated, masked, time‑consistent training views.
• Leverage time‑travel syntax to provide temporal views of data.
• Define feature contracts by materializing hot features selectively and keeping sensitive sources federated.
• Capture lineage from source to model, ensuring reproducibility and retention of experiments.
• Integrate Starburst AI agents and workflows with public/private LLMs to enable applications and natural‑language interactions with governed, enterprise‑secure data and model guardrails.

7) Engineer performance, cost, and reliability

Now that you’ve got a working AI workflow, it’s time to begin optimizing it for performance, cost, and reliability. The principles are similar to those of any other data engineering project and should involve the following steps. 

• Isolate workloads, including making distinctions between ad‑hoc, scheduled, and critical workflows. Apply smart acceleration, including result-set caching, selective materialized views, warp speed, filesystem caching, and metadata caching.
• Set SLOs by implementing query tracing, slow‑query surfacing, data quality monitors, and SLA alerts.

8) Operationalize compliance and audit

At this stage, it’s good to introduce compliance and audit checks. Again, the principles are similar to those of any other data engineering project, except that they now include AI workflows. Consider using the following steps. 

• Centralize audit logs, including a full account of key questions like who, what, when, and where. 
• Ensure that policies are applied appropriately, and that you have scheduled access recertifications.
• Enforce lifecycle, including retention, legal holds, and defensible deletion by tag.

9) Pilot with 2–3 high‑value use cases

Now that you have predictable, governed, auditable results, it’s time to begin using your workflows in real-world applications. To do this, you might consider 2-3 high-value use cases to start and then expand from there. 

Examples might include:

• Cross-market trade analytics, involving tick + alt data across regions. 
• Client 360 views
• Model onboarding to governed sources
• Trading-strategy insights
• Trade-anomaly detection
• Sanctions screening

10) Scale through enablement and governance ops

Now it’s time to scale. You’ve got a working model and several real-world applications. The next step is to push further into other use cases. 

• Launch office hours, playbooks, templates, or a data product marketplace. 
• Establish a lightweight policy change board and review “golden” products. 
• Track ROI and publish value reports.

30‑60‑90 day plan (quick checks)

How should you measure success? Like any project, it’s best to approach it in a time-bound way. We suggest using a familiar 30-60-90 framework to model your AI adoption projects. The principles are the same as for any project, which makes them both familiar and achievable. 

Consider the following key milestones. 

30-day milestone

• Establish inventory and classify sources 
• Finalize your roles and policies 
• Deploy federated access in your development environment 
• Connect 3–5 sources 
• Select pilots

60-day milestone

• Publish your initial data products
• Enable fine‑grained controls
• Centralize audit logging
• Deliver two pilot outcomes

90-day milestone

• Add AI/ML training views and feature contracts
• Discover and test agentic AI and AI workflows
• Implement isolation, caching, and SLO monitoring
• Complete first access recertification

Take the next step

Ready to see the end‑to‑end approach in action? Watch the on‑demand session: Data‑Driven Alpha — Competitive Advantage through Data and AI in Financial Services.