Investment teams live and die by information quality and speed.
Yet many still rely on a forest of spreadsheets, shared drives, and manual exports from core systems.

A good investment IT data platform doesn’t start with “big data” buzzwords—it starts with what decisions need to be made.

Start with critical decisions and time horizons

For each investment team, clarify:

• What are the recurring decisions? (allocation, risk, entry/exit, rebalancing)
• Which data points are non‑negotiable for those decisions?
• How often does information need to update?

From there, design:

• A golden dataset for positions, exposures, and cash.
• A clear lineage from source systems (trading, risk, accounting) to that golden view.
• A simple semantic layer analysts can query without fighting schemas.

Ingest once, reuse everywhere

Avoid building one-off pipelines for each dashboard or report.

Instead:

• Centralize ingestion and transformation into a shared data model.
• Expose it via SQL, BI tools, and notebooks.
• Tag datasets by owner, quality level, and update frequency.

This turns new analytics use cases into configuration, not new ETL code every time.

Governance without paralysis

Regulated investment environments need strong controls—but also speed.

We balance this by:

• Defining data owners and stewards per domain.
• Tracking who changed models, fields, and business logic.
• Providing sandboxes where analysts can experiment safely on curated data.

The goal is to make the compliant path the easiest one to use.

Build for questions, not just KPIs

Dashboards are useful, but real insight happens when analysts can explore:

• Drill from aggregated risk to individual positions.
• Compare scenarios and stress tests.
• Ask “what if” without waiting weeks for IT.

Design your investment IT data platform so that asking new questions is cheap—because markets won’t stop changing.