We talk to an enormous variety of asset managers every week—alternatives, long-only, boutiques, global giants—and here’s the truth:
- Everyone’s “modernizing their data strategy.”
- Everyone’s “exploring AI.”
- And most have Snowflake… somewhere.
But only a subset of firms are pulling it all together—in a way that’s meaningful, aligned to how they make money, and built to scale. Here’s what those mature firms are actually doing, broken down by firm type.
Small to Mid-Sized Managers (<$50B AUM)
Mostly focused strategies: SMAs, mutual funds, ETFs, or private credit.
1. Digital strategy = automation + leverage.
They aren’t building massive data science teams—they’re automating lower value tasks:
- Fund reporting automation (PDF parsing, Excel-to-database ingestion)
- AI-assisted analyst workflows (e.g., memo summarization, news classification)
- CRM and client data unification to personalize outreach
2. Their Snowflake use case is focused.
These firms treat Snowflake like a clean, central data lakehouse, not a pet project.
- EDM-lite: Reference data + position data + benchmark data = golden records
- Quick reporting wins for investor ops and compliance
- Plugged into Sigma or Power BI for end-user access
3. They buy TIME, not tools.
No boiling the ocean. They pick 1–2 domains (like client reporting or investment research) and go deep with a partner who brings the talent and IP to move fast.
Mid to Large Managers ($50B–$500B AUM)
Multi-strategy, global footprint, more complex operations, and investor types.
1. Data is treated like a product.
They’ve moved from “building a data platform” to managing data as a set of products:
- Exposure analytics
- ESG data aggregation
- Client servicing data marts
Each with a domain owner, SLA, and roadmap.
2. AI is embedded into real workflows.
Not just pilots—in-production tools built around:
- Deal memo review for PE/credit
- Portfolio monitoring (entity risk, market sentiment)
- Auto-tagging and enrichment of documents and emails
- NLP over call transcripts for sentiment trends
3. Snowflake is the platform, not a component.
This is where firms:
- Centralize security master, position, risk, and client data
- Build data contracts across domains
- Extend into AI/ML with Snowpark or external LLMs that come to the data
- They industrialize self-service.
Tools like Sigma sit on top of governed models, enabling PMs, client teams, and compliance users to get what they need—without ticket queues.
Alternatives Powerhouses / Complex Multi-Manager Models
Think PE, private credit, hedge fund platforms, and fund-of-funds.
- Intelligence is a competitive advantage.
These firms live and die by:
- Proprietary deal flow and the speed of underwriting
- Real-time risk and performance visibility
- Sophisticated client mandates and reporting
Their digital strategy is surgical:
- AI-powered diligence (summarizing CIMs, scraping web/private data, entity linking)
- Real-time dashboards from portfolio companies or underlying funds
- Deep integrations with external vendors (e.g., iLevel, Burgiss, DealCloud) and unstructured data ingestion pipelines
2. Snowflake becomes an intelligence engine.
It’s the staging ground for:
- Entity resolution and tagging across sources
- Real-time KPI rollups across illiquid portfolios
- Secure, governed collaboration between ops, IR, and deal teams
- Digital strategy is owned at the C-level.
These firms often have a Chief Data Officer or Chief Digital Officer with direct accountability to the CEO or CIO—because data isn’t back-office, it’s strategic.
Common Traits Across the Best Firms
No matter the firm size or strategy, the leaders share a few key traits:
- They start with business workflows. Not “data for data’s sake.”
- They staff lean, but with high-leverage roles. Data product managers, not armies of analysts.
- They don’t chase perfect—they deliver repeatability. MVPs that scale.
- They treat AI seriously— they have been learning by doing for some time.
- They demand ROI in months or quarters, not years.
Where Are You?
Ask yourself:
- Do we have a clear inventory of our core data products?
- Can our PMs or IR teams answer complex questions without sending an email to IT?
- Are our AI efforts plugged into real workflows—or just experiments?
- Is our Snowflake instance generating actual business leverage?
If not, that’s a signal—not a failure. But it’s time to move from adopting technology to executing strategy. That’s the real difference between “exploring” and “maturing.”