Data Sharing Strategy
Data Sharing Strategy is the design and governance pattern for moving data between companies (or between business units within a holding company) without copying, exporting, or losing control of it. The modern stack offers three architectural patterns: (1) Native warehouse sharing — Snowflake Secure Data Sharing, BigQuery Analytics Hub, Databricks Delta Sharing — where the consumer queries the producer's data without a copy ever moving; (2) Open standards — Apache Iceberg with Delta Sharing protocol — for cross-platform sharing without vendor lock-in; (3) Data clean rooms — Snowflake Clean Rooms, AWS Clean Rooms, Habu — for sharing aggregate insights from joined datasets without either party seeing the other's raw data. The strategic question is not 'which technology' but 'what business value does shared data create?' Retail-CPG collaboration, financial-fraud consortia, healthcare research, and ad measurement post-cookie deprecation are the four use cases driving most of the investment.
The Trap
The trap is treating data sharing as an export problem (CSV files, S3 buckets, SFTP) when modern warehouse-native sharing eliminates copies entirely — and with them eliminates 80% of the security and governance overhead. Companies that build data-sharing programs on top of file exports are recreating problems that platform-native sharing solves architecturally. The other trap is signing data-sharing partnerships without commercial governance — who can use the shared data for what, what counts as derivative work, what's the kill switch if a partner abuses access. KnowMBA POV: data sharing is becoming a meaningful revenue line for companies whose data has external value (retailers selling first-party data to CPG brands post-cookie, payment processors selling fraud signals, B2B SaaS selling industry benchmarks). The companies treating it as a strategic product line — with named owners, pricing, SLAs, and customer success — are capturing the value; companies treating it as a one-off engineering favor are leaving 7-figure recurring revenue on the table.
What to Do
Design data sharing as a product, not a project. Step 1: identify the use case — internal cross-BU sharing (centralization without copies), external partner sharing (revenue or strategic value), data clean rooms (cookie-deprecation ad measurement, joint customer analytics). Step 2: choose the architecture — Snowflake Data Sharing if both parties are Snowflake; Delta Sharing for cross-platform; Iceberg-based for vendor-neutral; clean rooms for privacy-sensitive joins. Step 3: build the governance layer — entitlements, usage logging, audit, kill switch, contractual TOS. Step 4: if monetizing, build the commercial layer — pricing tiers, SLAs, customer success, renewal motion. Step 5: measure: shared dataset adoption (active consumers per month), latency on shared data updates, audit completeness, and (for monetized sharing) ARR + retention by shared dataset.
Formula
In Practice
Snowflake Secure Data Sharing has become the default cross-company data exchange in industries where most major players already use Snowflake — retail, CPG, financial services, healthcare. The Snowflake Marketplace lists 2,500+ live data products, including Bloomberg market data, Weather Source climate data, FactSet financial data, S&P credit data, and dozens of fraud and identity-resolution providers. Walmart Luminate (their data product for CPG suppliers) and Kroger 84.51° both use Snowflake Data Sharing as the consumer-side delivery mechanism. Databricks Delta Sharing extends the model with an open protocol that works across platforms — adopted by Nasdaq, Atlassian, and others who explicitly wanted to avoid Snowflake-only lock-in. The strategic point: native warehouse sharing has matured to the point where building data-sharing programs on file exports is now an actively bad architectural choice.
Pro Tips
- 01
If you're sharing data externally, never accept a 'send us a CSV' request from a partner if both of you are on Snowflake/BigQuery/Databricks. Direct warehouse-to-warehouse shares eliminate the copy, the SFTP server, the security review of the export, the freshness gap, and the audit nightmare. The default proposal to any partner should be platform-native sharing.
- 02
Build a 'data product' wrapper around any externally-shared dataset — versioned schema, change log, freshness SLA, deprecation policy, support contact. Treating shared datasets as products (not feeds) is the difference between a partner who renews and a partner who churns after the first breaking change.
- 03
Data clean rooms are the post-cookie ad measurement primitive. Walmart, Kroger, Albertsons, and most major retail media networks now operate clean rooms for advertiser measurement. If you're a CPG brand or ad agency and you're not piloting clean room measurement with your top retail partners in 2025-2026, you're already behind on first-party data partnerships.
Myth vs Reality
Myth
“Data sharing is too risky from a security/compliance perspective”
Reality
Modern warehouse-native sharing is dramatically MORE secure than the export-based alternatives most companies use today. Snowflake Secure Data Sharing, Delta Sharing, and clean rooms all enforce row-level entitlements, usage logging, and instant revocation — capabilities that ad-hoc CSV exports lack entirely. Security concerns push companies away from the safer architecture toward the more dangerous one. The risk framing is upside down.
Myth
“We'll build our own data exchange platform”
Reality
Building a data exchange from scratch (entitlements, sharing protocol, billing, security, audit) is a 2-3 year platform engineering project. Snowflake Marketplace, Delta Sharing, and AWS Data Exchange exist precisely because the build is so expensive and the platform-as-a-service alternatives are so good. Custom data exchanges almost always fail in year 2 when the initial team moves on and operational costs become unbearable.
Try it
Run the numbers.
Pressure-test the concept against your own knowledge — answer the challenge or try the live scenario.
Knowledge Check
A retail company has 12 CPG brand partners who each currently receive a weekly SFTP CSV of sales data. Each export takes 4 engineering hours of maintenance per partner per quarter, and partners frequently miss the latest data due to SFTP failures. Both the retailer and most partners are on Snowflake. What is the architecturally correct migration?
Industry benchmarks
Is your number good?
Calibrate against real-world tiers. Use these ranges as targets — not absolutes.
Snowflake Marketplace Data Products
Snowflake Marketplace 2024 statisticsLive data products on Snowflake Marketplace
2,500+ as of 2024
Notable data product publishers
Bloomberg, FactSet, S&P, Weather Source, dozens more
Industries dominant in marketplace adoption
Financial services, retail/CPG, ad-tech
Source: https://www.snowflake.com/data-marketplace/
Real-world cases
Companies that lived this.
Verified narratives with the numbers that prove (or break) the concept.
Snowflake Marketplace + Walmart Luminate / Kroger 84.51°
2020-present
Major US retailers (Walmart, Kroger, Albertsons) have built first-party data product businesses on top of Snowflake Secure Data Sharing. Walmart Luminate and Kroger 84.51° both deliver shopper insights, sales data, and audience-targeting signals to CPG brand partners through Snowflake-native shares — no exports, no SFTP, real-time freshness, full audit. Both have launched clean room products for advertiser measurement post-cookie. These data product lines have grown into 9-figure ARR businesses for the retailers, with high-margin economics because the platform-native sharing eliminates most of the engineering and operational overhead of export-based alternatives.
Architecture
Snowflake Secure Data Sharing + Clean Rooms
Use Cases
CPG insights, ad measurement, audience targeting
Reported Revenue Lines
9-figure ARR per major retailer
Margin Profile
70%+ contribution margin at scale
First-party data + native warehouse sharing has become a major retail revenue line. The retailers who moved fastest captured a structural advantage in the post-cookie advertising ecosystem.
Databricks Delta Sharing
2021-present
Databricks introduced Delta Sharing as the first open protocol for secure cross-platform data sharing — Delta Sharing servers can be consumed by Spark, Pandas, Tableau, Power BI, and any other client implementing the open spec. Notable adopters include Nasdaq (sharing market data with research partners), Atlassian (cross-product data sharing), and Shell. Delta Sharing's open-protocol design explicitly targets customers who want native warehouse sharing semantics without Snowflake lock-in. The protocol has been donated to the Linux Foundation to remove single-vendor governance concerns.
Protocol
Open Delta Sharing (Linux Foundation)
Notable Adopters
Nasdaq, Atlassian, Shell, others
Differentiator
Cross-platform, open-source spec
Strategic Position
Anti-Snowflake-lock-in for sharing
Open-protocol sharing matters for organizations crossing multiple platforms or wary of single-vendor lock-in. The technical capability is now table stakes; the strategic question is which protocol your partner ecosystem will converge on.
Hypothetical: Mid-Market Retailer
2021-2022
A regional grocery chain decided to build a custom data exchange portal for CPG partners — REST APIs, custom entitlements, web UI. Total spend: $3.2M over 24 months. By launch, the portal supported 8 brand partners with limited query patterns. Partners complained the API was less flexible than direct SQL and the portal was 'a worse version of what Snowflake gives us with our other retail partners'. After a leadership change, the program was rebooted on Snowflake Data Sharing, retired the custom build, and reached 20+ partners within 6 months. The lesson: in a category where platform-native solutions are 80% of what you'd build, building from scratch is almost always the wrong call.
Wrong-Build Investment
$3.2M over 24 months
Partners on Custom Portal
8
Partners on Snowflake Sharing (post-reboot)
20+ in 6 months
Architectural Lesson
Use the platform-native pattern
Custom data exchanges almost always lose to platform-native sharing. Building when Snowflake/Databricks already offers 80% of what you need is reinventing infrastructure that won't catch up.
Decision scenario
Launching a Data Product Line
You're CDO at a $3B regional grocery chain. The CEO has approved a strategic initiative to monetize first-party shopper data to CPG brand partners post-cookie. You have rich loyalty data, transaction data, and basket-level history across ~5M shoppers and ~800 CPG brands. Snowflake-based warehouse. The question is how to architect, govern, and commercialize the data product line.
Shoppers (loyalty)
5M
CPG Brands as Potential Partners
~800
Existing Architecture
Snowflake warehouse, dbt transformations
Approved Investment
$5M over 18 months
Strategic Goal
$10M+ ARR data product line by year 2
Decision 1
The product team proposes building a custom data exchange portal with REST APIs, web UI, and proprietary entitlements (estimated $4M build, 18 months). The data team proposes launching on Snowflake Marketplace + Snowflake Data Sharing + Clean Rooms (estimated $1M build, 6 months) with the remaining budget invested in commercial team, partner success, and clean room measurement use cases.
Custom data exchange portal. Maximum control, branded experience, proprietary IP.Reveal
Snowflake Marketplace + Data Sharing + Clean Rooms. Spend $1M on platform launch + 5 dataset products. Reinvest $4M in commercial team (sales, partner success, clean room measurement engineering).✓ OptimalReveal
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Beyond the concept
Turn Data Sharing Strategy into a live operating decision.
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Typical response time: 24h · No retainer required
Turn Data Sharing Strategy into a live operating decision.
Use Data Sharing Strategy as the framing layer, then move into diagnostics or advisory if this maps directly to a current business bottleneck.