ABM LinkedIn Ad Copy — FY27 Q1
Program: FY27-Q1 ABM Pilot — Top 8 Named Accounts
Format: 3-Ad Journey per Account — Problem → Solution → Why DataHub
Channel: LinkedIn Single Image / Static (animated possible)
Owner: Wacarra Yeomans
Date: May 2026
Format guidance (from Marco's spec): Headline ≤ 7 words. Body copy ≤ 20 words. Copy should work as a standalone image text overlay — not a long-form ad. Account-specific audience targeting handles relevance; copy handles resonance.
Journey logic: Each account gets 3 sequential ads. Audiences see them in order over 2–4 weeks. Ad 1 earns attention (the problem they live with). Ad 2 makes them curious (the solution exists). Ad 3 earns the click (why DataHub specifically for them).
| Ad | Goal | Tone |
|---|
| Ad 1 — The Problem | Name the pain precisely enough that they feel seen | Observational, not alarming |
| Ad 2 — The Solution | Make the fix feel tangible and within reach | Direct, confident |
| Ad 3 — Why DataHub | Give a specific, credible reason to click | Proof-forward, low pressure |
Brooks Running Tier 3 — Tech Stack
dbtAirflowSnowflakeDISCOVER stageApparel / Retail
Account Value Prop (internal — not ad copy)
Brooks runs dbt, Airflow, and Snowflake across their retail and supply chain data stack — which means the infrastructure is solid, but lineage is the gap. When a dbt model changes, there's no fast path to knowing which downstream reports or seasonal planning processes break. DataHub connects Brooks' transformations to every downstream consumer, making it possible to see what's affected before a change ships. Discovery and observability close the loop: when analysts can find certified, current data without hunting through Slack, the team spends its time acting on consumer insights instead of chasing them.
Ad 1 — The Problem
Your dbt pipelines break. You find out later.
When Airflow and Snowflake don't tell you what changed upstream, debugging means hours of manual digging your team doesn't have.
Headline: 7 words · Body: 20 words
Ad 2 — The Solution
See exactly what breaks before it does.
Column-level lineage connects every dbt transformation to the reports it feeds — so your team knows the impact before they make the change.
Headline: 7 words · Body: 20 words
Ad 3 — Why DataHub
Connects to dbt, Airflow, and Snowflake. Already.
DataHub plugs into your existing stack. Automated lineage, real-time observability, and conversational search — no manual cataloging required.
Headline: 7 words · Body: 18 words
Hulu Tier 5 — Industry Fallback
No tech stack confirmedDISCOVER stageStreaming / Media$5B revenue
Account Value Prop (internal — not ad copy)
At Hulu's scale, content, advertising, and user behavior data pipelines run in parallel — and when metrics don't align between teams, hours go into debugging instead of building. DataHub brings discovery, lineage, and observability into one platform so analysts can find trusted data in seconds, engineers can trace anomalies to the source, and platform teams can maintain quality without blocking the product teams that depend on them. For a streaming company where data freshness drives both content decisions and ad revenue, it's the layer that makes the rest of the stack reliable.
Ad 1 — The Problem
Three teams. Three versions of the same metric.
When content, ads, and user data live in silos, your engineers spend more time reconciling numbers than building what's next.
Headline: 7 words · Body: 19 words
Ad 2 — The Solution
One platform. Every data asset. Instantly findable.
DataHub unifies discovery, lineage, and observability so your data team traces anomalies to the source — fast, not over Slack.
Headline: 6 words · Body: 19 words
Ad 3 — Why DataHub
Netflix runs on DataHub. So can Hulu.
Netflix uses DataHub to manage millions of data assets across their platform. See what the same foundation looks like for your stack.
Headline: 7 words · Body: 20 words
Block Expansion — Existing Customer
AI ToolingDatabricksdbtAirflowSnowflakeDEPLOY stageFintechCurrent Customer
Account Value Prop (internal — not ad copy)
Block is already a DataHub customer — but the teams now building AI models on Databricks, Snowflake, and their fintech stack have needs that go beyond their current deployment. DataHub Cloud's MCP Server and Context Graph let AI agents query trusted metadata at machine scale, while freshness monitoring and automated data contracts prevent the training data quality issues that derail production AI. For Block's engineering teams shipping payment and financial products, Cloud is what makes AI initiatives reliable enough to trust at transaction scale.
Ad 1 — The Problem
AI models need metadata. Your catalog can't deliver it.
Production AI requires a metadata layer agents can query in real time — not a catalog built for human search.
Headline: 9 words (can trim: "Your catalog wasn't built for AI agents.") · Body: 19 words
Ad 2 — The Solution
Give your AI agents trusted data, automatically.
DataHub Cloud's MCP Server connects AI agents directly to certified, real-time metadata — so production models at Block run on data they can trust.
Headline: 6 words · Body: 22 words (can trim to: "MCP Server feeds your AI agents trusted, real-time metadata — built for production, not pilots.")
Ad 3 — Why DataHub
You're already running DataHub. Cloud adds the rest.
Freshness monitoring, automated data contracts, and AI agent infrastructure — built on what your team already knows and trusts.
Headline: 8 words · Body: 17 words
Apollo Global Management Tier 3 — Tech Stack
DatabricksdbtSnowflakeDISCOVER stageCapital Markets
Account Value Prop (internal — not ad copy)
Apollo's capital markets operations run on data accuracy — from investment analysis to regulatory reporting, where lineage gaps in undocumented data transformations can create real compliance exposure. Running Databricks, dbt, and Snowflake gives Apollo the modern infrastructure, but without automated lineage tracking, changes to data models can propagate undetected to downstream regulatory reports. DataHub connects Apollo's transformations to every downstream consumer with the audit trail that BCBS 239 compliance requires — turning point-in-time audit scrambles into continuous compliance monitoring.
Ad 1 — The Problem
Lineage gaps in capital markets aren't just technical.
When dbt models feed regulatory reports and nobody can trace how, every audit is a manual fire drill with real compliance exposure.
Headline: 8 words · Body: 20 words
Ad 2 — The Solution
Compliance documentation that stays current automatically.
DataHub traces every transformation from raw data through Databricks and dbt to your regulatory reports — always current, always auditable.
Headline: 5 words · Body: 19 words
Ad 3 — Why DataHub
Visa runs this. Your stack connects in hours.
Visa uses DataHub for continuous compliance monitoring across their data platform. Apollo's Databricks and Snowflake stack connects with no custom build. See how.
Headline: 8 words · Body: 22 words
Coca-Cola Tier 5 — Industry Fallback
No tech stack confirmedDISCOVER stageBeverages / CPG$10B+ revenue
Account Value Prop (internal — not ad copy)
At Coca-Cola's scale, data fragmentation compounds across brands, markets, and distribution networks. When merchandising, supply chain, and marketing teams are each working from separate data sources, metric disagreements slow decisions and erode trust in the analytics that drive market planning. DataHub unifies discovery, lineage, and governance so analysts across divisions can find certified data in seconds, platform teams can catch quality issues before they reach dashboards, and governance keeps pace with the data flowing across a global operation.
Ad 1 — The Problem
Every market. Every brand. A different version of the truth.
When supply chain, marketing, and merchandising pull from separate data sources, metric disagreements slow decisions at global scale.
Headline: 11 words (trim to: "Hundreds of brands. Dozens of data silos.") · Body: 18 words
Ad 2 — The Solution
Trusted data. Every team. Every market.
DataHub makes certified data discoverable across every division — with lineage that shows where each dataset came from and governance that keeps it accurate.
Headline: 6 words · Body: 22 words
Ad 3 — Why DataHub
Walmart uses DataHub. So does Apple.
Global enterprises with complex operations choose DataHub for unified data governance at scale. See what it looks like for a CPG operation as distributed as Coca-Cola.
Headline: 6 words · Body: 24 words (can trim: "Enterprises at global scale choose DataHub. See how it maps to your operation.")
Abbott Tier 1 — OSS User
AI ToolingDatabricksdbtAirflowSnowflakeOSS — DataHub CoreDEPLOY stageHealthcare / Medical125 developers
Account Value Prop (internal — not ad copy)
Abbott's data team has already shipped on DataHub Core — 125 developers working across Databricks, dbt, Airflow, and Snowflake means they've solved the hard problems of lineage and discovery at scale. What Cloud adds is the layer that makes DataHub viable for what comes next: freshness monitoring with automated assertions, incident SLA dashboards for a team of 125, compliance workflows tuned for healthcare regulatory requirements, and the MCP Server and Context Graph that let AI agents query trusted metadata at machine scale. For a healthcare organization building AI into diagnostics and device data pipelines, that infrastructure is what separates experimental AI from production-grade.
Ad 1 — The Problem
DataHub Core got you here. What's next is harder.
Freshness monitoring, incident SLAs, AI agent infrastructure — the things Core can't provide still have to be built from scratch by your team.
Headline: 9 words · Body: 20 words
Ad 2 — The Solution
Cloud adds what Core was never designed to do.
Automated freshness assertions, incident dashboards, healthcare compliance workflows, and MCP Server for AI agents — without rebuilding what Abbott already shipped.
Headline: 9 words · Body: 19 words
Ad 3 — Why DataHub
Abbott already knows DataHub works. Cloud is the upgrade.
No migration. No retraining. Just the operational layer your 125 developers need to take production AI seriously.
Headline: 9 words · Body: 16 words
Experian Tier 2 — AI Tooling
AI Tooling confirmedDatabricksdbtAirflowSnowflakeOSS TelemetryEVALUATE stageProfessional Services / Data Bureau48 developers
Account Value Prop (internal — not ad copy)
Experian's core product is data — which means data quality and governance aren't supporting functions, they're the product itself. With 48 developers actively building on Databricks, dbt, Airflow, and Snowflake, and AI tooling confirmed across the stack, Experian is at the point where the certification question becomes critical: which datasets have been validated enough to train credit models on, and how do you demonstrate that to regulators? DataHub makes AI readiness operationalizable — automated lineage from source through Databricks transformations to model inputs, continuous quality assertions, and governance workflows that let Experian prove data provenance without manual audit prep.
Ad 1 — The Problem
Which data is safe to train your models on?
When your product is data and regulators want proof, manual audit prep doesn't scale to 48 developers shipping AI on Databricks.
Headline: 9 words · Body: 19 words
Ad 2 — The Solution
Certify your AI training data. Automatically.
DataHub automates lineage from raw source through every Databricks transformation to the model input — with continuous quality assertions regulators can review.
Headline: 5 words · Body: 20 words
Ad 3 — Why DataHub
You're already evaluating. See what it connects.
Experian's stack — Databricks, dbt, Airflow, Snowflake — connects to DataHub out of the box. You're in the evaluation phase. Let's make it concrete.
Headline: 6 words · Body: 21 words
Bank of America Tier 5 — Industry Fallback
No tech stack confirmedDISCOVER stageFinancial Services / BankingSIFI — Systemically Important
Account Value Prop (internal — not ad copy)
For a systemically important financial institution like Bank of America, data lineage isn't a nice-to-have — it's a regulatory mandate. BCBS 239 requires risk data aggregation and reporting to be accurate, timely, and fully auditable, and point-in-time audit processes can't keep pace with the volume of data flowing across a bank of BofA's complexity. DataHub replaces manual compliance processes with continuous monitoring — lineage tracked automatically, compliance workflows that run in real time, and audit trails that are always current. For BofA's risk and data teams, that's the shift from managing compliance to maintaining it without the quarterly scramble.
Ad 1 — The Problem
BCBS 239 compliance isn't quarterly. Your process is.
One pipeline change can create a lineage gap between yesterday's data and tomorrow's regulatory report — and audits catch it too late.
Headline: 8 words · Body: 20 words
Ad 2 — The Solution
Continuous compliance monitoring. Not quarterly fire drills.
DataHub tracks lineage automatically, flags violations in real time, and maintains the audit trail BCBS 239 requires — every day, not just before reviews.
Headline: 6 words · Body: 22 words
Ad 3 — Why DataHub
Major financial institutions run this. See why.
Visa and capital markets firms use DataHub for continuous BCBS compliance monitoring. See how it maps to Bank of America's requirements. [Get a Demo]
Headline: 7 words · Body: 22 words
Notes for Creative Production
| Account | Signal Tier | Creative Angle | Priority CTA |
|---|
| Brooks Running | Tier 3 — Tech Stack | Stack-specific pain (dbt/Airflow/Snowflake) | See how it connects |
| Hulu | Tier 5 — Industry | Media/streaming metric consistency | See Netflix case study |
| Block | Expansion | AI agent infrastructure for existing users | See what Cloud adds |
| Apollo | Tier 3 — Tech Stack | Capital markets compliance lineage | See Visa case study |
| Coca-Cola | Tier 5 — Industry | CPG/global scale data trust | See enterprise case studies |
| Abbott | Tier 1 — OSS | OSS→Cloud upgrade, AI readiness | See Cloud vs Core |
| Experian | Tier 2 — AI Tooling | Training data certification at scale | Request a live eval demo |
| Bank of America | Tier 5 — Industry | Continuous BCBS 239 compliance | Get a Demo |
Generated for internal ABM use · FY27 Q1 · Wacarra Yeomans · Acryl Data