Most Companies Are Sitting on a Data Graveyard (Here’s How to Fix It in Weeks—Not Quarters)

Saw this Reddit post the other day:

“Does your company have like a 1000 data silos? How did you deal??”

It hit hard.

Every team has their own database, Kafka topic, Notion doc, or Google Sheet.

Everyone’s “kind of aligned,” but no one agrees.

Endless meetings about metrics.

Everyone nods. Nothing changes.

Feels familiar? You’re not alone.

This isn’t a tooling problem.

It’s a people–process–trust problem.

And unless you fix that, your next AI initiative will fail before the pilot ends.

Let’s fix that—fast, cheap, and with no 6-month data mesh migration.

How to Kill Silos and Enable AI in 30–90 Days

We’ve helped teams in exactly this mess—from messy SaaS CRMs to ad hoc Notion “warehouses.”

Here’s the exact playbook we run to restore trust, cut costs, and make the org AI-ready.

1. Run Micro Data Workshops

Start small. One department. 2–3 power users.

By the end of the call, you must have:

• Clear KPIs, goals, blockers

• Draft metric definitions

• A list of vanity metrics to push back on

Ask:

“Does this tie to revenue, cost, or risk?”

If not—it’s a nice-to-have. Kill it.

2. Document the Trust Trigger

For every KPI, ask:

“What has to be true for you to trust this number?”

Even if you define the metric together, most teams won’t trust it.

Why? They’re comparing it to a different source.

Fix the mental model, not just the SQL.

3. Assign Ownership

Every critical metric needs a named owner.

Put their name right in Looker or the Google Sheet.

This creates:

• Faster debugging

• Less finger-pointing

• Cleaner escalation paths

4. Kill Redundant Pipelines

Most orgs have 3–4 data flows per metric.

Example: revenue from Stripe, HubSpot, legacy Excel CSV from a VP who quit in 2022.

Kill the noise.

Pick one validated path, route everything through it.

Your CFO will thank you.

5. Stand Up the Lean Stack

You don’t need 9 tools and a 6-month RFP.

This stack gets 90% of companies AI-ready:

• Ingest → Fivetran / Airbyte / custom py scripts

• Warehouse → BigQuery / Snowflake

• Transform → dbt

• Data Marts → BigQuery views / Google Sheets

• Self-Service → Sheets / Looker

We’ve seen teams running LLM copilots on this stack—plus Sheets.

6. Enable Team-Specific Marts

Give each department what they need:

• Ops → pre-modeled views in BigQuery

• Finance → spreadsheet with validated LTV calc

• Marketing → campaign dashboard in Looker

No more Slack pings like “Can I get updated data?”

Self-serve, always-on, low-latency access.

7. Weekly Feedback Calls & DQ Checks

Every Friday, ask:

• Are you using this data?

• Anything feel off?

• What decision did this help you make?

Add lightweight data quality checks to alert on:

• Schema changes

• Row drops

• Volume shifts

Catch silent errors before someone sends the wrong forecast.

8. Monthly / Quarterly Roadmap Review

Use a Value × Complexity matrix to evaluate next steps.

• Keep: high-value, low-effort wins

• Kill: nice-to-haves with no impact

• Plan: 1–3 new initiatives tied to ROI

Bonus: Why This Actually Works

You’re not “fixing the stack.”

You’re restoring trust, cleaning up ownership, and creating space for AI.

When this is in place:

• Teams move without asking for permission

• Execs stop searching for miracle platforms

• Your org becomes ready for LLM copilots, automation, forecasting

• AI doesn’t break because the data layer doesn’t lie

TL;DR

If you’re stuck in data silo hell:

1. Stop chasing tools. Start fixing trust.

2. Kill pipelines you don’t need.

3. Assign ownership and document what “good” means.

4. Get teams their data in the tool they already use.

5. Run short feedback loops and iterate weekly.

Silos die. Trust builds. AI becomes usable.

Want Help Doing This in 30–60 Days?

We help teams go from “data chaos” → “AI-ready” without months of meetings or massive migrations.

👉 Book a 30-minute roadmap session

We’ll review your stack, backlog, and blockers—and outline a lean plan to cut silos, restore trust, and launch AI faster.

FAQ

What is a data silo?

A data silo is isolated data that’s inaccessible or inconsistent across departments, making collaboration and accurate reporting difficult.

Why do data silos block AI adoption?

Because AI tools rely on centralized, trustworthy data. If different teams use different definitions, the AI becomes unreliable.

How do I fix data silos quickly?

Start with workshops, align on definitions, assign owners, and build one clean path to the truth. Add trust checks before scaling AI.