How to Make Your Data GenAI-Ready (Without Rebuilding Everything)

Most GenAI projects fail before they even start.

Not because the model was wrong. Not because RAG didn’t work. But because the data wasn’t ready.

Over the last year, we’ve worked with 20+ companies trying to launch GenAI pilots. 9 out of 10 weren’t even ready to start.

They all thought the problem was the AI. It wasn’t.

The real issue? Nobody had usable data. Definitions were a mess. Tools didn’t talk. Metrics contradicted each other.

Before you burn another sprint trying to build a co-pilot, here’s how to actually get your data GenAI-ready — without blowing up your stack.

1. Start with Stakeholders, Not SQL

The problem isn’t your pipelines — it’s the people.

Start with 5–7 short interviews with decision-makers in Sales, Ops, Finance, Support:

• What decisions are you trying to make weekly?

• What KPIs don’t you trust today?

• If your data worked perfectly, what would change?

Document:

• Metric name

• Business definition

• Technical logic

• Frequency of need

• Action it supports

If a metric doesn’t drive a decision, don’t track it. This step alone filters out 40% of useless requests.

2. Audit the Chaos — Then Ignore Most of It

Every company thinks their data is uniquely messy.

It’s not.

You probably have:

• 10+ apps (CRMs, ERPs, spreadsheets)

• Inconsistent IDs, missing timestamps

• Conflicting KPIs across departments

Don’t boil the ocean.

Just ask:

• What’s the highest signal data we trust today?

• Where are the silos blocking decisions?

• What are the critical gaps we must fix to get value?

Fix only what blocks the project. Ignore the rest until value is proven.

3. Pick a Stack You Can Actually Use

You don’t need a trendy stack. You need one your team can operate.

Minimum requirements:

• Real-time + batch ingestion

• Support for structured + unstructured data

• Fast querying

• Lineage + access control

Most clients land on:

• BigQuery or Snowflake → scale + flexibility

• Databricks → great if you’re ML-heavy

• Azure Synapse / AWS Redshift → if you’re already there

Don’t delay on stack decisions. Just choose something reliable and move.

4. Build the Simplest Usable Data Model

You don’t need perfect models.

You need usable tables with clear logic.

Start with simple naming layers:

• raw_ → untransformed source

• stg_ → cleaned, deduped

• dim_ / fct_ → dimensions + fact tables

• rpt_ → final business logic for metrics

Examples:

• rpt_churn_risk_score

• rpt_mrr_forecast

• rpt_support_volume_trend

Keep it lean. Avoid repeating logic across tools.

5. Ingest Smart — Not Everything

You don’t need “all the data.” You need signal.

We always start with top 5 sources that:

• Feed core workflows (support, billing, product)

• Are relatively clean or easy to fix

• Drive urgent metrics

Use:

• Fivetran, Portable, Stitch → fast ingestion

• dbt → transform + test

• Airbyte, ADF, Matillion → orchestration

• Custom scripts for weird/legacy cases

6. Add Trust Layers (Docs > Dashboards)

If people don’t trust the data, they won’t use the AI.

Add context:

• Business glossary embedded into dashboards

• Lineage maps (can be done in dbt or manually)

• Sample data exports in Sheets for review

Build a culture of feedback, not handoffs.

Let end-users review data, spot gaps, and propose improvements.

Transparency = trust.

7. Govern Like You’re Shipping a Product

Treat data like software. Ship small. Get feedback. Improve.

Set up:

• 1 owner for the project

• Biweekly user check-ins

• Slack/Teams channel for async Q&A

• Metrics for freshness, error rates, usage

Use this to gradually grow trust — and your use cases.

Final Thought

You don’t need perfect data to build GenAI.

You need:

• Clear definitions

• Usable sources

• Stakeholder alignment

• A simple, working model

Start small. Prove value. Iterate.

That’s how you become the 10% of companies actually shipping GenAI — while others are still fixing CSVs.

Want a free GenAI Data Readiness Audit?

Book a 30‑minute Data / AI Audit. 

We will provide you roadmap to get value from your genAI and Data initiatives fast and push to production.

No gimmicks just experience and aligning to business objectives.

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