Insight
7/4/25
How to Manage Disparate Data: 5-Step Playbook to Eliminate Fragmented Sources
Disparate data kills trust and slows AI. Learn a proven 5-step framework to centralize, clean, and control fragmented data sources fast.
“We’ve got data everywhere—Excel in sales, HubSpot in marketing, Stripe in finance. None of it matches.”
— Every ops leader, ever
Welcome to the club. Most mid-market companies can’t agree on one metric because their data lives in a dozen uncoordinated tools. The result?
Forecasts no one trusts
AI projects stalled at ‘proof-of-concept’
Endless meetings about whose number is “right”
Let’s end that. Below is the exact workflow we run at Aztela to tame disparate data, rebuild trust, and get you AI-ready in weeks not quarters.
Inventory & Score Every Source
Create a quick spreadsheet with columns:
Source | Owner | Use-case | Trust (1–5) | Last Updated | Kill/ Keep? |
|---|
Prioritize by business impact + data freshness. If nobody uses that legacy CSV, archive it. Less noise = faster wins.
Route Everything to One Landing Zone
Pick a modern, boring destination: BigQuery, Snowflake, Databricks—whatever your team can actually manage.
Ingest via Fivetran/Airbyte for SaaS apps
Use Python scripts or ELT jobs for edge cases
Load raw tables first; no premature transformations
Goal: all rows in one warehouse within two weeks.
Define “Source of Truth” Metrics Once
Workshop with 2–3 power users per department. For each KPI write:
SQL / dbt logic
Owner
Update frequency
“Trust trigger” (what must be true to believe the number)
Publish definitions in Confluence or a data catalog; link straight from Looker dashboards.
Model & Test in dbt (or Your Favorite Tool)
Transform raw → clean staging → business marts.
Add dbt tests for:
Not-null & unique IDs
Row count anomalies
Freshness SLA
Failures trigger Slack alerts so surprises die early.
Self-Service & Feedback Loop
Expose curated views to the tools people already live in:
Department | Delivery Tool |
|---|---|
Sales | HubSpot widget / Looker tile |
Marketing | Google Sheets auto-refresh |
Finance | Tableau or PowerBI |
Run a 30-minute “data feedback” call every Friday:
“Did the metric help you decide something? What felt off?”
Iterate weekly; ship fixes in the next sprint.
TL;DR
Inventory sources → kill noise
Centralize in one warehouse
Define metrics once, with owners
Automate tests to catch drift
Deliver self-service + weekly feedback
Do this and “disparate data” goes from blocker to competitive edge. Your dashboards align, your AI prototypes stop hallucinating, and your execs finally believe the numbers.
Want This Done in 60 Days?
We’ve implemented this playbook at mid-market and entrepise firms—cutting report prep time by 70 % and unlocking AI pilots that actually ship.
👉 Book a free 30-minute data alignment session—walk away with a mini-roadmap, no strings attached.
