How to Manage Disparate Data: 5-Step Playbook to Eliminate Fragmented Sources

“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.

1. Inventory & Score Every Source

Create a quick spreadsheet with columns:

Prioritize by business impact + data freshness. If nobody uses that legacy CSV, archive it. Less noise = faster wins.

2. 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.

3. 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.

4. 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.

5. Self-Service & Feedback Loop

Expose curated views to the tools people already live in:

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

1. Inventory sources → kill noise

2. Centralize in one warehouse

3. Define metrics once, with owners

4. Automate tests to catch drift

5. 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.