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Integrations and data: scope, controls, and requirements

This guide explains where integrations and data may apply: the team copies and pastes the same information across spreadsheets, email, the CRM and the ERP. It covers inputs, human decisions, limits, and metrics worth agreeing.

Miguel Angel Diaz →

What gets built

  • An inventory of systems, permissions, and sources
  • API or export data flows
  • Deduplication and master-system rules
  • Migration validation and rollback plan

Guide controls and deliverables

  • A data map and named owners
  • Documented transformation rules
  • Validation controls before a workflow changes

Operational signs to review

  • The team copies and pastes the same information across spreadsheets, email, the CRM and the ERP
  • Two systems show different data and every team loses time deciding which one is correct
  • Reports arrive late because files must first be gathered, cleaned and reconciled by hand

How data integrations work

  1. Source inventory

    We list the tools and files where information lives (ERP, accounting, CRM, spreadsheets, WhatsApp) and which data each one holds.

  2. Connection

    Each source is connected through an API, a scheduled export or automatic reading of emails and files. No manual copying.

  3. Common model

    Data is translated into a common model (contact, order, invoice) with cleaning and deduplication rules.

  4. Output

    With documented sources, definitions, and permissions, reports, alerts, or queries can be configured within the agreed scope.

Workflow description

Before

The team copies and pastes the same information across spreadsheets, email, the CRM and the ERP

After

The data updates across tools workflow defines a master source, transformation rules, rejected records, and validation before each update.

Master system, transformation rules, rejected records, and applied validations.

What the company needs before starting

  • A list of the current tools and who administers them
  • Access or API credentials for each one
  • One key piece of data copied by hand today: that's the first flow
  • Agreement on which system wins when two disagree

Common integration mistakes

  • Integrating everything at once

    One data flow at a time, validated, before connecting the next.

  • Ignoring duplicates

    Without deduplication, integration multiplies the mess.

  • Not defining the master system

    If two tools hold the same data, one wins. Decide before connecting.

  • Migrating without validation

    Every migration is compared against the original source before switching the old one off.

What to review during operation

  • Hours of copy-paste in data updates across tools per month
  • Inconsistencies detected between systems
  • Delay between an event and its reflection in reports
  • Automatic versus manual reports

Terms worth knowing

API
A door through which one system lets another read or write its data automatically.
Master system
The tool that holds the official version of a piece of data when several store it.
Deduplication
Detecting and merging repeated records, such as the same contact written three ways.
Data pipeline
An automatic sequence that extracts, cleans and loads data from one source to another.

Frequently asked questions

Which tools can be integrated for a company?

Tools that offer an API, export, or authorized data-exchange method. Viability depends on permissions, technical limits, and information quality.

How is information protected during migration?

Backups, tests, validation against the original source, and a rollback plan are agreed before an earlier process is retired.

How is the data consulted afterwards?

A master source, reports, and queries can be defined according to the connected systems and each team’s permissions.

Which process is slowing your company down?

Describe the integrations and data workflow to review in Integrations and data: input, decision, system involved, and exception owner.

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