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Glossary

What is data integration? Explained

A plain definition of data integration, how ETL and ELT differ, the steps involved, what the cloud changes and why AI projects depend on it.

Updated

Definition

Data integration

Data integration is the practice of combining data from different sources into one consistent view that people and software can trust. It is the answer to the question what is data integration for any growing business.

In short: Many sources, one consistent view.

What is data integration in plain words

Without integration

  • Data sits in separate tools
  • Reports disagree
  • Someone copies numbers by hand

With integration

  • Sources feed one view
  • Reports match
  • Updates move on their own

Most companies keep data in many places: a CRM, accounting software, email, spreadsheets and a website. Each holds part of the story.

The definition of data integration is simple: pull those parts together so they match and can be used as one.

To define data integration in a sentence, think of it as turning scattered records into a single picture.

Data integration meaning, for an owner, comes down to one result: the same customer, order or invoice looks the same in every report.

The data integration process step by step

  1. 01

    Find

    List the sources and the data you need.

  2. 02

    Extract

    Pull the data out of each source.

  3. 03

    Clean

    Match formats, names and duplicates.

  4. 04

    Load

    Put it in one shared place.

  5. 05

    Check

    Test it and keep it updated.

The data integration process follows a clear order, whatever tools sit behind it. Data integration processes differ in detail, but the main stages repeat.

IBM notes that ELT and ETL are both data integration processes that move data from a source system to a target system. The key difference is the order of the steps.

Data integration and ETL are closely linked. In ETL, data is transformed before it lands in the target.

In ELT, it is loaded first and transformed there. ELT is not always the better pick, and ETL still helps when legacy systems have limited transformation abilities.

Cloud data integration and automated pipelines

  • Cloud based

    Runs online, no servers to maintain.

  • Scheduled

    Data moves at set times.

  • Triggered

    A change in one tool starts the flow.

  • Monitored

    Failures get flagged, not buried.

Cloud based data integration runs in the provider's environment instead of on your own servers. Data integration in the cloud connects online tools such as CRM and accounting without hardware to maintain.

Data integration cloud computing suits companies whose tools already live online. A cloud based data integration service handles the connections and keeps them running.

Once the flow is built, you can automate data pipeline runs so data moves on a schedule or when something changes.

Manual exports and copy-paste then drop out of the routine. Our API integration entry explains the connections underneath.

Why data integration comes before AI

Ready for AI

  • Sources are connected
  • Fields match across tools
  • Someone owns data quality

Not ready yet

  • Data lives in silos
  • Duplicates are common
  • Updates depend on manual exports

AI needs connected, consistent data to work well. The US Census Bureau found that 37% of firms with at least 250 employees reported using AI, so adoption is real.

Results lag, though. An MIT study reported that most pilots fail because of brittle workflows and poor fit with day-to-day operations. Disconnected data makes that worse.

Good data integration solutions give an AI system the same facts your team sees. See how this fits with AI integration, system integration and data governance.

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI integration. They named 96 companies, and 82% appeared in only one of the three.

CriterionChatGPTGeminiPerplexity
Companies named373746
Sites cited mostibm.com and accenture.com (5 of 10)rtslabs.com and iternal.ai (2 of 10)esparkinfo.com (4 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 7 companies appeared in all three. Google showed an AI Overview for 9 of the 10 questions.

FAQ

Data integration questions

  • The purpose is to give a business one reliable view of its data. Teams stop reconciling numbers by hand, reports agree, and automation and AI tools get the clean inputs they need.

  • Both move data from a source to a target. ETL transforms the data before loading it, while ELT loads first and transforms inside the target. IBM calls the order of steps the key difference.

  • Common methods include copying data into a central store, linking systems through APIs, and syncing tools on a schedule. Our system integration entry compares the main approaches.

  • Automating a pipeline means data moves from source to destination on its own, on a schedule or when something changes. Nobody exports files or retypes records, and failures raise an alert.

  • No. Integration connects and combines the data, while data governance sets the rules, owners and quality standards for it. Good results usually need both.

Related

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