AI projects get the headlines, but most of them succeed or fail on something far less glamorous: whether the data feeding them is complete, accurate, current and allowed to be used. Building and maintaining that foundation is the job of the data engineer. When companies race to adopt AI and discover that the real obstacle is the data, they are discovering why this role matters.
Gartner put the risk plainly in February 2025, predicting that through 2026 organizations will abandon 60% of AI projects that are not supported by AI-ready data. The same research found that 63% of organizations either did not have, or were unsure whether they had, the right data management practices for AI. This article explains what “AI-ready” means in practice and how data engineers get you there.
What data engineers actually build
A data engineer designs and runs the systems that move data from where it is created to where it is used. In a typical business that means pulling records from a CRM, an ERP, e-commerce platforms, support tools and spreadsheets, then cleaning, combining and storing them so analysts, applications and AI models can rely on them.
The building blocks are consistent across platforms:
- Ingestion and pipelines, batch or real-time, built with services such as Azure Data Factory, AWS Glue or Google Cloud Dataflow, or open-source tools such as Apache Spark and Kafka.
- Storage in a data warehouse or lakehouse, such as Snowflake, Databricks, BigQuery or Microsoft Fabric, organized so data can be queried efficiently.
- Transformation, usually in SQL and Python, that turns raw records into consistent, well-defined tables.
- Orchestration with tools such as Apache Airflow, so pipelines run in the right order, retry on failure and alert someone when they break.
None of these tools is the point on its own. The point is data that people and systems can trust without checking it by hand. That is why the best data engineers think beyond pipelines. They ask what decisions and products the data will feed, which failures would be most costly, and which parts of the business need to agree on a definition before any code is written. Tool skills in SQL, Python, Spark, Airflow and Databricks get the work done; that business understanding decides whether it was the right work.
What “AI-ready data” means in practice
AI raises the bar compared with traditional reporting. A dashboard with a stale number misleads a person who can often spot the error. An AI model or assistant that consumes bad data will produce confident, wrong answers at scale. AI-ready data generally has five qualities:
It is fit for the specific use case. Data that is fine for monthly revenue reporting may be useless for predicting individual customer churn. Readiness is judged against what a particular model or application needs, not in the abstract.
It is accurate and consistent. The same customer should not appear under five spellings. Definitions such as “active customer” should mean the same thing in every system.
It is current enough. A support assistant answering from last year’s policy documents is worse than no assistant. Freshness requirements should be set per source.
It is traceable. Data lineage records where each field came from and how it was transformed, so when an output looks wrong, someone can find out why.
It is governed. Access controls, retention rules and consent obligations travel with the data, so an AI system cannot expose information that its users should not see.
Unstructured data is the new frontier
Traditional data engineering focused on structured tables. Generative AI changed that. Retrieval-augmented generation, the pattern where an AI assistant looks up relevant documents before answering, depends on contracts, policies, emails, tickets and manuals being collected, cleaned, split into sensible chunks, tagged with metadata and indexed for search.
That is data engineering work, and it brings familiar problems in a new form: duplicate and outdated documents, missing permissions, and no clear owner for each source. Our post on retrieval-augmented generation explains the pattern; the quality of the underlying document pipeline decides whether it works.
Security and governance belong in the pipeline
Data pipelines concentrate an organization’s most valuable information in one place, which makes them attractive targets and a common source of accidental exposure. Data engineers reduce that risk by building protection into the pipeline rather than adding it afterward: encrypting data in transit and at rest, using service identities with narrow permissions instead of shared credentials, masking or removing sensitive fields that downstream users do not need, and logging who accessed what.
For regulated businesses, lineage and access logs are also compliance evidence. Being able to show exactly which data fed a model, and who could see it, is increasingly what auditors and regulators ask for.
An AI-readiness checklist for your data
Before funding an AI initiative, work through these steps with your data team or partner:
- Name the use case and its data. List every source the AI system will need and who owns each one.
- Profile the data. Measure completeness, duplicates, freshness and obvious errors before building anything.
- Agree on key definitions. Customer, order, active account and similar terms should have one documented meaning.
- Automate the pipeline with tests. Build data quality checks that stop bad data from flowing downstream and alert an owner.
- Record lineage and permissions. Know where each field came from and who is allowed to see it, and make the AI system respect those permissions.
- Plan for ongoing ownership. Pipelines break when source systems change. Budget for maintenance, not just the initial build.
For the engineering skills behind this, see our post on why data flow development separates good engineers from great ones.
Frequently asked questions
What is the difference between a data engineer and a data scientist?
Data engineers build and maintain the systems that deliver reliable data. Data scientists analyze that data and build models from it. A data scientist without good data engineering spends most of their time cleaning data instead of answering questions.
Do small businesses need a data engineer?
Not always full time. Many small businesses need a well-designed initial setup, such as a cloud warehouse with automated pipelines from their main systems, and then periodic maintenance. That can be delivered by a consultant or a part-time specialist.
Can AI tools clean our data for us?
They can help, for example by suggesting matches between duplicate records or classifying documents. But they still need rules, review and a pipeline to apply the results consistently. AI assists data engineering; it does not replace it.
Build the data foundation AI needs
Delana Technologies designs data pipelines, warehouses and governance that make analytics and AI dependable. Explore our data analytics and modern data platform services and AI consulting services, call 239.414.5126 or contact us.
Sources: Gartner press release, “Lack of AI-Ready Data Puts AI Projects at Risk” (February 26, 2025); Azure Data Factory, AWS Glue, Google Cloud Dataflow and Apache Airflow documentation.