The problem nobody wants to admit: most companies’ data isn’t ready for AI
Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren’t supported by AI-ready data, and 63% of organizations are unsure whether they manage their data the way AI requires (Gartner). The most common cause isn’t choosing the wrong algorithm or lacking computing power — it’s poor-quality input data.
For companies considering AI initiatives — or that have already invested without getting the results they expected — data quality is the mandatory starting point. Without reliable data, AI models learn the wrong patterns, and their predictions are at best unhelpful and at worst actively harmful to decision-making.
What “poor data quality” actually means
Data quality problems take many forms, and each one affects AI models differently:
Incomplete data: records with empty or null fields in critical variables. A demand forecasting model trained on history missing sales data for entire seasons will learn the wrong patterns.
Duplicate data: the same customer recorded under different spellings of their name, the same order logged twice because of a system error. Duplicates artificially inflate metrics and distort the patterns the model is trying to learn.
Inconsistent data: the same quantity expressed in different units across sources (kilograms and metric tons, euros and thousands of euros), or categories renamed over time without normalizing the history.
Outdated data: records that were correct when captured but no longer reflect reality (customers with old addresses, discontinued products never updated).
Unverified outliers: values that may be data-entry errors or genuine exceptional events. Without a validation process, there’s no way to tell them apart.
The data quality improvement process
At AIRES, we follow a structured four-phase process to bring company data up to the reliability AI projects require:
Phase 1 — Quality audit: a thorough analysis of every data source relevant to the project. We measure quality indicators: completeness, uniqueness, consistency, validity, and timeliness. The result is a diagnosis that ranks the problems found by severity.
Phase 2 — Improvement plan: not every quality problem has the same impact on AI models. We prioritize improvements by their expected effect on project results, separating what can be fixed technically from what requires changes to how data is captured.
Phase 3 — Cleansing pipelines: automated scripts and processes that turn raw data into validated, documented datasets. These transformations are reproducible and auditable: every change is logged.
Phase 4 — Ongoing monitoring: data quality isn’t a state you reach once. New data coming in every day can introduce new problems. We set up alerts and dashboards that catch quality degradation before it affects models in production.
Why data quality is an investment, not a cost
Companies that invest in data quality before building AI models get results much faster and need fewer corrective iterations. Fixing data problems after the model is in production — and has been making wrong decisions for weeks or months — costs significantly more than solving them before training.
The data quality infrastructure built for one AI project also benefits all of the company’s analytics: the same clean, validated data improves reports, dashboards, and any other analysis that relies on those sources.
Frequently asked questions
How long does a data quality improvement process take?
It depends on the volume of data and the severity of the problems found. We define the scope after an initial audit that sets priorities and the action plan.
Can we improve data quality without changing our current systems?
In many cases, yes. You can build transformation layers that fix problems downstream without modifying the source systems. However, if the quality problems originate in how data is captured, the source systems will also need attention for the improvement to last.
What level of data quality do you need to start an AI project?
There’s no universal threshold; it depends on the type of model and how it will be used. What is true is that the more complete and consistent your historical data, the better the training results. In the initial audit, we can assess whether your existing data is enough to get started or whether it needs a period of improvement first.