In the technology decision-making rooms of large corporations, a dangerous axiom persists: Until our data is perfectly clean and centralized, we cannot make the leap to Artificial Intelligence (AI). While this premise stems from logical prudence, it has become the primary inhibitor of real innovation.

From the perspective of a technology or business leader, the quest for data perfection often leads to massive infrastructure projects that consume years of budget before delivering the first ounce of value. Today’s market reality dictates a different rule: in the era of Generative AI and advanced integration architectures, “good enough” data today is infinitely more valuable than “perfect” data eighteen months from now.

The Trap of Eternal Cleaning: A Cost-Opportunity Analysis

The traditional concept of Data Cleaning involves a manual or semi-automatic process of correcting errors, removing duplicates and normalizing formats. In organizations with decades of legacy technology, this process is, by definition, endless.

When an AI project is subordinated to a prior total cleanup, the company incurs three critical risks:

1. Model Obsolescence: By the time the data is clean, market needs or AI technology itself have evolved, making the original use case lose relevance.
2. Investment Fatigue: Stakeholders lose confidence in AI when they do not see tangible results, perceiving the technology as a cost centre rather than a profit driver.
3. Analysis Paralysis: Structure is prioritized over results, forgetting that modern AI is capable of processing unstructured information (such as emails, PDFs or transcripts) with an efficiency that was unthinkable a decade ago.

The Minimum Viable Dataset (MVD) Framework

To break this cycle, we propose a pragmatic approach based on the Minimum Viable Dataset (MVD). This is not about working with poor quality data but about defining the quality threshold necessary for the AI to be functional, secure and profitable.

A robust MVD is built upon four pillars of pragmatic quality:

1. Operational Representativeness: We do not need the history of the last fifteen years. For most use cases, such as supply chain optimization or customer assistance, data from the last 6 to 12 months offers superior predictive accuracy, as it reflects the current market reality.
2. Contextualization over Structuring: Current AI does not need everything in a perfect Excel table. It needs context. A technical manual in PDF, although “messy” from a traditional data standpoint, is pure gold if the system can retrieve it through RAG (Retrieval-Augmented Generation) architectures. This technique allows the AI to consult external documents to provide precise and verifiable answers.
3. Integrity of Key Fields: Instead of cleaning 50 columns in a database, we identify the 5 that truly move the needle for the business. If those five fields are consistent, the rest can be treated as secondary information or manageable noise.
4. API Accessibility: Data is good if it is actionable. The priority should be creating connectors that allow the AI to read information recurrently, ensuring that the model does not work with a fixed snapshot of the past, but with the company’s living flow.

Practical Implementation: Execution Without Endless Projects

How do we land this in an organization? The process does not start at the database but at the business objective.

• Step 1: Case-by-Case Segmentation. Instead of a transversal cleanup, we perform a vertical one. If the goal is to improve technical service efficiency, we only sanitize data related to incidents and product manuals, ignoring (for now) marketing or human resources data.
• Step 2: Automated Curation. We use AI itself to clean the data. There are models specifically designed to identify anomalies, normalize dates or detect duplicates at a speed that no human team could match. AI becomes the cleaning tool, not just the final product.
• Step 3: Domain Expert Validation. Instead of external data audits, we use a “oop validation. Company experts review a sample of the AI’s results. Their corrections are fed back into the system, improving data quality organically while the project is already underway.

AI as a Catalyst for Data Quality

One of the most interesting collateral benefits of this approach is that an AI project often acts as a mirror for the organization. Upon seeing the first results, data inconsistencies become evident and, more importantly, they become expensive.

When a manager sees that AI could save 20% of operational time if certain records were better captured, data governance stops being a tedious IT task and becomes a strategic business priority.

Leadership Through Technological Pragmatism

Success in AI adoption does not belong to those with the best servers or the cleanest data but to those who know how to identify the minimum information necessary to generate maximum impact.

At Intech Heritage, our methodology focuses on shortening the distance between data and results. We design architectures that leverage your current information assets, transforming good enough into a differential competitive advantage while others continue to wait for data to be perfect.

The question for leaders today is not “when will our data be ready?”, but “what value are we failing to capture by waiting for it to be?”.