Data-centric: improve the performance of AI and machine learning models

Would you trust the critical decisions of your company to the data you have or would you invest in data-centric? In a world where the global AI market has already reached about $391 billion and grows at a compound rate of almost 36% per year, it is no longer enough to rely solely on sophisticated algorithms. McKinsey’s research reveals that 78% of organizations already use AI in at least one function, but only 1% can scale effectively.

What is the main barrier? Fragmented, poorly structured, or poorly governed data blocks real transformation. The data-centric approach represents the answer to this challenge. 

Prioritizing data quality, consistency, and governance is the path to reliably scaling AI models and generating sustainable value. In this article, we will show how to elevate your data journey and unlock the full potential of AI in your company.

How to apply a data-centric approach to improve the performance of AI and machine learning models?

Traditionally, the development of artificial intelligence (AI) and machine learning (ML) models has been guided by a model-centric logic, focusing on the choice and tuning of algorithms to extract maximum performance from increasingly complex architectures. However, this approach has clear limits, especially when the database does not keep up with the sophistication of the model.

It is in this context that the data-centric approach becomes relevant. It proposes a shift in focus: instead of trying to compensate for noisy, unbalanced, or poorly labeled data with more robust models, the goal becomes to systematically improve the quality of the data used for training.

Applying a data-centric mindset involves key practices that place data at the center of the development process:

Continuous data curation and cleaning

Removing inconsistencies, duplications, and outliers that compromise the model’s ability to learn relevant patterns is essential. Data cleaning is a prerequisite for any attempt at performance optimization.

Accurate and standardized labeling

Supervised models are highly sensitive to label quality. Human errors or ambiguous annotation criteria reduce accuracy, especially in critical domains such as health, finance, or natural language interpretation. Standardization and cross-review help mitigate this impact.

Dataset augmentation and balancing

Imbalances between classes or the lack of representativeness in subsets of data create bias in the models. Techniques such as data augmentation, oversampling, and synthetic data generation help correct distortions and strengthen the model’s capacity for generalization.

Continuous review based on model failures

A data-centric approach assumes iterative cycles of improvement. Analyzing the errors made by the model in validation or production environments allows for the identification of problematic patterns and refining the dataset more effectively than adjustments to the algorithm itself.

More than a trend, data-centric AI represents an evolution in how data science teams conduct projects. Placing data quality at the center of technical decisions results in more consistent gains, reduced bias, and accelerated delivery times for models with real business impact.

How to structure teams and responsibilities around a data-centric architecture?

Adopting a data-centric architecture is more than a technical adjustment: it is a strategic decision. As data becomes a central asset in generating value, companies must ensure that their organizational structures are prepared to guarantee the integrity, reliability, and data governance on a large scale.

Executives leading this transformation reap concrete gains, such as greater analytical agility, reduced rework in AI projects, and improved regulatory compliance.

Establish a data governance with organizational vision

Data Governance teams should operate transversely, defining quality, standardization, and data access policies. This structure reduces silos and aligns all domains to a common information management model.

Direct benefit: companies with mature data governance policies save on rework in data science projects. 

Appoint data owners by domain (Data Stewards and Domain Owners)

Each functional area should be responsible for its own data. Stewards ensure the accuracy and timeliness of information and act as the link between technology and business.

Maturity indicator: each domain should have clear quality KPIs, such as fill rate, consistency, and compliance.

Strengthen data engineering with a focus on reliability and observability

Pipelines need to be versioned, monitored, and auditable. Engineering teams should build resilient flows with automated testing and anomaly alerts in production.

Impact: companies with observable pipelines reduce mean time to resolution for data incidents (MTTR) by up to 40%.

Reposition data scientists as accelerators of the business

With clean and well-structured data, data scientists stop acting as “data curators” and begin to focus on generating value: testing hypotheses, developing models, and delivering actionable insights faster.

Expand the role of Product Owners with a data-driven vision

Product leaders should incorporate quality and governance criteria into backlog prioritization. Each new functionality must consider from the start the impact on data collection, traceability, and reusability.

Implement a culture of continuous feedback based on production data

Models in production should generate inputs that feed back into the data teams. Classification errors, ingestion delays, and performance drops are signs that input data needs to be reviewed.

Data-centric or model-centric?

AI and machine learning projects have historically followed a model-centric path: focusing on choosing the best algorithm, tuning hyperparameters, and increasing architecture complexity to extract performance. For a long time, this was sufficient, especially when the data was already clean, structured, and in a controlled volume.

Today, this scenario has changed.

With the popularization of generative AI, the exponential growth of unstructured data, and the use of increasingly diverse sources, the main limitation is no longer the model. Now, the quality of the data defines the success or failure of an AI initiative.

What defines each approach

In companies with a low level of data maturity, insisting on model adjustments usually results in marginal and temporary gains. On the other hand, investing in data structuring can yield substantial performance leaps, reducing biases and increasing the model’s generalization.

What the market has already realized

Companies that have migrated to a data-centric mindset report gains such as:

• Reduction of time for model development (up to 50% less data preparation time)
• Diminution of rework caused by inconsistent data
• Continuous improvement of accuracy based on real-time feedback cycles

Recent studies from Stanford HAI reinforce this movement: incremental improvements in data have more impact than changes in the model, especially when datasets are limited, noisy, or poorly labeled.

How to decide which approach to prioritize?

• If your data is reliable, but the model results are still weak, it is worth optimizing the architecture.
• If there are constant complaints about inconsistent, biased, or incomplete data, the problem is not with the model.
• If your company wants to scale models across multiple domains, the data-centric approach is the only sustainable path.

The choice between data-centric and model-centric is not exclusive but rather a matter of focus. In complex corporate environments, with multiple sources and strong regulation, prioritizing data is the most efficient way to build more robust, auditable models that align with the business.

What are the impacts of a data-centric strategy on the scalability of AI and value generation for the business?

Adopting a data-centric strategy is not just a technical decision, but a lever for scale and value. As AI projects move out of the lab and into real operational environments, the quality and governance of data become critical factors in maintaining the performance of models and sustaining gains over the long term.

Technical scalability with less friction

With standardized, consistently labeled, and end-to-end monitored data, models can be replicated, adapted, or expanded to new domains with less effort. This reduces the delivery time for new models and allows the creation of reusable repositories of trainable data, accelerating future development.

Impact: companies with robust data pipelines scale AI solutions up to 3x faster than those with fragmented datasets (Source: McKinsey).

Reduction in operational costs

Poorly fed AI projects lead to rework, manual adjustments, and low reliability. A data-centric architecture reduces input errors, improves model stability, and decreases the number of iterations necessary to achieve optimal performance.

Example: a fintech that implemented automated quality controls in the data labeling process reduced rework costs in fraud detection models by 40%.

Greater adherence to compliance and security

Traceable, governed, and versioned data are easier to audit — an essential condition in sectors such as finance, health, and energy. Additionally, a data-centric approach facilitates access control, anonymization, and ethical data use, reducing regulatory risks.

Enhanced generalization and adaptability of models

Models trained with well-balanced and representative data adapt better to new scenarios, segments, and geographies. This is essential for companies looking to expand operations, offer personalization at scale, or operate with AI across multiple channels and journeys.

Continuous value generation with active learning

A data-centric strategy does not end with deployment. Data collected in production feeds continuous improvement cycles, allowing for gap identification and generation of new insights not only for the model but for strategic decisions in product, marketing, and operations.

How does Objective support companies in building data pipelines for a data-centric journey?

Building reliable, scalable, and quality-centered data pipelines is one of the main challenges for companies looking to extract real value from artificial intelligence. Objective supports organizations on this journey with a focus on technical pragmatism, integration with internal teams, and delivering continuous value.

Business-oriented diagnostics

Before the first line of code, Objective conducts a complete mapping of data flows, identifying bottlenecks, redundancies, and critical sources for the business. This diagnostic guides the architecture of pipelines prioritizing governance, traceability, and quality from the start.

Building pipelines with observability and resilience

The pipelines implemented are designed for production environments: with versioning, automated testing, structured logs, and configurable alerts. This ensures that failures are detected quickly and avoids compromised data from feeding models or strategic decisions.

Integration with AI and analytics platforms

The structuring of data does not end at ingestion. Objective connects pipelines to analytical solutions and machine learning models, ensuring that data flows consistently and is ready for consumption in BI, APIs, and generative AI applications.

Knowledge transfer and sustainable practices

The delivery goes beyond code. The team works alongside internal teams, structuring reusable standards, documenting processes, and training company professionals to maintain and evolve pipelines autonomously.

If your company is looking for greater efficiency, governance, and scalability in data and artificial intelligence projects, our specialists can help. And contact Objective right now.