Keeping your data clean is crucial for businesses aiming to modernize and innovate.
Data cleaning; fixing errors, filling in missing information, and organizing raw data, is the foundation for AI models, analytics, and reliable decision-making.
In this article, our data scientist Almudena Alvarez Bullain shares her insights on why data cleaning matters, the most common challenges companies face, and practical tips to build a stronger, more resilient data foundation.
By mastering these basics, businesses can make smarter decisions, stay competitive, and truly unlock the potential of digital transformation.
Almudena emphasizes:
Data cleaning is essential to transform raw, unstructured data into a usable format. It involves removing duplicates, handling missing values, and structuring the data for analysis.
Even the most advanced AI solutions can’t perform if the underlying data is unreliable. Almudena Alvarez Bullain highlights four major issues that frequently derail data projects:
Missing values in critical fields are surprisingly common, especially when data entry depends on human input.
“Human errors (like forgetting to click a button or skipping a field) can leave important gaps in the story your data is supposed to tell,” Almudena explains.
Without complete information, AI models struggle to find patterns or make accurate predictions.
Sometimes, data looks correct at first glance — but under the surface, it’s in the wrong format.
“For example, you might see the number ‘12′, but it’s actually stored as a text string (’twelve’) instead of a number,” Almudena says.
These subtle mistakes can break data pipelines or cause models to misinterpret information.
Duplicate records are another silent killer of data quality.
Especially in environments where the same type of data is entered daily (such as sales transactions or support tickets), accidental repetition leads to skewed analyses and poor AI performance.
In Natural Language Processing (NLP) tasks, even small typos or different ways of writing the same thing can cause huge inconsistencies.
Almudena recalls:
“When classifying issues manually, small differences (like typos in category names) meant the same type of problem was recorded under multiple different labels.”
Such inconsistencies make it difficult for AI models to group, classify, or predict correctly without extra cleaning work.
When category names vary due to typos or inconsistent phrasing, the model treats them as entirely different entities, fragmenting the dataset and weakening its predictive power.
Almudena explains that without thorough normalization, aligning different forms of the same data, AI algorithms can misinterpret patterns, miss important connections, or even learn the wrong relationships.
It’s not just about fixing mistakes,” she notes. “It’s about creating consistency so the model can actually ‘understand’ what it’s analyzing.
Careful attention to these subtle inconsistencies early on saves enormous amounts of time and prevents costly errors later in the project lifecycle.
Data quality problems can, in the worst case, completely derail AI projects. Sometimes in subtle ways that only appear late in the process. Almudena shared a real-world example where a small oversight caused major disruption.
While working on a sales forecasting project, her team faced an unexpected error during model training. After a long search, they discovered the problem: two missing dates in the dataset.
Instead of recording “zero sales” on days when stores were closed (as the system usually did) the dates were left out entirely.
This small inconsistency broke the model’s assumptions about time series continuity, causing failures deep into the project.
She reflects:
You think you’ve cleaned everything, and then suddenly, one missing detail throws off your entire pipeline. This showed me that good data cleaning is about anticipating how missing or inconsistent data can break the system later.
This example highlights a key truth:
Catching small issues early saves enormous effort later.
Missing values, inconsistent entries, or overlooked anomalies can pass unnoticed during development, only to cause critical failures during training, testing, or even live deployment.
Building strong, clean datasets requires more than just correcting mistakes. It starts with establishing smart habits and thoughtful processes from the beginning. Almudena Alvarez Bullain shares several strategies that can be used to strengthen their data foundations.
Teams often rush into data cleaning without first fully understanding what their data represents. Almudena emphasizes that one of the most valuable steps is manual exploration:
“Spend half a day just exploring. Understand what every column means, what values are expected, and how they connect to your project goals.”
A deep understanding early on helps to spot inconsistencies and missing information that automated tools might overlook.
Datasets often include far more information than a project actually requires.
“If you’re only going to use eight columns, there’s no point keeping 18,” Almudena gives as example.
Trimming down datasets not only improves processing speed but also sharpens focus on the fields that truly matter for analysis and modeling.
Modern libraries and packages in Python allow teams to automate much of the data cleaning process, from handling duplicates to standardizing formats.
However, Almudena stresses that even when using trusted tools:
“You should never blindly trust automated tools. Always validate that the transformations align with what the project and the client actually need.”
Automation improves efficiency, but careful review ensures the final dataset truly meets project goals.
Natural Language Processing (NLP) projects require an even more careful approach.
Minor differences in phrasing, typos, or unnecessary small talk can distort meaning.
Steps like expanding contractions (“can’t” to “cannot”) and removing irrelevant filler text help models capture the true intent behind the language, leading to more accurate insights.
Many companies only realize the importance of data quality when problems surface during model training or project deployment. By then, fixing issues is time-consuming and expensive. Almudena Alvarez Bullain advocates for a proactive approach: ensuring data quality from the very beginning.
She points out that businesses often put all their energy into later stages like model tuning or stakeholder presentations, without giving enough attention to the data preparation phase.
“There’s much more stress on the flashy end results, and not enough value placed on deep understanding and cleaning of the data early on,” Almudena explains.
A proactive mindset means viewing data cleaning as a critical, strategic stage. Not just a technical hurdle to clear quickly. It involves:
Almudena also emphasizes that cultural habits play a role.
“Sometimes it’s just easier to hope for the best instead of ensuring the best,” she notes.
Changing this habit requires leadership to clearly communicate the value of investing time upfront in data preparation, ultimately saving time, money, and reputational risk down the line.Successful digital transformation starts with mastering the basics.
Thorough data cleaning, combined with a clear understanding of what the data truly represents, creates the foundation for reliable AI models, sharper insights, and better decision-making.Almudena Alvarez Bullain emphasizes:
Without good data preparation, the entire process can fall apart. Clean data should be the foundation everything else is built on.
At COMPUTD, we specialize in helping businesses in HR, logistics, and other traditional sectors modernize through expert data and AI solutions.
By making data quality a priority from the very first steps, companies gain the stability and confidence needed to innovate and grow.
Ready to strengthen your data foundation?
Connect with the COMPUTD team, explore more of our expert insights, and start building a smarter future for your organization today.
