How to choose and prepare datasets for decision making?

We live in the information age, where data has become a strategic asset. However, the true value of data is only realized when it is well-structured, analyzed, and intelligently applied in decision-making.

A dataset is the starting point of this journey. It is a structured set of data that can be used for analyses, training AI models, or extracting insights. But it is not enough to have data: it is necessary to have the right data — organized, clean, and relevant to the problem you want to solve.

According to a survey by McKinsey, data-driven companies are up to 23 times more likely to win customers, 6 times more likely to retain customers, and 19 times more likely to be profitable.

However, to reach this level of maturity, it is essential to know how to choose and prepare a quality dataset. In this article, you will understand the criteria for selecting efficient datasets and the best practices for structuring them correctly. Keep reading and discover how to transform data into more precise and confident decisions.

What is a dataset?

A dataset is a structured set of data, typically organized into columns and rows, similar to a spreadsheet. Each row represents an individual record (such as a customer, a transaction, or a product), while each column contains attributes or variables about these records (such as name, date, value, category, etc.).

In other words, the dataset is the raw material used for analyses, visualizations, training machine learning models, and, importantly, to underpin more intelligent business decisions.

A dataset can be simple — like a contact list — or extremely complex, like those used by recommendation systems, fraud detection algorithms, or predictive analytics platforms. What defines its utility is not only the amount of data but also the relevance, quality, and alignment with the analysis objective.

Contrary to what many think, data is not automatically valuable. They need to be organized, contextualized, and ready for use. And that is precisely what a good dataset delivers: structure and consistency to generate reliable insights.

What are the main types of datasets?

Before choosing and preparing a dataset, it is important to understand that there are different types, each suited to specific contexts and objectives. Below, we list the main ones:

1. Structured dataset

This is the most common type in the corporate environment. These are data organized in tables, with well-defined rows and columns — like Excel spreadsheets or relational databases. Examples include customer registration information, sales history, and financial records.

2. Unstructured dataset

This includes data that does not follow tabular organization. This encompasses texts, images, audio, videos, social media posts, or emails. They require more advanced processing techniques (such as NLP or computer vision) to be analyzed effectively.

3. Semi-structured dataset

This is a combination of the two previous types. The data are not in a traditional tabular format but follow a minimal structure that facilitates interpretation, such as JSON files, XML, or system logs.

4. Labeled dataset

Widely used in supervised machine learning, this type of dataset has entries associated with a “response” or “label.” For example, a dataset of emails classified as “spam” or “not spam.” This allows models to learn patterns based on known responses.

5. Unlabeled dataset

Unlike the previous one, these data do not have defined labels. They are primarily used in unsupervised learning, where the model needs to identify patterns and groupings on its own.

6. Synthetic dataset

Artificially generated by algorithms, this type of dataset is created to simulate real-world situations when real data is scarce, confidential, or expensive. They help train models, validate solutions, or conduct performance tests.

How to choose the ideal dataset to train machine learning models?

Choosing an appropriate dataset is one of the most critical steps in the success of a machine learning project. A model will only be as good as the data used to train it — or, as the saying goes, garbage in, garbage out. This means that inadequate, incomplete, or biased data can compromise the entire performance of the model.

Below are the main criteria for choosing a good dataset to train machine learning algorithms:

1. Relevance to the problem

The dataset needs to contain variables that are directly related to the problem you want to solve. For example, if the goal is to predict customer default, it is not enough to use data only about location — it is necessary to have information about payment history, income, duration of the relationship, etc.

2. Quality and integrity of the data

Data with many errors, missing values, or inconsistencies negatively impact the training of the models. It is important to check whether the dataset has undergone a data cleansing process and if it has a reliable structure.

3. Sufficient volume

Although not always more data means better results, in machine learning it is common for models to benefit from a large volume of examples. The larger the dataset, the greater the chance of capturing patterns and reducing overfitting.

4. Diversity and representativeness

A good dataset should reflect the diversity of situations that the model will encounter in the real world. If it is biased (for example, representing only one user group), the model may reproduce or even reinforce these biases in its predictions.

5. Labeled data (if supervised learning)

To train supervised models, it is essential that the dataset contains correct and consistent labels. These labels act as the “correct answers” that the model will use as a basis for learning.

6. Update and temporality

Very old data may not reflect the current reality. Assess whether the dataset is recent enough and whether it needs to be updated frequently, especially in fast-moving contexts, such as finance or retail.

What are the most common mistakes when preparing datasets for analysis or predictive modeling?

The preparation of a dataset for analysis or predictive modeling requires attention to details that go beyond basic data cleaning or variable selection. Many failures arise precisely in the intermediate stages of the process, directly affecting the quality of the results. Below, we highlight frequent mistakes that often go unnoticed:

1. Disregarding the impact of granularity

When assembling a dataset, it is common to mix different levels of granularity — such as daily data with monthly data — without the proper adjustments. This can create distortions in temporal analyses or in training models that depend on a logical sequence of events.

2. Ignoring derived variables that would add value

Instead of just working with the data “as they came,” it is often possible to enrich the dataset with derived variables — such as moving averages, standard deviations, grouped categories, or calculated indicators. The absence of these attributes can limit the model’s predictive power.

3. Not standardizing units and formats

Dates in different formats, unconverted currencies, inconsistent units (such as mixed meters and kilometers) can compromise statistical analyses and cause subtle failures in algorithms. Standardizing the dataset is essential to avoid silent conflicts.

4. Removing outliers without understanding the context

Many professionals automatically exclude outliers, treating them as errors. However, in certain contexts — such as fraud detection or operational failures — outliers are often the most important events. Before excluding any atypical data, it is essential to understand its meaning.

5. Applying irreversible transformations before splitting between training and testing

It is common to apply normalizations, encodings, or dimensionality reductions to the entire dataset before separating it into training and testing. This can generate data leakage, causing the model to “learn” from information it should not know.

6. Underestimating the impact of unbalanced data

When a dataset has very unequal classes (for example, 95% “non-fraud” and 5% “fraud”), the model may learn to ignore the minority and still appear accurate. Detecting this imbalance and addressing it correctly is crucial, especially in classification problems.

Avoiding these mistakes increases confidence in the results obtained from analyses and predictive models. A well-prepared dataset is not just a collection of organized data — it is a solid foundation for generating value from data.

How to build a data pipeline for the construction and versioning of datasets?

As data science and machine learning projects scale, relying on manual processes to prepare datasets becomes unsustainable. Task repetition, difficulty in reproducing results, and lack of control over data versions are common obstacles. The solution lies in building an automated and versionable data pipeline.

A data pipeline is a structured sequence of steps that allows for the continuous, automated, and reproducible capture, transformation, validation, and storage of data. When well-designed, it facilitates not only the preparation of datasets but also change control over time — essential for historical analyses and model reevaluation.

Here are the key elements of an effective pipeline:

1. Data ingestion

It all starts with the collection of raw data, which can occur via APIs, databases, CSV files, IoT sensors, or other sources. This step should ensure data integrity and availability, respecting formats and update frequency.

2. Validation and cleaning

Immediately after ingestion, it is essential to apply validation rules — such as format checks, removal of duplicate records, or filling in missing values. This step ensures that the dataset does not carry flaws that compromise later stages.

3. Transformation and enrichment

Here, the data is converted to the necessary format: normalizations, type conversions, creation of derived variables, and cross-referencing with external sources enrich the dataset and increase its analytical potential.

4. Versioning and traceability

Tools like DVC (Data Version Control), Delta Lake, or even Git-integrated solutions allow datasets to be versioned as if they were code. This ensures that each machine learning experiment uses the same database and facilitates audits, version comparisons, and rollbacks.

5. Storage and availability

At the end of the pipeline, the ready data should be stored securely and accessibly — whether in a data lake, analytical database, or structured cloud storage. Ideally, the dataset should be accessible by different teams without losing context.

Building this pipeline from the start contributes to the scalability, agility, and reliability of data-driven projects. It reduces rework, avoids inconsistencies, and ensures that the use of datasets is always transparent and auditable.

What to evaluate in the curation of datasets for applications in computer vision, NLP, or prediction?

The curation of a dataset goes beyond cleaning or organizing data. When we talk about specific applications such as computer vision, NLP, or time series-based predictive models, the process of choosing and preparing data requires tailored criteria. Below, we highlight key points for each type of application:

Computer vision: much more than images

Computer vision projects — such as facial recognition, object detection, or OCR — require datasets with high-quality images and precise annotations. Here are some critical points:

• Variety of scenarios and lighting conditions: models that only see well-lit images tend to fail in real environments.
• Appropriate resolution: low-resolution images can hinder the detection of subtle patterns.
• Well-aligned annotations: whether in bounding boxes, segmentations, or classifications, consistency in labeling is essential.
• Demographic diversity: in cases like facial recognition, a lack of diversity can lead to serious biases.

NLP: textual quality and linguistic context matter

In projects involving natural language — such as sentiment analysis, chatbots, or text classification — the challenge lies in the language itself. Evaluate:

• Presence of noise and grammatical errors: data from social media, for example, require careful preprocessing.
• Lexical and semantic diversity: slang, regionalisms, and linguistic variations need to be represented.
• Context and ambiguity: short texts and out-of-context (such as titles or tweets) can have ambiguous meanings and affect model accuracy.
• Balance between classes: it is common for certain sentiments or topics to be over or under-represented.

Predictive models: attention to temporality and data signals

For applications that involve forecasting — such as churn, demand, or time series — the dataset needs to reflect the evolution of data over time. Pay attention to:

• Temporal alignment: future data should not be available during training.
• Delay between cause and effect: understanding the time between user behavior and the event to be predicted is fundamental.
• Stability of variables: highly volatile or structurally breaking variables can confuse the model.
• External events: identifying periods impacted by external factors (e.g., pandemics, seasonality, promotions) prevents incorrect interpretations.

How do tools like Databricks facilitate the management and reuse of datasets?

Modern tools like Databricks are transforming the way companies handle their data — especially when the challenge is to manage large volumes of information in a collaborative, secure, and reproducible manner.

Based on lakehouse architecture, Databricks allows combining the best of data lakes (flexibility and scalability) with the rigor of data warehouses (structure and governance). This makes the process of building, versioning, and reusing datasets much more agile and reliable.

Here’s how the platform directly contributes to the efficient management of datasets:

• Versioning with Delta Lake: allows tracking the history of changes in data, comparing versions, and restoring previous states — essential for traceability and reproducibility in machine learning projects.
• Collaborative environment: engineers, data scientists, and analysts can work on the same pipeline in an integrated manner, without silos or rework.
• Integration with notebooks, pipelines, and MLflow: facilitates everything from dataset preparation to model training and deployment in the same workflow, speeding up time-to-value.
• Data security and governance: with access control based on roles, auditing, and masking of sensitive data, Databricks ensures compliance with corporate policies and regulations.

Moreover, the ability to scale processing with distributed clusters allows companies to analyze billions of records without compromising performance — which is essential for those looking to create reusable datasets ready for multiple projects.

In practice, choosing the right tools is only part of the journey. Having a well-defined strategy and experienced experts to structure pipelines, prepare data, and operationalize the use of datasets is what guarantees concrete results.

Objective is an official Databricks partner in Brazil and works alongside companies wishing to extract the maximum value from their data — from curation to the construction of scalable predictive models.

Want to evolve the way your company collects, organizes, and transforms data into intelligent decisions? Talk to our specialists and discover how we can help.