Lead Data Scientist
Sand Technologies
Poor data quality is one of the top reasons that artificial intelligence (AI) projects fail. Think of data as the foundation of a skyscraper — just as weak materials would jeopardize the building’s stability, inaccurate or incomplete data can compromise an AI model’s performance. AI models are only as good as the data the model uses. Preparing data is critical to ensure accurate and meaningful results to unleash the full power of AI in a company’s operations. Data that is clean, uniform and ready to process is a requirement. Before starting an AI project, follow these five steps to prepare your data for robust and reliable models.
Data Collection
Data Cleaning
Data Labeling
Data Transformation and Feature Engineering
Training, Validation and Testing Sets
Gathering relevant, high-quality data from diverse sources ensures the AI system has enough information to learn and make accurate predictions. This step helps teams improve the accuracy and effectiveness of an AI model.
Collect data representative of the intended use cases. Determine whether real-time data, such as consumer behavior analytics, would enhance the AI model’s performance. Consider external sources like third-party datasets if internal data doesn’t meet the requirements.
Assess all datasets for both completeness and potential gaps. Are all necessary variables included? Identify missing or inconsistent data patterns that could distort the AI’s output.
Identify the data types available (structured, unstructured, or semi-structured) to help determine the preprocessing needed and the type of AI model to use.
The format of the data dictates how easily AI algorithms can process it. Typical data formats include CSV from spreadsheets, JSON for APIs, and XML for legacy systems. Mapping out these formats at the start streamlines the preparation process and prevents future compatibility issues.
Tool options for data collection: Python libraries (Pandas, BeautifulSoup) and database systems (SQL) are particularly effective in this phase.
Data cleaning is a detailed data quality assessment that involves removing duplicates, filling in missing values and fixing errors to ensure consistency and accuracy. For example, the AI model might produce unreliable predictions if the training dataset contains duplicate entries or incomplete transaction details.
Ensuring data accuracy improves the reliability of the AI results and enhances decision-making processes. For example, a lending AI trained with inaccurate data could approve high-risk loans or deny low-risk ones in the financial sector, leading to revenue loss or reputational damage.
By cleaning and standardizing data — removing duplicates, filling in missing values and ensuring consistency – teams provide a solid foundation for an AI model. Missing values, if left unaddressed, can distort the training process of an AI model and lead to unreliable predictions. To handle this issue, teams can either impute missing values (filling them with the median, mean, or a predictive estimate) or drop the incomplete data points altogether. The choice depends on the volume of missing data and its potential significance. If the input data is incomplete, contains duplicate entries, or inconsistent information, the resulting model may produce unreliable or biased outputs — something no company can afford.
Consistent data formatting is crucial. Consider date and time formats, such as MM/DD/YYYY or YYYY-MM-DD. AI models can misinterpret or reject this data without a standardized approach, leading to flawed predictions or wasted resources. Similarly, currency values must be uniform, whether expressed in USD, EUR, or another denomination, to avoid skewed analyses. Standardizing these formats ensures the data is clean, interpretable and ready for preprocessing.
Identify and manage outliers (data points that deviate significantly from the norm). Outliers can distort AI models. To handle anomalies, techniques such as trimming (removing the extreme values), capping (limiting the influence of outliers by setting a maximum threshold), or applying transformations (e.g., logarithmic scaling to normalize data) are often employed.
Suppose a dataset includes irrelevant chatter like minor errors, outliers, or inconsistencies that don’t reflect accurate trends. In that case, this “noise” can misrepresent the underlying patterns AI models need to learn from. Teams can refine the dataset by applying techniques such as filtering inconsistencies or smoothing abrupt spikes, allowing the AI to focus solely on meaningful signals.
Tool options for data cleaning: OpenRefine or Python’s data-cleaning libraries (e.g., Pyjanitor, Pandas) can streamline this process.
Data labeling is an essential process for supervised learning models. This step involves categorizing or annotating data to ensure the AI system can learn to identify patterns and relationships effectively. For example, data labeling could identify fraudulent transactions in the financial sector. A bank could label thousands of transactions as “fraudulent” or “legitimate,” providing the AI model with clear examples to analyze. By doing so, the model can recognize suspicious activity in real time.
Two key steps are normalization and standardization. Normalization transforms data into a uniform scale, typically between 0 and 1, ensuring larger values don’t distort the model’s calculations. For example, if one column in the dataset measures transaction amounts in thousands and another represents credit scores under 1,000, normalization ensures both have equal weight when processed by AI algorithms.
Standardization adjusts data to have a mean of zero and a standard deviation of one, which is particularly useful when the data follows a Gaussian distribution. Both techniques help AI models interpret data more effectively, improving prediction accuracy without biasing results toward unbalanced inputs. Standardization is leveling the playing field for the data—an essential step to delivering reliable insights.
Data transformation includes techniques like discretization and binning. Discretization involves converting continuous numerical data into discrete intervals, much like assigning letter grades (A, B, C) instead of numeric scores in a test. Similarly, binning organizes data into ranges or “bins” to simplify its complexity, such as categorizing customer ages into brackets like 18–25, 26–35, etc. These transformations are crucial in helping AI algorithms identify patterns more effectively, especially when dealing with inconsistent or noisy data.
Another key challenge is managing discrepancies between sources. Imagine combining transaction records from two financial institutions — one categorizes expenses by date while the other organizes them by category. These inconsistencies can confuse an AI model, leading to skewed predictions or inaccurate insights. By cleaning, normalizing and aligning the data formats, teams ensure the AI interprets the data consistently and effectively.
One fundamental aspect is combining data from various sources into a unified dataset ready for AI modeling. For example, the data team might pull transaction data from bank records, customer demographics from CRM systems and market trends from external sources like stock exchanges. These disparate datasets can differ in structure, format and even quality. Cleaning, normalizing and harmonizing this data ensures consistency.
One of the most critical steps is labeling data for classification and regression tasks. For classification tasks, labels help the model identify distinct categories, such as types of products or customer segments. For regression tasks, labeling often involves assigning numerical values. Accurate and relevant labeling helps the AI model build a foundation to learn patterns.
Companies must decide on their approach to labeling data: human annotation or automated labeling. Human annotation involves experts manually labeling data to ensure precision and contextual understanding. Automated labeling uses AI or algorithms to quickly label large volumes of data, making it ideal for repetitive or straightforward tasks like categorizing transaction types.
While automation introduces efficiency, its downside is the potential for inaccuracies, especially without thorough validation. For some industries, the best practice often combines both methods: using human annotators to create a gold-standard dataset and training automated systems to replicate those standards at scale. By striking this balance, organizations can improve consistency, reduce bias and ensure the AI model trains on reliable, accurately labeled data.
Tool options for data labeling: Labelbox and Amazon SageMaker Ground Truth provide frameworks for scalable annotation projects.
This process involves converting raw data into a format suitable for AI models to interpret and learn effectively. Common techniques include scaling numerical values to ensure consistency across features, encoding categorical data into numerical formats that algorithms can process, and creating new features that enhance the predictive power of a model.
Unsupervised learning in data preparation is a machine learning technique where algorithms analyze and cluster unlabeled data. This process means the data doesn’t have predefined categories or labels to discover hidden patterns and relationships without human intervention. This process is often used in the initial exploratory phase to gain insights about the data structure before further analysis or modeling.
Supervised learning prepares a dataset with known outcomes for each labeled data point. This process allows a machine learning model to learn the relationship between input features and the expected output to make predictions on new, unseen data. The data is “supervised” by having clear labels for the desired outcome during training.
Feature extraction is about identifying the most relevant information (or “features”) in the dataset that help the AI make predictions or decisions. For example, if using AI to predict stock prices, key features include historical price trends, trading volume, or economic indicators.
Representation learning takes this a step further by enabling AI models to automatically discover and capture complex patterns or relationships within the data. This strategy is beneficial when the data is too vast or unstructured for traditional feature extraction. These steps are vital because they help refine and optimize the data so the AI application can precisely deliver insights, especially in data-intensive sectors.
Data integration involves converting data into a format suitable for the chosen AI model (e.g., numerical encoding and feature scaling). It’s critical to integrate and format it to align with the selected model’s requirements to ensure the data is ready for AI. For instance, raw data might include categorical information like “yes” and “no” or dates in various formats — neither of which an AI algorithm can process directly. Converting data into numerical formats through encoding (like turning “yes” into 1 and “no” into 0) is a key step.
Similarly, feature scaling ensures that different data points, such as account balances in the thousands and percentages under 100, are normalized to prevent the AI model from favoring one attribute.
Effective data integration ensures that diverse data sources consolidate into a cohesive and usable format. During this process, it’s crucial to consider whether the dataset includes unnecessary or redundant variables that could overwhelm an AI model or reduce its accuracy. In this case, dimensionality reduction techniques, such as Principal Component Analysis (PCA) or feature selection, come into play. These approaches help simplify the data by identifying the most relevant variables, streamlining computational processing and improving the model’s ability to deliver actionable insights. This step is a game-changer for building efficient and performance-driven AI models.
Tool options for data transformation: Labelbox and Amazon SageMaker Ground Truth provide frameworks for scalable annotation projects; Python libraries like Scikit-learn and TensorFlow offer useful utilities for transformations.
One last step in preparing data for AI is to ensure the dataset is split into training, validation and testing sets to validate model performance. This division is crucial to ensure the AI model is robust and reliable when applied in real-world scenarios. The training set allows the model to learn patterns and relationships, while the validation set aids in fine-tuning its parameters and preventing overfitting. The testing set independently evaluates the model’s accuracy and functionality.
There are several strategies for dataset splitting. Some companies follow an 80-10-10 split, while others follow 70-15-15. For example, in the financial sector, a bank developing a fraud detection AI might collect transactional data and allocate 70% of this dataset for training, 15% for validation, and 15% for testing. This strategy ensures the model doesn’t just flag suspicious patterns in past data but performs consistently when processing unseen transactions—a critical requirement for reducing financial losses and ensuring customer trust.
Tool options for training, validation and testing: Scikit-learn’s ‘train_test_split’ method uses an 80-10-10 split.
Preparing data for artificial intelligence is a critical first step in ensuring the success of an AI model. Why? Because the quality of the data directly impacts the insights and predictions the model can deliver. Teams should not overlook this critical process. As mentioned previously, data quality is one of the top reasons that AI projects fail.
Dedicate significant effort to properly cleanse, organize and label data before entering any AI pipeline. While the timeframe for preparing data varies depending on the size and complexity of a dataset, it’s worth taking the time required for this stage. Careful data preparation will mitigate risks and ensure more accurate insights, making it a non-negotiable step in any AI-driven project.
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