Understanding the symbiotic relationship between data and AI
Jeanne-Louise Viljoen, Data Engineer at PBT Group
Building effective artificial intelligence (AI) models requires more than sophisticated algorithms. It demands a deep understanding of the data powering those models. Despite the best planning and development, an AI model can underperform or even fail if the data used is not suitable.
For AI to generate meaningful insights, it must be trained on high-quality data. The success of an AI model is intrinsically linked to the characteristics of the data used in its development. Simply put, the better we understand and prepare data, the more effective the model will be.
Key elements to consider in data preparation include data types, preprocessing techniques, and an awareness of potential data issues that may arise.
Understanding data types and preprocessing
Different data types require different preprocessing steps. Understanding these is fundamental to effective model development.
1. Data types as the foundation: Knowing your data types (for instance, numerical, categorical, or textual) is essential. This influences the feature selection, model choice, and preprocessing techniques. Numerical data may require normalisation, categorical data often needs encoding, and text data usually undergoes tokenisation. Selecting the appropriate preprocessing for each data type helps ensure the model’s foundation is solid.
2. Feature selection and model alignment: Data types also inform feature selection, as not all features contribute equally to model performance. Understanding which features hold valuable insights allows data engineers to filter out irrelevant data, improving model accuracy and efficiency. Additionally, the model’s suitability depends on data characteristics; for example, decision trees work well with categorical data, while neural networks excel with continuous numerical data.
3. Data quality and early issue detection: Recognising data quality issues – such as missing values, outliers, or incorrect data types — early in the process maintains model integrity. Thorough data preprocessing not only resolves these issues but also enhances the effectiveness of model outcomes, helping prevent errors that could arise later in deployment.
Avoiding common mistakes
Several common mistakes can impede AI model effectiveness if not addressed. Here are some key pitfalls to be aware of:
1. Insufficient or irrelevant data: Having too little data or data that does not align with the model’s intended task can lead to poor performance. Models trained on outdated or irrelevant datasets, for instance, may produce inaccurate classifications, especially in applications like fraud detection or image recognition.
2. Incorrect labelling and imbalanced datasets: Inaccurate data labelling and imbalanced datasets can lead to biased models. Labelling errors misguide the model, while an imbalance in dataset classes can skew results, particularly for classification tasks, where an overrepresentation of one class distorts predictions.
3. Data leakage and feature interactions: When training data inadvertently includes information from the target variable, data leakage occurs, misleading the model into learning patterns that won’t exist in real-world applications. Additionally, neglecting feature interactions or high correlations between features can confuse the model, reducing interpretability and effectiveness.
4. Overlooking temporal and statistical properties: Time-series data and datasets with changing statistical properties require unique handling. Ignoring temporal relationships or shifts in data properties can result in models that fail when applied to new, unseen data, as they may not adapt well to real-world changes.
5. Data augmentation and standardisation: Poor standardisation or inadequate data augmentation can limit a model’s generalisability. Without these processes, models are prone to overfitting, making them less capable of handling new data, ultimately reducing their reliability.
Poor data handling
Here are several repercussions when data issues go unaddressed:
1. Reduced model accuracy and generalisation: A model trained on poorly prepared data may excel during training but struggle with unseen data, limiting its accuracy in practical applications. This leads to models that are either too simplistic (underfitted) or overly complex (overfitted) for real-world use.
2. Biased and unstable predictions: Bias in training data can lead to skewed predictions, especially in sensitive applications like hiring or credit scoring. Furthermore, model instability hampers interpretability, complicating the ability to derive reliable insights from the data.
3. Increased resource and operational costs: Inadequate data selection and preprocessing extend training times, drive up operational costs, and delay model deployment. Inefficient data handling wastes resources that could be better spent on refining the model and deriving insights.
4. Ethical and legal implications: Models trained on biased data or with poor feature representation can lead to unethical outcomes or discriminatory practices, risking legal consequences and reputational harm. Ensuring responsible data practices in AI is not only beneficial for performance but also essential for ethical integrity.
5. Complex debugging: Poor data handling, including irrelevant features or unaddressed missing values, complicates debugging, making it challenging to identify and correct performance issues efficiently.
As AI adoption grows, robust data management practices will continue to be essential, not only for successful AI deployment but also for fostering trust and value in AI-driven decisions.
