As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
Data, artificial intelligence (AI), and machine learning (ML) are changing the way we operate in all industries, as we already see it supporting automation in healthcare and fraud detection in finance. However, beyond the power of AI and ML, the underlying data that feeds into these models is vital as it dictates the output of your models. Without first-class data – you can expect unreliable predictions, poor automation, and wasted time and resources.
If you want to build intelligent, reliable, and scalable AI/ML systems, you must prepare your data correctly. This article will outline the essential steps and considered practices to make your data AI-ready and to improve their performance.[1]
In all successful AI/ML projects, the first step will always be to define the desired business outcome. It is important to ask yourself the following questions:
You should use this framework for delimiting what data you will need and how to manage it.
AI does not necessarily rely on all data sources. To find relevant data, use:
Depending upon the application of data, you may need structured data or unstructured data. [2]
Also, a focus on data completeness, diversity and reliability is features of good data.
It is uncommon to receive usable raw data. Data cleaning involves:
You want to make sure that your data is consistent and accurate to avoid poor performance of your model later.[3]
EDA allows you to identify patterns, relationships, and outliers in your dataset. Major components of EDA include:
This is valuable information since it gives your insight into feature importance and your choice of model.
Feature engineering turns raw ingredients into usable variables that your model can learn from. The approaches taken for feature engineering might include:
Features that are well-engineered enable improved accuracy and generalization.
Different units or scales of measurement can throw ML algorithms off. You’ll want to normalize or scale your data for consistency:
This is especially useful for SVM, KNN and neural models.[5]
If you have too many examples from a single class when carrying out a classification task, the model might be biased. You might consider these ways to deal with imbalance:
Ensuring your classes are balanced improves fairness and, in the end, your predictive potential.[3]
High-dimensional datasets can be time-consuming to train and can also result in overfitting issues. To increase your dimensionality reduction:
The idea here is to hold on to as much possible information while eliminating the least number of features.
Always split your data to help prevent overfitting and assess model generalization:
Use stratified sampling to keep the classes balanced, especially for classification problems.
There are fewer data privacy and ethical issues being raised, so compliance of regulations, either GDPR, HIPAA or ISO is necessary. Good standard practices include:
All these can help create trust and drive accountability across your entire AI pipeline.
Data prep is the foundation of success with AI and machine learning. Each aspect of the data problem-solving process, from framing the business problem to cleaning, transforming and structuring your data on a meaningful scale, will have a huge impact on model accuracy and reliability. Without proper data prep, you may encounter significant issues, regardless of the algorithms you to apply in your analysis.[2]
By taking a structured, domain-informed, and quality-first pathway to data preparation, organizations will experience the true benefits of AI and ML, and all this can offer in the form of smarter decision-making and efficiency advantages for sustained competitive advantage.
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