What are the best data sources for your machine learning?

Data is the fuel of machine learning. Without quality data, your machine learning models will not perform well or deliver the results you expect. But how do you find the best data sources for your machine learning projects? In this article, we will explore some criteria and tips to help you choose and evaluate data sources for your machine learning needs.

1 Data relevance

The first and most obvious criterion is data relevance. You want to use data that is related to your problem domain, target variable, and features. Data relevance means that the data reflects the reality and context of your machine learning goal, and that it is representative of the population or sample you want to generalize to. For example, if you want to build a machine learning model to predict customer churn, you need data that contains information about customer behavior, preferences, transactions, feedback, and retention.

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Helen Wall

LinkedIn [in]structor | Data Science Consulting
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An increasing amount of data in the world today is automatically generated through logs. These can be error logs, transaction records, or automatic weather measurements (to name a few). I’ve found that this data often works best in machine learning models because it is automatically generated and not subject directly to human input variations or errors.

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2 Data availability

The second criterion is data availability. You want to use data that is accessible, reliable, and affordable. Data availability means that you can obtain the data from a trustworthy source, that the data is updated and maintained regularly, and that the data does not violate any legal or ethical constraints. For example, if you want to build a machine learning model to detect fraud, you need data that is secure, compliant, and timely.

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Richard Smith

Pathfinder for Implementing Artificial Intelligence in Talent Management | Innovation | Human Capital Management | Data Engineering | Labor Economics
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One thing I’ve found helpful in the world of Data and AI is recognizing the significance of data availability. It’s not just about access; it’s about trusting the source, ensuring regular updates, and adhering to legal and ethical norms. An example I’ve seen is building a fraud detection model. Secure, compliant, and timely data are essential for accurate predictions and maintaining ethical standards.

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3 Data quality

The third criterion is data quality. You want to use data that is accurate, complete, and consistent. Data quality means that the data does not contain errors, missing values, outliers, duplicates, or biases. Data quality also means that the data is formatted and structured in a way that is compatible with your machine learning tools and methods. For example, if you want to build a machine learning model to classify images, you need data that is clear, labeled, and standardized.

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4 Data diversity

The fourth criterion is data diversity. You want to use data that is varied, balanced, and comprehensive. Data diversity means that the data covers a wide range of scenarios, cases, and outcomes, and that it does not favor or exclude any groups or categories. Data diversity also means that the data is sufficient and scalable for your machine learning task and model complexity. For example, if you want to build a machine learning model to generate natural language, you need data that is rich, diverse, and contextual.

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Walter Gentile

Deliver value across the enterprise through Digital Transformation|Business Intelligence|Business Applications|Process Automation|RPA/ML/AI|Team & Project Management
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In real-world applications data are often imbalanced, this poses challenges like - reduced accuracy - generalization issues - bias Techniques to address this include for example - oversampling - undersampling - suitable metric selection - synthetic data generation like SMOTE These techniques are effective for large binary classification problems. In my experience for example in IT service desk automatic ticket classification, where Pareto principle leads to imbalanced multi-label data, applying standard techniques can be difficult. Focusing on well-represented classes, corresponding to common issues, might be preferable. However, rigorous testing and refinement remain vital to ensure model performance aligns with expectations.

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5 Data usability

The fifth criterion is data usability. You want to use data that is easy to understand, analyze, and manipulate. Data usability means that the data has clear definitions, descriptions, and metadata, and that it follows common standards and conventions. Data usability also means that the data is compatible and interoperable with other data sources and systems, and that it can be easily integrated and transformed for your machine learning pipeline. For example, if you want to build a machine learning model to recommend products, you need data that is structured, organized, and linked.

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6 Data ethics

The sixth and final criterion is data ethics. You want to use data that is fair, transparent, and responsible. Data ethics means that the data respects the privacy, consent, and rights of the data subjects and owners, and that it does not harm or discriminate against any individuals or groups. Data ethics also means that the data follows the principles and values of your organization and stakeholders, and that it adheres to the best practices and guidelines of your industry and domain. For example, if you want to build a machine learning model to diagnose diseases, you need data that is ethical, trustworthy, and accountable.

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Richard Smith

Pathfinder for Implementing Artificial Intelligence in Talent Management | Innovation | Human Capital Management | Data Engineering | Labor Economics
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In the realm of Data and AI, the final criterion, data ethics, holds paramount importance. It’s about using data that’s fair, transparent, and responsible. Upholding privacy, consent, and rights of data subjects is key, while avoiding harm or discrimination. Data ethics align with your organization’s values and industry best practices. For instance, in diagnosing diseases with machine learning, ethical, trustworthy, and accountable data is vital.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best data sources for your machine learning?

1

2

3

4

5

6

7

1 Data relevance

2 Data availability

3 Data quality

4 Data diversity

5 Data usability

6 Data ethics

7 Here’s what else to consider

Machine Learning

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What are the best data sources for your machine learning?

1

2

3

4

5

6

7

1 Data relevance

2 Data availability

3 Data quality

4 Data diversity

5 Data usability

6 Data ethics

7 Here’s what else to consider

Machine Learning

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