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Data Quality vs Data Quantity in Applied ML

Alexandra Anghel

Last updated on Jul 26, 2024

QUICK SUMMARY

How powerful is machine learning? I’ve detailed the checks and balances that should be considered when looking at data quality and data quantity for applied Machine Learning.

TABLE OF CONTENTS Data Quality vs. Data Quantity: How They Fit Into Machine Learning Trade-offs Takeaways

In the world of data analysis and interpretation, two terms frequently arise; data quality and data quantity. Data quality refers to the accuracy, consistency, and reliability of data throughout its lifecycle.

It underscores the importance of collecting precise, relevant, and timely data for use in decision-making processes, analytics, and operations. High-quality data is clean, well-organized, appropriately classified, and free from redundancies or errors. It is critical in ensuring credibility and offering valuable insights that can propel a business to its desired trajectory.

On the other hand, data quantity pertains to the volume of data collected, stored, and processed. It is often believed that the more data one has, the clearer the patterns and trends. However, having heaps of data does not always result in better insights, especially if the data is of low quality.

It is crucial to strike a balance between the quality and quantity of data. This ensures that big data analytics serve their purpose in driving innovation, predicting market trends, and informing strategic planning.

The never-ending quest for data: more is always better, right? Wrong! In the world of machine learning, quality trumps quantity every single time.

This article explores two sides of the data coin – why both are crucial for building reliable machine learning models and how to strike the perfect balance to unlock powerful insights and avoid misleading results.

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Data Quality vs. Data Quantity: How Do They Fit Into Machine Learning?

While it’s easy to view artificial intelligence as a magic wand that can solve data quality issues by combing through unstructured, nonstandard, and incomplete data to give us a desired output – the reality is the exact opposite.

Data serves as the fundamental foundation for machine learning (ML) models. These models identify trends and patterns and then use this information to make predictions and decisions based on new, unseen data. The more data the model is trained on, the more accurate it can become in predicting outcomes or making decisions.

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Don’t be fooled, though—having a significant amount of data isn’t necessarily sufficient for training a good model. In fact, the saying “garbage in, garbage out” is a well-known concept for Machine Learning engineers, highlighting that flawed data input or instructions will ultimately generate flawed outputs.

Despite this phrase being used frequently, data quality and integrity concerns remain frequently overlooked in applied AI. Most educational materials focus on the mathematical foundation of machine learning and use clean, organized, and pre-labeled “toy” datasets.

In most use cases though, it’s crucial to account for a more realistic scenario: implementing machine learning in a particular domain has to take into account that real-world data is flawed and bad data is a possibility.

Most ML engineers or Data Scientists who work with productionalizing ML models are well-versed in this, as most of the challenges in creating ML models that output quality results are data science-related.

Why is Data Quality Important?

A qualitative dataset in machine learning should represent the underlying problem as closely as possible. High-quality data is crucial for producing reliable machine-learning models. Several aspects contribute to data quality.

Accuracy: Data should be free from errors, inconsistencies, and inaccuracies. Inaccurate data can lead to biased or misleading models.
Completeness: Data should contain all relevant information necessary for the machine learning task at hand.
Consistency across different data sources and over time: Inconsistent data can lead to confusion and errors in model training and evaluation.
Relevancy to the problem being addressed by the machine learning task: Including irrelevant features or duplicates can increase complexity and decrease model performance.
Up to date: Data should be up to date and reflect the most recent observations for certain applications, such as real-time predictions or trend analysis.

Addressing data quality issues often involves preprocessing steps such as data cleansing, filling in missing values, normalization, and feature selection.

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Data quality in practice

So, what does this actually look like in practice? When going into data collection with the purpose of developing a machine-learning model, start by asking yourself the following questions:

Is the data accurate and error-free? Are we missing values or do we have incorrect values?
Is the data linked to the problem we are trying to solve?
Does the data contain enough examples to train the machine learning model effectively?
Does the data contain conflicting or contradictory information?
Does the data reflect a real-world scenario?

The required volume of data depends on the complexity of the problem you are trying to solve, but if your dataset is less than a few thousand entries, a machine-learning model might not be a good solution for your use case. Could the problem be solved using a rules-based algorithm instead?

Quality data is critical to the accuracy and fairness of machine learning models. Plan to carefully curate, preprocess, and validate it to ensure it meets the necessary standards for the problem being solved.

Why is Data Quantity Important?

Data quantity refers to the amount of data available for analysis, typically measured in terms of volume or size. Advanced technologies such as cloud computing, machine learning, and IoT devices make it easy to collect a large quantity of data.

A high volume of data can offer broader insights that enable more informed decision-making, predict behavior patterns, or even create complex algorithms. This massive accumulation of data is often seen in areas like social media platforms, where hundreds of terabytes are generated daily.

Yet, it's crucial to understand that a larger quantity of data doesn't necessarily imply better results. A vast database can oftentimes lead to redundancies, inaccuracies, and noise that can misinform analyses.

Therefore, it is important to double-check the quality of the gathered data. In SaaS development, for example, having a large quantity of low-quality data can lead to erroneous insights that could detrimentally affect software development processes.

Proper data management practices such as data cleaning, integration, and validation should be employed to ensure that the volume of data does not compromise its quality.

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How Does Data Quality Impact Decision-Making?

The quality of data plays a pivotal role in decision-making. It is instrumental in forecasting, strategizing, and analyzing any business's growth metrics. Good-quality data provides an accurate basis for executives to make informed decisions, eliminating the possibility of errors and misleading facts. High-quality data eliminates inconsistencies, which, if untreated, can distort the reality of business performance and future prospects.

The impact of data quality on decision-making lies in its ability to provide a true reflection of the company's standing. Correct, complete, and reliable data enables businesses to precisely identify their strengths, weaknesses, opportunities, and threats. Incorrect or incomplete data, on the other hand, can lead to faulty decisions, often resulting in adverse consequences for the business.

Data, Data and More Data

Let’s take a step back and explore a key question: Why do machine learning models need a lot of data for better decision-making? It’s a good question, but one that’s too often overlooked.

In short, a machine learning model is a combination of a dataset and the algorithm used to train on that particular dataset. So, the same algorithm trained on different datasets will produce very different results.

A machine learning model needs a fair number of examples from which to learn. Depending on the complexity of the problem that it is trying to solve, this often requires different volumes of data, spanning from hundreds of data points for modeling a single user profile to millions of data points for large language models or computer vision models.

The more complex the problem, the more data the model will need to learn to make accurate business decisions. Additionally, if the data is noisy or contains many outliers, the model may require more data to filter out these anomalies.

When a model is trained on a limited amount of data, it may not have enough examples to accurately generalize to new data, resulting in overfitting or underfitting. Basically, the machine learning model learns the dataset “by heart” or fails to capture the underlying patterns in the data, leading to the data analysis yielding poor results.

How Does Data Quantity Impact Decision Making?

The evaluation of the impact of data quantity on decision-making rests heavily on the premise that more data results in more accurate and reliable outcomes. In SaaS development, the sheer volume of data processed allows for a broader understanding of user behaviors, systematic patterns, or anomalies.

Large quantities of data can result in greater predictive accuracy, enabling data-driven decisions that can significantly improve the efficiency and effectiveness of business operations.

For example, monitoring server logs can provide a massive amount of data points, which when analyzed, can lead to identifying potential infrastructure issues before they become a problem.

However, appreciating the value of data quantity should not undermine the potential issues associated with it. While an abundance of data provides a larger pool for meaningful patterns and trends, handling colossal datasets involves certain challenges.

One of the key challenges is ensuring the cost-effectiveness of data storage and processing. Additionally, a larger data set may increase the complexity of extracting helpful information, hence taking up more time and resources.

Therefore, understanding the role of data quantity in decision-making should involve a balanced consideration between the advantages of extensive insights and the implications of managing vast volumes of data.

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Data quality/quantity trade-offs

Collecting vast amounts of data isn't necessarily beneficial unless the data is of high quality and relevant to your research or business needs.

While in-depth analytics and predictions often require large volumes of data, making sure your data supply is accurate, consistent, and clean is just as, if not more, important for machine learning. This ensures that your organization bases its decision-making processes on credible, unbiased information.

As such, striking a balance between data quality and quantity often denotes employing data management strategies that are extensive and selective. It's about inviting more data sources but with an unwavering emphasis on data credibility, relevance, and value. Applying advanced tools and technologies to clean, sort, and analyze data will assist in utilizing big data's full potential without compromising quality.

The reality is that there’s often a trade-off that takes place between the quantity and quality of data. While it’s true that more data can lead to better performance of a machine learning model, that’s only the reality if the data is of high-quality and correct.

However, even a small amount of high-quality data can produce a useful machine-learning model, but only if the model is not too complex. In those instances, you can also use extrapolations to generate more data out of a small, quality dataset.

Takeaways

Unfortunately, there’s no silver bullet solution. However, there are a few considerations that need to be front-and-center when searching for the right balance between the amount and quality of data, including:

Collecting and labeling a massive amount of data can be costly and time-consuming.
If the data is of low quality, it may lead to a model with poor accuracy.
Data can be validated, cleaned, and preprocessed to fix errors, such as removing bad examples or filling in missing values.
If you have a huge dataset, you don’t have to use all of it, as training a model with such a dataset is expensive. In fact, experimentation can be done — varying the dataset size to measure how much data is required to reach optimal performance.

With that in mind, though, it’s important to also consider the specific task and context and determine the appropriate amount and quality of data required for building a successful machine learning model.

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