Data Labeling: What It Is and Why It Matters
Improve your machine learning models by creating high-quality data labels.
Data labeling is the practice of adding context to raw data so that machine learning models can generate predictions based on what they learn from the data. ML models perform best when fed data with meaningful, high-quality labels, so data labeling plays a crucial step in the ML pipeline.
However, companies often encounter challenges when attempting to produce high-quality labels. Here are common challenges—and how to overcome them to create better data labels.
Common Challenges with Creating High-Quality Data Labels
Creating ML models for complex applications typically requires an enormous amount of data. Labeling large datasets can be a lengthy, time-consuming task and often involves a lot of manual labor. Taken together, these characteristics lead to a variety of challenges that can affect data quality.
Data Labeling Can Be Expensive
Because data labeling is time-consuming, the process can be costly. Many companies do not have a dedicated team for data labeling, so engineers and analysts might be pulled away from other tasks. Companies that hire a data labeling team accumulate even more costs.
Labelers May Lack Knowledge or Information About the Data
Accurately labeling data often requires expertise. The labeling of de-identified medical images, for example, can be outsourced only to qualified persons.
Additionally, datasets don’t always contain the information needed for a labeler to accurately classify the data. For example, a labeler who is classifying terms into categories and comes across the term mouse would not know, without additional information, whether this word refers to the computer accessory or the rodent.
Miscommunication Between Labelers
Manually labeling a massive dataset often requires a large team of human labelers. Each labeler may have a different interpretation of the labeling criteria and definitions, leading to inconsistent labeling results.
Difficulty Complying with Data Security Standards
Setting up a data labeling system that meets data security standards can be complicated, but it’s vital when handling confidential data. Data leaks and violations of security standards can have serious consequences for a company and its customers. Various data security laws have been enacted around the world, such as the EU’s General Data Protection Regulation (GDPR) and China’s Personal Information Protection Law (PIPL), so it’s important to understand which apply.
What Are Common Approaches to Labeling Your Data?
While some companies prefer in-house solutions to data labeling, others prefer to outsource the entire process. Still others prefer a mixture of both, either building their own data tool and outsourcing the labeling process or purchasing a data tool and performing the labeling themselves. Here are some common approaches to labeling data, and the advantages and disadvantages of each.
Automated Data Annotation Tools
These tools help label large datasets. Some automatically label the data, while others are used to assist in quality control and verification. Automated tools can greatly speed up the labeling process, but their performance directly affects the quality of the data labels. As a result, these tools are often paired with human review.
Crowdsourcing
Crowdsourcing data annotation to a third-party service that uses human labelers can be an effective way to cut down on the costs of data labeling. With this approach, companies release their data to a platform and individuals add labels. This can be an effective approach for companies that have limited internal resources. However, because crowdsourced data labeling is done by a diverse group of people without much oversight, it can result in inconsistent, low-quality data.
In-House Teams
Some companies hire a managed staff exclusively to process and label their data. When data quality is the primary concern, in-house annotation provides more consistent results than does crowdsourcing. For companies with smaller data needs, however, hiring an in-house team can be prohibitively costly. And delegating annotation to inside resources risks pulling employees away from other important tasks.
Outsourcing
Outsourcing the labeling process combines the benefits of crowdsourcing and in-house teams. With this approach, companies benefit from labels that are generated from a managed team of expert labelers without the need to create or manage their own team. Outsourcing companies also often have proprietary tools that can be used to increase the quality of the data even further.
How Can I Improve the Quality of My Labels?
Regardless of how you choose to label your data, there are several things you can do to improve the label quality.
Ensure that the Labeling Instructions Are Clear
Provide all data labelers with consistent instructions for how data should be labeled. Ask labelers to keep an eye out for situations where the correct label is ambiguous. When these situations arise, discuss how these items should be classified, and update your labeling instructions to reflect this change.
Use Consensus Labeling to Spot Inconsistencies
Because people’s viewpoints can vary, labelers may not immediately notice ambiguity in the data they’re labeling. Additionally, when manually labeling a large dataset, a labeler will inevitably make a mistake due to human error.
Consensus labeling leads to more consistent results by having multiple people label the same set of data. If their labels are the same, the value can be accepted. Otherwise, the label can be reviewed and standardized.
Implement an Independent Quality Assurance System
A quality assurance team is responsible for a variety of aspects of data quality, and it can also help to ensure that data labels are consistent and high-quality. This team should consist of individuals who were not involved in data processing or labeling.
The Future of Data Labeling
The amount of data businesses gather is expected to grow exponentially in the next few years. As the quantity of data outpaces our ability to label it, organizations will increasingly rely on automated solutions for data labeling.
ML-powered tools greatly increase the speed at which datasets are labeled while decreasing the human labor required. Additionally, these tools can apply quality control and check for labeling inconsistencies that might not be obvious to a person.
As automated tools become more popular, increasingly robust tools will emerge that provide high-quality labels with less human labor.
Elevate the Quality, Performance of Your ML Models
Although data labeling can be a time-consuming and expensive process, it plays a vital role in ML. By ensuring that your dataset includes meaningful, high-quality labels, you can enhance the quality and performance of your ML models.
Choosing the labeling solution that is best for your company, whether that is automated annotation, crowdsourcing, in-house annotation, or outsourcing, will help you outpace your competition.