You’ve probably seen social media hashtags that categorize posts into easily viewed groups. Data labeling is pretty much the same thing, but for machine learning (ML). 

These labels are what ML models learn from to make predictions and recognize patterns. The clearer and more consistent the labels, the better the system performs. 

But if you’re still scratching your head about what data labeling really is, don’t worry — in this guide, we lay out the entire process, including types of data labels, potential applications, and the benefits and drawbacks of using them. 

What is data labeling?

Data labeling is the process of adding a descriptive tag to unstructured data, like images, videos, audio, or text. This helps an ML model categorize and contextualize what it’s seeing. Examples include adding descriptive text that identifies objects in a photo, tagging emotions in a customer review, or noting when a sound clip includes a voice. 

These labels provide ML models with the additional context they rely on to learn patterns. If you show a model thousands of photos labeled “cat,” it will start recognizing which features make up a cat. 

Types of data labeling

Not every type of data labeling is useful for every ML model. Here are some of the common varieties and how they’re used.

Image classification

Image classification assigns a single label to an entire image. It’s used when an ML model needs to understand an image’s primary purpose so it can quickly sort and categorize information — for example, tagging photos as “cat,” “dog,” or “car.” Common image classifications also include metadata like the pixel number or whether an image is landscape or portrait. 

Object detection

Object detection identifies multiple items within an image. Annotators draw boxes around each object and provide each with a unique label, such as identifying every pedestrian or traffic sign in a street scene. This type of data labeling is crucial for applications where spatial reasoning matters, like driver-assist systems. The model learns both what something is and where it appears.

Semantic segmentation

Semantic segmentation assigns a label to every pixel in an image for a more detailed understanding of the scene. For example, instead of just drawing a box around a car, each pixel that makes up part of the image is assigned “car” as a label. The level of detail allows models to distinguish between closely packed or overlapping images. It’s widely used in industries like autonomous driving and medical imaging, where precision is non-negotiable.

Named entity recognition (NER)

NER helps models identify useful information within text. It identifies words that represent things like names, places, brands, dates, and other important details. For example, in “Apple opened a new store in London,” an NER system would label “Apple” as a company and “London” as a location. By extracting these key details, models can better understand what the content is about. This makes search results more relevant, recommendations more accurate, and large documents easier to organize and analyze. 

Sentiment analysis

Sentiment analysis determines how people feel based on what they write. Text is labeled as positive, negative, or neutral, like identifying whether a customer support ticket expresses satisfaction or frustration. With enough labeled examples, models learn to recognize tone, intensity, and even slight shifts in sentiment. Thanks to these labels, organizations can quickly understand trends in how people feel about products, brands, or experiences.

Speech-to-text transcription

Speech-to-text transcription pairs audio recordings with written transcriptions, teaching a model how spoken words map to text. Annotators typically segment the audio into short frames and align them with a corresponding text transcript. 

Example uses for the labeled data include speech recognition models that train virtual assistants, call-center analytics tools, and accessibility software. With enough precise examples, models get better at understanding different accents and tones, and they can decipher speech even when there’s a lot of background noise.

The data labeling process

The data labeling process involves a few key steps. Here’s a rundown of the process.

Step 1. Data collection

First, the raw data (like images, text, or audio) that’ll be used to train an ML model is gathered. How the data is sourced depends on organizational goals. It could be from public datasets, sensors, or user interactions. Collecting the right data matters because it determines what the model will realistically learn to handle.

Step 2. Labeling task definition

This step clarifies what needs to be labeled and how, including instructions, categories, and examples. Clear task definitions help annotators stay consistent, so the model learns reliably.

Step 3. Labeling execution

Human annotators or automated tools apply labels to the collected data based on the defined rules. How they apply those labels shapes the model’s ability to recognize patterns. Accurate execution ensures the model learns from the right signals. 

Step 4. Quality assurance

This step checks labeled data for errors, inconsistencies, or missing information. It often includes human review, automated checks, and expert oversight. Strong quality control prevents flawed data from reducing model performance.

Step 5. Data storage and management

Labeled data needs to be well-organized and stored securely so teams can track versions, make updates, and reuse data. Good storage practices help maintain a reliable foundation for future model improvements.

Benefits of data labeling

High-quality data labeling gives ML models the clarity they need to perform well. A few benefits of data labeling include:

• Precise predictions: High-quality data labeling helps models learn subtle differences between categories. This leads to more reliable predictions, which is especially important for sensitive tasks like fraud detection or medical screening. 
• Better data usability: Labeling transforms chaotic raw data into information that a model can quickly organize, search, and analyze. Teams can test ideas faster because data is already structured for model training.
• Data quality: Catching messy or incomplete data and improving it before training begins is a core part of the data labeling process. Proactive cleanup prevents problems from surfacing when deploying the model.
• Data accuracy: Accurate labels align model behavior with real-world expectations. The result is an output that users can trust, even as conditions and inputs develop.

Challenges of data labeling

Handling data can be messy. Here are a few challenges you might encounter during the data labeling process:

• Ambiguity in labeling tasks: Some data isn’t straightforward to interpret — like determining whether a product review is genuinely positive or sarcastic. Without clear rules, annotators may label the same data differently from one another, weakening model learning.
• Inconsistent labeling: Different annotators may interpret guidelines in slightly different ways, leading to inconsistent labels across the dataset. This can introduce clutter that makes model training less effective. 
• Scalability issues: As models grow, they require massive amounts of labeled data, which is difficult to produce quickly at high quality. Scaling up without sacrificing accuracy can be a major operational hurdle.
• Cost and time constraints: Human labeling is often slow and expensive, especially when domain expertise is required. These constraints can delay development timelines and limit how often datasets can be updated.

Data labeling example applications

Machine learning and data labeling power many real-world applications, from product recommendations to fraud alerts to safer self-driving systems. Here are a few example applications.

Autonomous vehicles

Self-driving systems rely on labeled images and sensor data to identify roads, pedestrians, signs, and objects. McKinsey predicts autonomous vehicles will be as common as household appliances by 2040. Scalable, precise labeling will be essential to train systems for the enormous variety of scenarios they’ll encounter.

Healthcare diagnostics 

Medical images like X-rays and MRIs are labeled to highlight diseases, abnormalities, or risk indicators. Models trained on this data assist doctors in early detection and faster decision-making, and more accurate labels support more consistent diagnoses.

Natural language processing

Labeled text helps ML models understand meaning, tone, and intent in human communication. It powers tools like chatbots, search engines, translation apps, and content moderation systems. High-quality language labels improve clarity, responsiveness, and overall user experience.

Financial fraud detection

Transaction data can be labeled as legitimate or suspicious based on past behavior patterns. Models use these examples to flag unusual activities before significant losses occur, enabling financial institutions to spot and act upon fraud quickly.

Customer service automation

Labeling chat transcripts and support messages teaches models how to respond to different questions or emotions. This process improves chatbot accuracy and helps route inquiries to the right agents. 

How Fivetran enables efficient data labeling workflows

While Fivetran doesn’t label data itself, it simplifies how to label data for machine learning by delivering clean, structured datasets through automated extract, load, and transform (ELT) and extract, transform, and load (ETL) pipelines. By eliminating manual extraction, transformation, and integration tasks, Fivetran ensures data teams have reliable inputs ready for labeling. 

Get started for free to see how Fivetran can streamline the data labeling process.

FAQs

What are the best methods for labeling a dataset?

The best methods for AI data labeling combine expert human input with automation, supported by clean, unified data pipelines like Fivetran to ensure consistently high-quality training inputs.

How does AI-assisted data labeling work?

AI-assisted data labeling uses ML to pre-annotate information, helping teams scale the process once they start with structured datasets. This makes it easier to identify what labeled data is and how it improves model performance.

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