Data annotation constantly evolves, and companies use it to improve their operations. Data annotation provides the artificial intelligence and machine learning (AI/ML) model with information it can process when responding to user-submitted tickets. Among the most popular examples of the result of data annotation’s use is chatbots.
However, is there more to AI/ML than just chatbots? Are there more data annotation examples and use cases in various industries? Find out in this article.
Before diving into the examples, it’s first important to understand what data annotation is as a job.
What Is the Main Purpose of Data Annotation?
The main purpose of data annotation is to assist machine learning models in understanding and interpreting data. It improves the performance of machine learning models, as they depend on the quality of data they receive to produce high-quality and accurate predictions.
Data annotation makes raw data understandable for machines. Think of it as teaching a child to read. At first, a child cannot differentiate between words. But as you teach them, they can slowly read words and understand their meanings.
Machine learning models are similar. They require the assistance of data annotation to understand what they need to do with the data they are given.
Who Needs Data Annotation?
Any industry that uses artificial intelligence or machine learning needs data annotation. Data annotation streamlines information processing, making it usable for real-world functions.
For instance, with data annotation used in customer care, machine learning models can offer customers human-like responses, enhancing their experience.
It is important to note that for industries to use data annotation and AI/ML models, they must first ensure the quality and reliability of the data they process, which requires the assistance of humans. This is called human-in-the-loop machine learning.
Data Annotation Examples in Different Industries
Numerous industries require efficiency and precision to function. To achieve this, they use data annotation to assist them.
Here are eight examples of how data annotation is used in various industries:
1. Retail & eCommerce
Amazon, one of the leading ecommerce companies today, boosts customer sales by offering product recommendations and excellent customer service. Data annotation plays a significant role in this type of customer service.
Data annotation enhances the accuracy of the products they recommend to customers, increasing their satisfaction. They use AI to analyze customer behavior and identify patterns and preferences while browsing products.
Another example is Starbucks’ online ordering system. Business Insider reported that mobile orders make up over 30% of Starbucks’ store transactions. With data annotation, Starbucks enables its customers to personalize their mobile orders.
2. Healthcare
One of the primary examples of data annotation in the healthcare industry is the creation of medical analysis tools. For example, medical professionals use data annotation to train machine learning systems to recognize diseases and disorders.
To accomplish this, data annotators feed the ML model with labeled medical images such as cancer cells, tumors, X-rays, CT scans, and MRIs.
In addition to diagnosing, medical professionals use data annotation to organize medical data and patient information. This process ensures effective and accurate medical care.
It is important to note that while AI is already advancing, the medical field still faces some challenges related to data annotation. Discover more about it on Medium.
Discover the role of artificial intelligence in medical surgeries on LinkedIn.
3. Self-Driving Vehicles
Self-driving vehicles primarily use image annotation. They use cameras, radar, and Light Detection and Ranging (LiDAR) to identify objects related to cars and roads, such as traffic signs, pedestrians, vehicles, and other relevant data. It is one of the best examples of data annotation in the tech industry.
Self-driving cars use this data to predict the speed, location, and speed of objects when they encounter them on roadways. This is essential to ensure the passenger’s safety and any car encounters while driving.
LiDAR is a sensor-based system that creates 3D models of an autonomous vehicle’s surroundings. It helps self-driving cars navigate on roadways. Find out more about LiDAR in this Forbes article.
4. Sports Analytics
Game tactics and strategies are essential components of every sport. Everyone can agree that a great strategy is behind every good play. But did you know that sports analytics is also an example of how data annotation works?
Data annotation specialists annotate sports data to identify player behavior, including body points, goals, tackles, passes, and other movements related to the game. The goal is to assist coaches in understanding player performance.
With data annotation, coaches can create a more effective training and game plan.
5. Agriculture
One example of data annotation in agriculture is detecting pests. It is crucial to detect pests early, as they can cause significant profit losses.
To make this possible, data annotators teach machine learning models to identify crops, soil, pests, plant diseases, and even farming equipment. The system can pinpoint the exact location of a pest, enabling farmers to treat it.
In cases where the pest is untreatable, farmers can prevent it from spreading to healthy crops.
Soil monitoring is another example of how data annotation works in agriculture. Crops require specific soil conditions and nutrients for optimal growth. AI systems can evaluate the nutritional value of soil through data annotation, helping farmers prepare it for planting.
Do you want a more in-depth explanation of data annotation and artificial intelligence’s role in modern farming? Check out this LinkedIn article.
6. Finance
The finance industry handles a large amount of customer information and assets. To protect this data, a high level of security must be maintained to prevent risks such as cyberattacks and theft.
AI is used in the finance industry for fraud detection, market risk analysis, and credit risk assessment.
- Fraud Detection.
AI/ML models are trained to identify abnormal patterns, such as transactions that are not typical for a customer. Detecting potential fraudulent activities can help finance companies prevent losses. - Market Risk Analysis.
Identifying and avoiding potential market risks is crucial for finance companies. AI monitors economic indicators like stock prices to this end. - Credit Risk Assessment.
In this data annotation example, AI/ML models automatically detect and label a customer’s financial behavior and credit history. This allows financial companies to identify which customers are eligible for loans.
7. Robotics
One example of how data annotation works in robotics is service robots. These robots are commonly found in restaurants and help customers with tasks such as ordering and payment.
Medical robots are also commonly used. They assist medical professionals in caring for patients, from interacting with humans to helping with complex surgeries. Industrial robots are also used in factories, specifically for production tasks.
These examples of robotics work thanks to data annotation. The robots use cameras to locate the items they need to move or work on, enabling them to perform tasks efficiently.
8. Manufacturing
Manufacturing AI is becoming increasingly prevalent. It allows companies to reduce production costs, increase production speed, and ensure quality.
One excellent example of using manufacturing AI to reduce production costs is General Electric’s (GE) AI/ML model. The model reduces the logistics cost of installing wind turbines. By 2030, this technology is predicted to help the wind industry save up to $2.6 billion.
In addition to reducing costs, data annotation can also enhance production speed and quality. AI models can streamline manufacturing processes, enabling companies to produce products quickly without compromising quality. AI/ML models can quickly detect defects, making it easier for manufacturing companies to remove faulty products.
LinkedIn has an article discussing how data annotation is transforming the manufacturing industry.
9. Satellite Imagery
One of the top examples of data annotation tasks in satellite imagery is the Global Positioning System (GPS). GPS can provide accurate information and navigation of different locations, thanks to geospatial annotation that accurately labels their terrains, landmarks, and geographic coordinates.
Here are some of the industries that can benefit from geospatial annotation in satellite imagery:
- Logistics.
Geospatial annotation allows logistics companies to find the best transfer and distribution routes. - Forest Analysis.
Annotating images of forests helps AI/ML models understand how to manage them. - Disaster Management.
When natural disasters like floods and earthquakes occur, AI/ML models analyze the affected areas. This helps rescuers plan how to aid disaster victims.
10. Social Media Analysis
Social media platforms must manage and filter people’s posts. To do this, they use a type of data annotation called sentiment analysis.
With sentiment analysis, data annotation specialists label social media data as positive, negative, and neutral. In simpler terms, annotators train AI/ML models to understand the emotions conveyed in a social media post.
Brands are one of the top entities that benefit from sentiment analysis, as it lets them understand how people perceive a product, service, or the brand itself. This way, they can quickly address negative feedback or create new strategies based on how the public feels about them.
Various Industries Benefit From Data Annotation
Data annotation examples are available across various industries. This shows that the world is slowly leaning on artificial intelligence to improve efficiency.
However, data annotation efforts can only be successful if industries can ensure consistency and data quality. AI/ML models cannot perform their intended functions if they use poor-quality data. For this reason, industries utilizing AI systems need humans to train computer systems.
