Complete Guide to Data Annotation and its Use for Businesses

Data is an essential commodity for modern-day businesses. The reliance of artificial intelligence for data has made it an even more critical commodity for various industries. However, there is a great deal of difference between regular enterprise data management and the data processing involved in AI algorithms. AI systems don’t just require clean data; they need well-labelled data that can be used to establish context and help AI algorithms learn. That can be achieved through data annotation.

Read on to learn all about data annotation and its various types. We’ll also discuss the benefits you can get if you outsource text annotation services, image annotation, or video annotation services to an experienced third-party company.


What Is Data Annotation?

Machines require data to perform tasks or understand concepts as humans do. This data needs to be clean, coherent and annotated with the right labels. Data annotation is the process of attaching tags or labels to data (like images, video, audio, or text), so a machine can recognize it.

AI/ML algorithms use the training sets created via data annotation to teach themselves and develop decision-making capabilities.

Different types of data annotations are as follows.

Image Annotation

In this process, the image content is tagged with the right labels to achieve the desired objectives. Every element in an image is annotated using various techniques (lines, polygons, or boxes) and labelled for recognition.

Video Annotation

Different data elements in a video are labelled frame-by-frame to create a training dataset for machine learning purposes.

Text Annotation

This type of annotation is performed on textual data, where every word is tagged with the intended meaning and context. The resultant datasets are used so that the machine learning model can easily understand communication.

Challenges of Data Annotation

Data annotation depends on many things working properly at once. For instance, the sources of data collection should be genuine, the collected data should be accurate, and it should be free of biases (as much as possible). Moreover, the structure and labelling should be suitable and comprehensible for data annotators.

Apart from these, other challenges that generally occur during the process are as follows:

Requires Advanced Technology

Data annotation is a complex process. Advanced tools and software are needed to achieve its objectives efficiently. You may find managing such infrastructure challenging, especially if your business does not have a well-established technical background setup. Even if it does, the cost of data annotation tools can go quite high. You will have to invest in setting up infrastructure for data annotation operations involving software, hardware, maintenance, and upgradation.

Scarcity of Trained Labor

Data annotation is a comparatively new skill. It needs trained professionals who understand the process, can produce good results, and ensure time effectiveness. Therefore finding and hiring trained data annotation experts can be tough (and certainly expensive).

Regular Monitoring

You need perfectly labelled data to achieve desired objectives in AI applications. Failing to do so will lead to a flaw in the AI’s thinking capability, further degrading its decision-making capacity. Therefore, close monitoring and regular audits are essential parts of the process to address this concern. although finding eligible administrative professionals for supervisory tasks is tough.

Maintaining Cost Efficiency

Setting up the required infrastructure with advanced technology and hiring staff in-house needs investment. Its maintenance and upgradation are other challenges. It is the reason industry leaders are reluctant to involve this technology in operations, though they understand the advantages. Here, hiring image annotation services can be a solution to meet the challenge of financial burden.

Data Annotation Usages

As more businesses opt for AI-based operations, the importance of data annotation has also increased over time. Annotation has helped businesses reduce dependence on manual labor for repetitive tasks through AI. It has helped in better organization of the workforce.

The following industries find this technology useful:

Robotics

Artificial intelligence and robotics have proved to be highly useful in industrial operations like manufacturing, quality control, security, and random sorting. It has helped in increasing productivity and reducing labor costs. Industries like the automobile sector use image annotation services to train robots for defect detection and quality maintenance.

Medical Services

Healthcare organizations use image annotation services in diagnosis and operations to better treat patients. It helps in scanning, radio-imaging patients, and biotechnological research. Procedures like general or cosmetic surgeries also utilize video and image annotation.

Unmanned flying objects

Video annotation services assist in training flying objects like drones and autonomous aircraft to achieve desired objectives. They are widely used for delivery and surveillance.

Automated Driving

Traffic images and signs help AI-based vehicles drive smoothly through the city roads. Image annotation services and labelling of texts on signboards help autonomous vehicles set guided routes.

Retail industry

The retail industry widely utilizes data annotation to build AI models that can handle inventory management, product inspection, and quality checks. The repetitive tasks in store outlets and eCommerce shops are assigned to AI systems for better service and cost control.

Surveillance

Industrial security and access control systems extensively use image and video annotation services to ensure safety on the premises. It is one of the most commonly found uses of data annotation technology. Even the city traffic administration utilizes it for better management of road transport facilities.

Research

Data annotation helps in weather forecast and natural disaster prediction. It is also helpful in astronomical research via understanding satellite images.

Advantages and Disadvantages of In-house Data Annotation

The success of this technology is mainly because of its positive effects on productivity, efficiency, and operational cost control. At the same time, to utilize data annotation technology, you need to set up a specialized department with respective infrastructure, staff, and technology.

Let’s see the advantages and disadvantages of in-house data annotation.

Pros

  • Your data is more secure as a third party does not administer it.
  • You can control the process as desired.
  • You can manage the capital investment.

Cons

  • Finding capable experts and training them is challenging.
  • Setting up advanced technology workstations and infrastructure requires investment.
  • Have to bear the costs of hiring and training staff.
  • Achieving unbiased annotation is sometimes challenging in-house.
  • The management has to bear the load of one more department.

Outsourcing data annotation is a popular solution to these problems. You can easily find experienced providers of image annotation services, video annotation services, and text annotation services. Although most such service providers offer remote support, they are usually cost-effective.

How to Find Good Data Annotation Service Providers

Finding the most suitable data annotation service provider is essential, as any carelessness might result in inefficient operations and a waste of data, capital, and time. Here are a few tips to help you identify a capable partner to handle your text, image, or video annotation services.

Analyze Quality

High quality is a major requirement for processes as sensitive as data annotation. To find the perfect outsourcing partner, ask for a free trial and check their quality. If your data annotation service provider delivers below the standard expectations, it will result in AI algorithms missing their objectives. A free trial will give you insights into their performance and help you judge their capabilities.

Data Protection

As discussed earlier, data security is important. You should be able to trust the service provider with sensitive data; any leakage can be disastrous for your business. Therefore, check the data security measures followed by the annotation service provider company, review their certifications, and analyze their firewall and encryption methods.

Turnaround Time

Data annotation requires effort and skills. Due to the complexity of the process, it is obvious that it takes time to process. Before hiring your outsourcing partner, analyze their task turnaround time. For instance, if you engage a video annotation service for labelling security footage, that partner agency should be able to deliver high-quality output in time. It is vital to note that delays can hamper your security management.

Scaling Ability

Depending on your expansion plan and budget, it is essential that your outsourcing partner can keep up with the changing requirements.

Conclusion

Data labelling is a complex procedure, and it requires technical expertise. Different machines have different annotation needs. For instance, AI-powered vehicles will need different data than a drone. In such a scenario, experience, skills, and advanced technology play a crucial role in the ability to perform effective annotation. Outsourcing image, video, or text annotation services to trained professionals can help efficiently work with AI and ML models.

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