Why is it Better to Outsource Data Annotation?

Data is crucial for industries today. Data is power, and with the advent of artificial intelligence, it has become even more important. Artificial intelligence operates on algorithms that are prepared with the help of data inputs by humans. As more industries become reliant on AI to perform tasks efficiently with minimal human error, information gathered and processed through data becomes important. Depending on the requirement of the operational usage of this data, enterprises choose to perform the task in-house or hire a data annotation service provider, to suit the need.

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What is Data Annotation?

To train a machine to perform like humans, we need to feed it with the information of respective data. The process to capture and encoding this data for understanding a machine is called data annotation. Through this process, any data which is captured is converted into machine language in the form of algorithms.

Depending on the type of data, annotations can be categorized into:

Image Annotation

Data of images is used to create a program to respond to its contents, to achieve required goals.

Video Annotation

Video footage is utilized in the form of annotated data to train a machine program and get needed responses.

Text Annotation

Machines can be trained to bring meaning, and define the context and intention, of textual data by annotating it in suitable language.

Data Annotation challenges

It requires efforts from highly skilled professionals to annotate data efficiently to make it usable in achieving desired machine outputs. Data should be properly structured and labeled before being input into the machine. Some of the common challenges faced by companies include

Skills and training

It is a tedious job to manage the human resources for data annotation as training professionals who will perform labeling and hiring skilled professionals who can supervise over requires time and money. There is a shortage of skilled employees in the field as it is relatively new. This leads many enterprises to hire data annotation companies.

Technology and infrastructure

Creating an infrastructure to support the labeling of data and to maintain the same needs budget. Any technical infrastructure involves – development, maintenance, and up-gradation cost. Companies that are not into core technical services often find this a financial liability resorting to outsourcing data annotation.

Supervision

Perfect data labeling is key to getting exact AI outputs. If data annotation misses the mark it will result in similar errors in AI as in humans. Therefore it is crucial to annotate data efficiently and to supervise these annotations companies require skilled administrative professionals. Hunting for that talent is a challenge, more so when it impacts productivity directly.

Finances

The data labeling department needs technology and infrastructure along with human resources. Setting up all this involves costs. Knowing the benefits of data annotation many industry leaders are reluctant to upgrade the systems to its usage because of these challenges. Engaging annotation companies is another option they can consider, though brainstorming on eligible data annotation service providers is necessary before jumping into action.

Time

It is another challenge, as once a process system is on the run, it gets difficult to introduce a new segment to it.

Usage of Data Annotation

With the increase in the utilization of AI to streamline operational flows, data annotation has grown in importance over time. As technology is being preferred over human intelligence in tasks that have a repetitive nature, AI can reduce the risk of human errors. Data annotation is significantly used in:

• Industrial Robots: Data labeling-based AIs have brought significant efficiency to industrial operations, specifically manufacturing. It is used in defect detection, random sorting, intelligent handling, network security, surveillance, etc. Annotation has reduced human errors and enhanced quality assurance for industries.
• Healthcare: If the Innovation of AI has helped any industry the most, it would be health and medicines. Research and development have also gained from the advent of artificial intelligence. Diagnosis, general surgeries, cosmetic surgeries, medicinal research, and biotechnology is using data annotation.
• Unmanned flying object: Autonomous aircraft, drones, and other commonly used AI-controlled flying objects utilize data annotation to set flight targets. Data labeling helps these define processes and set goals.
• Automatic Driving: Motor training institutes and machinery uses data annotation to impart lessons on driving.
• Retail industry: Vision-based inspections, quality control, inventory management, and eCommerce are using data annotation. The retail industry is filling gaps between sales, marketing, and production through AI.
• Security: Most common data annotation usage is happening around us in various security and surveillance systems. It helps in identifying trespassers, lawbreakers, and dubious activities, as well as identity verification and entry-exit controls.
• Agriculture: The labor of farmers has reduced significantly with the use of yield-based data annotations. It assists in irrigation, pest control, quality check, and controlling trespassing stray animals. The AI agricultural equipment has resulted in improved yields and better returns for farmers.
• Space photography: Cyclone predictions and other weather reporting is easier now with the help of data labeling. Also, astronomical calculations based on space images are mostly a job of programs fed into AI systems through structured data labeling.

Pros & Cons of In-house Data Annotation

The success of data annotation is rooted in the fact that efficient use of this technology can significantly contribute to increasing the productivity of a trade. But, introducing AI & data labeling is initiating a whole department – involving infrastructure, human resources, and finance. Let’s look at the positive and negative sides of an in-house data annotation.

Pros

• Security of data as it is not shared with any third party
• Better control over the process
• Control over costing

Cons

• Talent hunt and skill training is difficult for in-house team
• Financial burden of developing high-tech infrastructure and maintaining upgraded technology.
• Cost of human resource training and hiring
• Annotation might get stuck in internal bias
• Management of an extra department

Ultimate tips to find best data annotation services

By now you must have got clarity over the decision of creating an in-house team or whether to outsource data annotation. To help yourself in the selection of data annotation service provider, we have got some tips:

Take a quality test

For instance, if your washing machine starts acting on your command, and you have to instruct it to wash your woolen shirt, but you don’t know how to wash a woolen shirt so you instruct it to wash it like a cotton shirt. The result would be disastrous.
Similarly, the objective of your algorithm depends on the quality of the data annotation. If your annotation expert is not upto mark the result would be worthless. Thereby, it is suggested to take a quality test of your data annotation service provider. Most annotation companies will be happy to comply and do a sample task for you.

Examine security

Data security is of uttermost importance. When you outsource data annotation services examine the security certifications and procedures beforehand. The gray market of raw data is as huge as the legitimate market. Please be sure to check safety layers, protocols, study security clauses in work contracts and make an informed choice.

Do a speed check

Data annotation services for machine learning is a complex task which requires time bound highly skilled expertise. Your outsourcing partner must meet the deadline and quality both. Just like a quality test, analyze the time management skills before hiring a data annotation company.

That may also cause you to think that the in-house team could do better on speed metrics as it will be under supervision. But, at the same time it gets difficult to train internal workforce, scale-up or maintain quality. An annotation company can hire skilled experts as and when needed, on project basis.

Verify scaling up capabilities

Leading to the last tip to hire a perfect data annotation service provider, it is the scaling ability check. Your data annotation service provider shall be capable of scaling up the task responsibilities, as well as lowering it down as per your requirement.

To Conclude

Data labeling is a crucial job. Different scenarios have different annotation requirements, eg- an automatic training vehicle will operate on a different algorithm than a drone. This industry is highly dependent on manpower, currently annotation experts depend on tools to label datasets.

Whether to go in-house or outsource data annotation services depends on the task too. If you have a limited annotation requirement or you are very concerned about the privacy of your data, going in-house can be an option.

Large projects may require a lot of semantic segmentation, recruiting a team and managing it efficiently would be a daunting task. In such a case, it would be better to hire data annotation service providers that are experienced and skilled to achieve your goals.

SunTec India‘s Data Annotation

We have many years of experience in the field of data annotation, with clientele from different verticals of the industry. At SunTec India you can rest assured of the annotation quality as we use highly advanced technology and tools, along with a professionally skilled and experienced workforce. Some of the salient features of our data annotation services are:

• Customized services to meet your exact goals
• ISO 9001:2015 Certified for data quality
• ISO 27001:2022 Certified for Information security
• Scalable service
• Comprehensive label support
• Dedicated team and project manager
• High-quality of labeled data
• Multi-layer security check