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Why use crowdsourcing to perform your digital tasks?

What is crowdsourcing?

The crowdsourcing you have chosen to classify your dataset is known as the labeling crowdsourcing. When you employ a private crowdsourcing, you also establish work teams, which are collections of your crowdsourcing members who are tasked with carrying out particular tasks. Each task can have one or more work teams assigned to it depending on the project requirements. 

For which use cases should you ask for crowdsourcing?

Lane and Parking Area Detection: crowdsourcing can provide training datasets for various computer vision tasks, including lane and parking area detection. The labeled data can be used to teach autonomous vehicles to identify lanes and lane markers, as well as determine the drivable area and parking space.

Object recognition: by creating training datasets, autonomous vehicle models can learn to accurately identify surrounding objects and obstacles.

Semaphore analysis: another area where crowdsourcing can be useful. Labelers can use bounding boxes to annotate traffic signals and signs, making it easier for autonomous vehicle models to recognize these signals and navigate accordingly.

Retail establishments: crowdsourcing can be used for product recognition. Data labelers can provide pixel-level precise product annotation services, which can help improve product search accuracy and enhance the customer experience.

Pros and cos using crowdsourcing to annotate your data vs in-house?

In house (pros)

Privacy: When your labeling is done in-house, you have complete control over the entire process and therefore avoid the risk of having confidential business information exposed to a third party. You can be certain that your contractor will not sell the labeled data to a competitor.

Quality control: Strong quality control is an advantage of doing your labeling in-house, as you can supervise the entire project's execution based on your preferred quality parameters.

Consistency: A self-developed, consistent annotation procedure can produce long-term dependability and success. It also creates a feedback mechanism that continuously promotes best practices in the collection of data for use in AI or ML models, which is something that all businesses eventually need.

Crowdsourcing (pros) 

Manage high volume data labeling: Crowdsourcing can handle the challenge of correctly classifying and annotating thousands of data sets, which is a time-consuming process. Moreover, the volume of data required for an AI project is usually project-specific, and an increase in demand might extend the deadlines for your in-house teams' milestones. By engaging additional teams, the overall productivity can be improved.

Eliminates internal bias: By using a hand-selected annotation crowdsourcing, you can ensure trustworthy quality control without any internal bias that may have developed by the internal team within a company.

Economical: Outsourcing the labeling of data to crowdsourcing is more cost-effective than doing it in-house since external labelers can annotate a lot of data quickly and precisely in a variety of formats.

Training: With a group of knowledgeable crowdsourcing data labelers working together on your task, you can be confident that it will be completed quickly and to your standards. These experts can improve your data labeling for ML and AI applications by building on their past knowledge of different data sets and honing their data labeling abilities.

24/7 availability: You will have access to a highly accessible, quickly deployable, and 24/7 global crowdsourcing annotation workforce.

How to choose the right crowdsourcing for your digital project? 

Size: You need to consider the scope of your project, how large or small it is, as this will affect the number of people you'll need on your crowdsourcing team. Essentially, the larger the scope of the work, the larger the team required.

Diversity: Crowdsourcing teams with greater diversity are more efficient and perform better. A diverse workforce offers a range of expertise, experience, and working styles that can enhance problem-solving skills and increase output.

Language: It's important to ensure that the crowdsourcing team you hire understands the same languages to avoid any communication errors or ambiguity that could impact the quality of the project.

Location: Finding and retaining good employees is difficult, and many are quite specific about where they work to achieve the ideal work-life balance. As much as possible, you should be able to manage remote work while taking into account different time zones.

Academic background: A strong academic background demonstrates that the crowdsourcing candidate has the necessary level of expertise and suggests that they will be able to carry out the required tasks. An annotator who has knowledge of the industry of the company that wishes to annotate is also a plus.

How to choose the best crowdsourcing provider for your project?

The decision to outsource or crowdsource your data labeling process depends on various factors, including the phase of your business and the amount of control you want over the process.

While in-house labeling is ideal, it may not always be feasible, and outsourcing or crowdsourcing becomes necessary. Crowdsourcing may seem attractive due to its cost-effectiveness, but it can be time-consuming to oversee quality control.
On the other hand, data labeling service companies such as Isahit have qualified teams that can handle the entire process, allowing you to focus on other internal initiatives while ensuring quality control.

Outsourcing your data labeling needs to such companies guarantees quality and may even cost less than you expect.

How much does it cost to crowdsource?

Outsourcing data annotation services can offer cost savings to businesses compared to building an internal team. Developing an internal team requires investments in infrastructure, technology, and staff salaries, all of which can be expensive. On the other hand, outsourcing data annotation services provides businesses with immediate access to a skilled team of experts who can quickly generate high-quality datasets that meet their specific needs.

By understanding the client's algorithm model, the outsourced team can produce relevant datasets for future queries or requirements. Additionally, outsourcing companies provide constant availability to datasets that can be used to train ML algorithms, allowing computers to identify the entities within the presented digital content more accurately. Overall, outsourcing data annotation services can be a cost-effective and efficient solution for businesses that need high-quality datasets for their AI or ML projects.

When is it time to use crowdsourcing to label your data?

It is time to use crowdsourcing to label your data when:

  1. In-house costs are unsustainable and cannot scale: Labeling data internally can be expensive due to high worker pay in advanced economies. As datasets grow larger, internal labeling becomes increasingly expensive and impractical.
  2. Difficulty recruiting and training labelers: If your internal labeling team is insufficient or has decreased in size, hiring and training new labelers can be challenging. New personnel require training to produce high-quality labels.

Why choose isahit to find the best crowdsourcing for your projects?

Ethical responsibility: You can rely on Isahit to provide a secure environment free of data security breaches. Since systems with inadequate encryption algorithms are vulnerable to hackers, we are a corporation that prioritizes data protection. Our culture also supports a broad representation in machine learning, which strengthens the objectivity and ethics of your AI model.

Workforce diversity: Isahit boast a very diverse workforce as we hire staff from various countries around the world and are especially particular about bringing employment to women in developing countries, thereby elevating their income and improving their standard of living.

Workforce management platform: Here at Isahit, we have the ideal workforce management platform to assist you in developing the best workflow for your business. This allows us to cut labor expenditures dramatically without sacrificing performance. In reality, the efficient administration of our staff encourages people to produce their best work.

Experience: Many companies undervalue the knowledge or abilities required to provide data labeling services because they believe this is a straightforward task. To avoid human error, which is common but can accumulate and have serious long-term effects, this talent necessitates accuracy and close attention to detail. Due to their lack of adequate resources and equipment to properly label your data, inexperienced providers may potentially incur expensive delays. You don't have to worry about this with us because with Isahit, you can be confident that your project is in good hands because our staff at Isahit is highly qualified and experienced in data labeling. We have also worked with a variety of clients over the years and have the results to show for it.

Quality: Quality assurance is a vital aspect of contracting your data labeling. You can be confident that your data is in excellent hands because all of our employees at Isahit are competent, well-trained, and correctly integrated in the area that your data services. Our staff can adapt rapidly to your requests for workflow adjustments, be open and effective in communicating with you through a tight feedback loop.‍


Nowadays, many organizations are looking to outsource their data labeling needs as it has become a popular trend and a wise business decision. It is also likely that outsourcing will continue to be a significant factor in the way many businesses operate in the future.

Isahit offers a variety of data labeling services to help improve the performance of your machine learning models. We have gained the trust of some of the world's largest and fastest-growing brands as a partner. Our extensive experience in data labeling enables us to support the projects of our international clients who rely on human-annotated training data.

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