Data and artificial intelligence (AI) are inseparable. It’s something every data engineer knows, but unfortunately, most organizations don’t.
According to Gartner Research Firm, around 60% of companies fail to realize how much bad data can cost their projects, resulting in annual losses of up to $15 million due to poor data quality1. Today, when the world is slowly progressing towards an AI-powered economy, industries are looking to partner with the best data annotation companies not only to avoid massive losses, but to stay ahead of the competition.
The projected value of the AI market is set to reach 10 billion USD in 20282, painting a marvelous picture of a future filled with self-driving cars, robot-assisted surgeries, and ridiculously intelligent chatbots.
AI’s ability to provide predictive information relevant to decision-making is one of the key factors contributing to the growth of data annotation services. Organizations are realizing the importance of relevant, accurately labeled, and updated datasets, resulting an increase in demand for the best data annotation services3.
Retail and E-commerce
In industries like retail and digital commerce, AI technologies is an indispensable tool for improving user experience and identifying opportunities to drive more value.
Banking, Finance, and Insurance
Data labeling has proven beneficial in streamlining fraud detection and compliance for financial companies. The technology helps in real-time documentation, verification, interaction, and flagging of customers.
Since the COVID-19 pandemic’s intense impact on the medical sector, the industry has been utilizing AI’s technologies to reorganize and streamline operations. AI offers three key benefits for healthcare: robotics, medical image analysis, and precision medicine.
Autonomous vehicles rely on a properly trained machine learning (ML) algorithm to operate and mitigate any safety concerns. It requires extensive image and video annotation, assessments, and mapping to reach optimal performance.
Augmented Reality (AR) and Virtual Reality (VR) are becoming increasingly popular. Social media companies are now looking to increase engagement in the virtual reality space using technologies driven by data labeling tools.
Investments will continue to pour in as more opportunities arise across industries. But before anything else, organizations must focus on the heart of AI: data. Programmatic code isn’t the driving force behind AI/ML models; it’s the data from which the model derives its training.
When training ML models, it’s important to remember: “Garbage in, garbage out.”
“Garbage,” or poorly labeled data, can seriously mess up an AI model’s results. Simply put, it’s impossible to expect outstanding results from an AI model if it’s only been trained on bad data. Needless to say, the machine can’t operate in the real world either. For example, in a 2021 project led by Andrew Ng with Stanford Health, the data quality in their test environment didn’t match the quality of the medical images captured in a real medical operation4. The AI model was ultimately deemed useless, putting millions of dollars and years of investment down the drain.
Building data annotation tools internally and getting a large amount of data requires significant investment and can be financially straining. That’s where the need for the best data annotation companies arises, as they already have the best practices, processes, and infrastructure in place to run a data annotation project of any size or complexity.
One of the goals of machine learning is to create applications that can replicate the way humans process patterns and behaviors and view the world. Data annotation is the process of labeling data in various formats such as text, images, or video to train ML models to do just that.
However, this process can be very time-consuming and financially straining. That’s why data annotation companies specialize in data labeling for machine learning and provide their services to companies looking to leverage the power of AI and ML.
The best data annotation companies employ experienced data annotators and have the best tools for collecting and labeling data for even the most complex tasks.
Labeling text data to help machines understand the meaning behind key phrases or words.
Image annotation is the most common way to create training datasets for computer vision. This process labels images into various categories, objects, etc. based on the project requirements.
Video annotation is also commonly used for computer vision. It’s one of the most labor-intensive forms of data annotation, building labeled video datasets on a frame-to-frame basis to help the ML model detect or identify moving objects.
Commonly used to train machine learning models like virtual assistants and chatbots. The process includes transcribing the audio to text and classifying acoustic and environmental audio datasets.
Outsourcing data annotation to a third-party vendor includes many vital benefits: reduced costs, access to a trained workforce, and industry expertise consultations.
These are a few critical factors to consider when looking for a data annotation company to invest in:
Tools and Technology
Choose a data annotation company that has the latest data annotation tools and technology in place to work on even the most complex projects efficiently.
Check if the company is internationally recognized, has the best data security practices in place, and has confidentiality agreements with their workforce to ensure the security of your project’s most sensitive data.
The best data annotation companies have a well-trained, scalable workforce for your project to ensure they’re able to meet the project demands such as timeline and quality of labeled data with the highest efficiency.
The best data annotation companies focus on evaluating your project needs first and then offer a quote that guarantees the best return on your investment.
With over 10 years of experience in data annotation and an average of 98% QA score in all data-related operations, TaskUs is one of the best data annotation companies in the industry.
One of our clients is a leading social media platform that partnered with Us to improve the accuracy, efficiency, and performance of their ML model’s text and image classification capabilities to ensure the integrity and security of the platform for all its users. Our agile and flexible solutions helped them to execute large-scale projects that cater to our partner’s specific data labeling needs.