The benefits of data annotation outsourcing
Training data is unquestionably one of the most tedious tasks in machine learning. Although human intervention is frequently necessary for tasks such as labelling unstructured data to prepare the training data for your model, it is laborious and time-consuming, making it underwhelming for qualified and well-paid data scientists. This is why many businesses outsource their data annotation projects to take advantage of affordable labour on a large scale. Research indicates that by 2028, the global market for data annotation will be worth $8.22 billion.
Despite the widespread acceptance data annotation of outsourcing, many businesses still prefer in-house experts to annotate their data. While the motivation behind this step is to reduce costs associated with outsourcing data annotation projects, people need to pay more attention to several factors and touchpoints that eventually cost them more. Many stakeholders believe that choosing internal data annotation modules will enable them to cut costs and finish AI development projects on a reasonable budget. But that's when costs start to accumulate. These choices force managers to suffer losses for a variety of reasons, such as inadequate datasets or data generation touchpoints, a lack of pertinent data, an abundance of unstructured data, overhead costs for team training on data annotation, the need to rent or buy annotation software, and more.
Here's why data annotation outsourcing makes sense
Although hiring a service provider to label and annotate your company's data may initially seem unnecessary, it will ultimately be more cost- and time-effective. If you're still debating whether to hire data annotation vendors, here are some advantages of data annotation outsourcing.
Data annotators are qualified experts with the necessary domain knowledge for their jobs. They know the best annotation techniques for various data types, the best methods for annotating bulk data, the best ways to clean unstructured data, and much more. Data annotators will ensure that the final data you receive is flawless and can be directly fed into your AI model for training.
Scalability of data
The more you train your model, the more intelligent it gets. Never assume that you won't ever require additional data volumes. The smooth operation of your AI development process depends on scalability, which cannot be attained solely by your in-house experts. Only seasoned data annotators can meet changing demands and supply reliable dataset volumes.
Unbiased data annotation
When internal teams annotate company data, there is scope for bias. Every single employee or team member may share beliefs to some degree, depending on the protocols, processes, methodologies, work culture, and other factors. When such biases seep into a machine learning model, it delivers results that are not as objective as they should be. Bias could result in a negative reputation for your company. Given that training datasets are one of the first places bias appear, it is ideal to delegate the task of mitigating bias to data annotators, who then provide accurate and diverse data.
An AI model is only as good as the training data fed to it. Because of this, when you input bad data, it produces inaccurate or irrelevant results. When you use internal sources to create your datasets, you will likely find irrelevant, inaccurate, or inadequate datasets. Your internal data touchpoints are dynamic; basing the preparation of training data on them will only make your AI model less robust.
Moreover, the members of your team could be annotating data inappropriately. Factors such as extensive bounding boxes, incorrect colour codes, and others could cause machines to make unintentional assumptions and learn inaccurate things. Data annotators excel at producing superior-quality datasets. They can identify inaccurate annotations and know how to gather the highest standard data for your business.
For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.
How can Infosys BPM help?
At Infosys BPM, we work with clients' data science teams to create high-quality training data for AI at scale using a platform plus a human-in-the-loop service model, which frees up the teams' time and resources to work on strategic objectives like developing and enhancing the AI model itself.