Why Your Business Should Consider Implementing Data Labeling

Machine learning (ML) and artificial intelligence (AI) aren’t just buzzwords in the business world. They’re considered the future of commercial operations.

According to a survey, 48% of businesses are tapping machine learning, deep learning, and natural language processing to use big data sets for multiple business advantages. AI is said to have enhanced productivity by more than half for companies using it. 

In the US alone, the AI hardware sector is projected to reach USD$87.68 billion by 2026, whereas ML and deep learning will peak at USD$80 million by 2025.  

But what do these numbers mean? And where do businesses begin? Before making any ML or AI-related decisions, learning more about data labeling and how certain procedures can boost your business is crucial. Let’s take a closer look at this essential data preprocessing method.  

Understanding data labeling 

Data labeling is the process that enables any digital object to be tagged, identified, and processed by machine learning platforms. A label or tag describes the object and allows machines to become smarter. 

Without labels, machines and algorithms cannot identify and analyze objects. As an essential process in supervised machine learning and other platforms, data labeling helps algorithms learn by example and refine their processing capacities with time. 

Business benefits of data labeling

Labeled datasets train algorithms for different applications, including data classification, pattern detection, and outcome prediction. With proper labels, ML data processing can be successful and more reliable—making accurate data tagging indispensable to any company in the digital age. 

1- Data labeling enables AI- and ML-run customized business solutions 

As mentioned, proper data labeling can boost ML algorithms responsible for specialized business applications that improve products and services.

Generally speaking, data labeling can be used in objects, including images or videos, texts, or Light Detection and Ranging (LiDAR). Categorical applications for data labeling include computer vision and image classification, natural language processing (NLP), and audio processing. These applications may sound foreign to you, but they can impact your operation in various ways. 

Proper data labeling leads to enhanced AI and ML performance, currently being applied in numerous industrial applications, including autonomous vehicles’ image-detecting and classification capabilities. Additionally, data labeling has enabled AI models to activate facial recognition and temperature monitoring. Moreover, advanced health imaging devices can help doctors detect abnormal growth, like tumors and other biological anomalies.

As a predecessor to advanced applications relying on ML, AI, and deep learning, data tagging makes scanning, detection, tracking, and interpretation swift and accurate. 

2- Data labeling is key to improving AI- and ML-driven business processes 

Besides its numerous industrial applications that enhance business outputs, accurate data labeling can improve corporate functions. Data labeling can feed high-quality data to any ML- and AI-driven application. While these projects involve high costs, companies can ultimately save operational time and costs by increasing precision and efficiency.

Increasing precision and productivity 

High-quality labeled data makes machines work with more precision. Take the case of a pizza-making robot relying on artificial intelligence, images, and audio inputs when taking orders and making pizza. Financial institutions can also use data labeling to make ML- and AI-run systems better at fraud detection. Manufacturing companies can spot low-quality products and stay on top of equipment maintenance, thanks to data labeling that trains these platforms more accurately.   

Enhancing customer service and marketing strategies 

With proper data labeling, chatbots can be trained to address common customer complaints and refer the issue to call center agents for complex problems. Data tagging can boost machine-learning platforms for more efficient marketing strategies. Algorithms can be trained to excel at enhancing marketing campaign personalization and user-generated content and analyzing customer reviews, all proven more effective than conventional advertising techniques. 

All of these business processes require high-quality data labeling to generate better outcomes. 

3- Data labeling helps systems predict outcomes with better accuracy 

Business intelligence is an important corporate activity that involves gathering and evaluating business data. It specifically covers data mining, performance analysis, benchmarking, and analytics. With such functionalities, companies can pull out accurate reports, measure their performances, and predict market outcomes. These, in turn, help executives make more informed decisions.      

Precise data labeling can make ML- and AI-reliant platforms predict more accurately. As a result, firms are able to reduce expenses and optimize resources without sacrificing the quality and quantity of their business output. 

Concluding thoughts 

When done right, data labeling enhances the quality of machine learning applications, which can be customized based on specific business needs. With better output, businesses can expect operational efficiencies from the machines and their staff, reducing overhead costs while improving their products and services. 

In addition to customization, data labeling can help ML, AI, and deep learning systems scale, helping businesses identify and pursue ambitious income-generating opportunities. 

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