Data Annotation & Labeling – Why does it matter?

January 13, 2022by Marktine Technology

Today, organizations are looking to future-proof their technology infrastructure through the adoption of AI technologies. In times when organizations across the industries are experiencing an increased demand for a better buying experience, Data Annotation &Labeling is emerging as a strategy to ensure that digital assets are created uniformly and in context to enable effective search and retrieval.


How do AI and ML work?

In the current industry scenario, where AI and ML are used across organizations to automate various processes and activities, we need to understand that data labeling is the heart of AI and ML systems. The process of data labeling is done through human intervention since these machines lack the decision-making capabilities that give organizations a major edge.

The data annotation process is the virtual training for machines to gain intelligence. Data labels are embedded with relevant information needed by the machine in order to come up with valuable conclusions.

This can be applied across industries such as retail, medical, finance, and e-commerce. With data annotation &labeling services, you can streamline your business processes and make better high-value decisions across industries with accuracy.


Let’s take a look at how AI & ML have added value to different sectors of businesses:

  • Document Data Extraction:
    Automating the data extraction process using computer vision models can significantly improve businesses in terms of cost savings, time savings, and accuracy. Computer vision-based models can effectively categorize the raw data and help the users in identifying and furnishing the relevant fields they are looking for ensuring superior accuracy. The extracted data can also be transformed into the desired format, e.g., text, XML, CSV, and stored as a file.
  • Easy Checkout:
    Scanning and labeling products with AI Computer Vision models powered by human-labelled data is critical for providing an accurate in-store checkout experience. Aiming to transform the in-store checkout experience, retailers and startups are combining computer vision and deep learning to automatically count in-store products, reduce transaction times, increase accuracy, mitigate data entry errors and improve the overall customer experience.
  • Content Moderation:
    Content moderation is applying human intelligence to make sense of large volumes of data. For example, any business with an online presence needs to monitor and moderate comments, reviews, posts and messages through online channels. With the current influx of personal and sensitive information being posted online at unprecedented rates, brands must rely on technology to help them moderate their content more efficiently, reduce costs and contribute to the overall brand image, as well as keep customers happy.
  • Property Inspection:
    AI-powered data annotation &labeling services can be applied in many fields, including home inspection and manufacturing of goods. A typical property inspection includes an examination of several different specifications. Specialists can scan for glaring errors such as holes in a wall, damaged doors, excessive rust, or if a tank has been installed properly. They also test for signal strength and whether antennas are within operational range.


Wrapping up:

It is essential to leverage the best AI practices for accelerating your core business processes at speed and scale. Digital technology today has enhanced the speed, accessibility and agility of businesses offering a 360-degree view of data assets. Going forward, pursuing the best AI practices can ensure customer satisfaction, secure customer data, and make business processes smarter by automating them.