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Data Labelling

Data labeling is the process of reviewing and placing relevant, functional labels on data to gather insights. 

Data Labelling Services

What is Data Labelling?

Data labelling is the act of manually reviewing and placing relevant and useful labels on data. Data, in this case, can be any sort, including text, video, images, and audio. A data label, thus, is an identifying factor that describes what an item is. Data labelling has become an integral part of many work processes in businesses that use big data and artificial intelligence. Data labelling takes a series of unlabeled data items and augments each item with highly relevant labels based on categories. This makes possible the efficient extraction of useful information from a large amount of unstructured data. 

The challenge for most companies dealing with large amounts of unstructured data is how to make them manageable and meaningful for decision making. This calls for expertise in the area of Computer Vision and Audio Processing technologies along with expertise in the areas of Databases, Information Lifecycle Management, Knowledge Warehouse Systems, and Data Labelling.

With the combined expertise of these, Foiwe can provide the necessary manpower that can work on your technical tools to effectively handling large data labeling tasks. 

Applications and Capabilities

The most popular applications of this technology are for recognizing objects, faces, and action verbs in live situations. 

Applications

  • Online Security applications
  • AI Systems
  • Social Platforms
  • Audio and Video Applications
  • Ecommerce platforms

Capabilities

  • Recognizing real-world objects in digital data plays a large part in facilitating a secure software or web environment.
  • Capable to handle large data volumes
  • Multilingual team
  • Experienced Staffs for greater output

Benefits of Data Labelling

Implementing AI technology helps businesses capitalize on decision-making and insights gathered by processing data, and Data Labelling plays a vital role in that process. 

Data Derivation
Labelling helps in the efficient retrieval of data required by resources for further processing.
Structuring the Unstructured
Data Labelling undertakes data enrichment by matching references to exact definitions and details of data.
Data Preparation
A critical step in efficient prepping of data is Data Labelling, as it identifies the type of data being needed for the dataset.

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