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Data Labeling refers to the tags or labels allotted to differentiate and add significance to the content to develop the AI models. The data labels indicate whether the content is a text, image, video, audio, etc. The futuristic technology; Artificial Intelligence (AI) and Machine Learning (ML), depends on the data scientists and engineers who spend immense time on data preparation rather than the actual model itself. Various approaches and best practices must be considered for an efficient data labeling process. 


Good knowledge about data science, computer, and domain skills are required to label the data. Companies encounter numerous problems related to data Labeling. Today, we will talk about a few challenges and how to overcome them.

1. Team Management

Data Labeling requires a large workforce of skilled data scientists and engineers to produce and maintain high-quality work. The generation of massive data for product models requires a large team to manage the data and troubleshoot the errors during the data-labeling process. Data errors can include:
  • training the new members for various tasks
  • difficulty to understand the domain and the specific task or subject 
  • assigning the tasks appropriately to carry out the work flawlessly
  • technical issues
  • ensuring easy communication and collaboration with data and team members
  • quality control and data validation
  • cultural, geographic, and language barriers between the team can hinder the work process.

2. Data Management

The Machine Learning model has to be trained with the right data inputs. Therefore, producing accurate data labels with consistency is of utmost importance to ensure correct predictions by the AI. Subjective and objective dataset qualities can create issues.

Subjective data issues: personal biases and socio-cultural differences may also lead to communication problems within the team. The change of team members may also create issues for the newcomers concerning data.

Objective data issues: the inaccuracies due to data illiteracy may lead to the generation of faulty data tags or labels in machine learning that will be misinterpreted by the AI.

3. Right Tools and Strategies

The combination of the software and trained data labelers can deepen the ML process. Some leading data (images) Labeling software include Semantic segmentation, Polygon, Keypoint, and Bounding box annotation. It is vital to ensure that whichever platforms are bought from the agencies, meet all your data requirements. If not, the quality of the data can be impacted, leading to inaccuracies. Also, there are quite a few open-source tools for data labeling, data wrangling, etc.

4. Ensuring a Cost-effective Process

Companies are now building their in-house tools to ensure an efficient data Labeling process based on the company’s needs. But, in-house tools or software increases the cost and delays the go-to-market The lack of transparency can affect the funding and sponsor of the data Labeling projects.

5. Conceding with Data Security Standards

Working with AI and Machine Learning requires meeting certain data security standards as stated by General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Data Processing Agreement (DPA). Data confidentiality compliance and security regulations are increasing globally. Data labeling companies are bound to comply with internal data security and privacy standards. The failure to meet the data security regulations may hinder your company’s work and data Labeling process.  


No matter how many challenges come across the data Labeling process, with the right skill and knowledge, the problems can be rectified. Certain advantages of using the data Labeling process are:
  • Data Consistency

    In-house tools can produce consistent data results and long-term reliability.

  • Feedback system

    An annotation feedback loop ensures constant performance monitoring and improvement. 

  • Auto Data Labeling

    Automatic data labeling is done using various programmatic algorithms that are task-specific or object-specific in ML algorithms. The process reduces the cost and time and uses software to accomplish the task. 

  • Deep Learning
    Deep learning, along with ML algorithms can analyze and label unstructured data with ease. Deep learning can excel in data labeling over a relatively short period of time and prevents unnecessary costs.
Most of the advanced technologies today rely on the accuracy of data and its labeling. Inaccurate data labeling will mislead the ML models and might cause significantly wrong results. It is crucial for every ML product company to invest in data labeling to gain economic benefits.

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