Data Labeling Mistakes

Top 5 Data Labeling Mistakes that Are Bringing Down AI Efficiency

In a world where business enterprises are jostling against each other to be the first to transform their business practices by applying artificial intelligence solutions, data labeling seems to be the one task everyone starts tripping on. Perhaps, that’s because the quality of data you are training your AI models on determines their accuracy and success.

Data labeling or data annotation is never a one-off event. It is a continuous process. There is no pivotal point where you might think you’ve done enough training or that your AI models are accurate in achieving results.

But, where does the AI’s promise of exploiting new opportunities go wrong? Sometimes during the data labeling process.

One of the major pain points of businesses incorporating AI solutions is data annotation. So let’s take a look at the top 5 Data labeling mistakes to avoid.

Top 5 Data Labeling Mistakes to Avoid

  1. Not Collecting Enough Data for the Project

    Data is essential, but it should be relevant to your project goals. For the model to throw up accurate results, the data it is trained on should be labeled, quality checked to ensure accuracy.

    If you want to develop a working, reliable AI solution, you have to feed it large quantities of high-quality, relevant data. And, you have to constantly feed this data to your machine learning models so that they can understand and correlate various pieces of information you provide.

    Evidently, the larger the data set you use, the better the predictions will be.

    One pitfall in the data labeling process is gathering very little data for less common variables. When you label images based on one commonly available variable in the raw documents, you are not training your deep learning AI model on other less-common variables.

    Deep learning models demand thousands of data pieces for the model to perform reasonably well. For example, when training an AI-based robotic arm to maneuver complex machinery, every slight variation in the job could require another batch of training data set. But, gathering such data can be expensive and sometimes downright impossible, and difficult to annotate for any business.

  2. Not Validating Data Quality

    While having data is one thing, it is also vital to validate the data sets you use to ensure they are consistent of high quality. However, businesses find it challenging to acquire quality data sets. In general, there are two basic types of data sets – subjective and objective.

    Not Validating Data Quality When labeling data sets, the labeler’s subjective truth comes into play. For instance, their experience, language, cultural interpretations, geography, and more can impact their interpretation of data. Invariably, each labeler will provide a different answer based on their own biases. But subjective data doesn’t have a ‘right or wrong answer – that’s why the workforce needs to have clear standards and guidelines when labeling images and other data.

    The challenge presented by objective data is the risk of the labeler not having the domain experience or knowledge to identify the correct answers.  It is impossible to do away with human errors completely, so it becomes vital to have standards and a closed-loop feedback method.

  1. Not Focusing on Workforce Management

    Machine learning models depend on large data sets of different types so that every scenario is catered for. However, successful image annotation comes with its own set of workforce management challenges.

    One major issue is managing a vast workforce that can manually process sizable unstructured data sets. The next is maintaining high-quality standards across the workforce. Many issues might crop during data annotation projects.

    Some are:

    • The need to train new labelers on using annotation tools
    • Documenting instructions in the codebook
    • Ensuring the codebook is followed by all the team members
    • Defining the workflow – allocating who does what based on their capabilities
    • Cross-checking and resolving technical issues
    • Ensuring quality and validation of data sets
    • Providing for smooth collaboration between labeler teams
    • Minimizing labeler bias

    To make sure you sail through this challenge, you should enhance your workforce management skills and capabilities.

  2. Not Selecting the Right Data labeling tools

    The data annotation tools market size was over $1 billion in 2020, and this number is expected to grow at more than 30% CAGR by 2027. The tremendous growth in data labeling tools is that it transforms the outcome of AI and machine learning.

    The tooling techniques used vary from one data set to another. We have noticed that most organizations begin the deep learning process by focusing on developing in-house labeling tools. But very soon, they realize that as the annotation needs start growing, their tools cannot keep pace. Besides, developing in-house tools is expensive, time-consuming, and practically unnecessary.

    Instead of going the conservative way of manual labeling or investing in developing custom labeling tools, purchasing devices from a third party is smart. With this method, all you have to do is select the right tool based on your need, the services provided, and scalability.

  3. Not Complying with the Data Security Guidelines

    Data security compliance will see a significant surge soon as more companies gather large sets of unstructured data. CCPA, DPA, and GDPR are some of the international data security compliance standards used by enterprises.

    Not Complying With The Data Security Guidelines The push for security compliance is gaining acceptance because when it comes to labeling unstructured data, there are instances of personal data present on the images. Besides protecting the privacy of the subjects, it is also vital to ensure the data is secured. The enterprises have to make sure the workers, without security clearance, do not have access to these data sets and cannot transfer or tamper with them in any form.

    Security compliance becomes a central pain point when it comes to outsourcing labeling tasks to third-party providers. Data security increases the complexity of the project, and labeling service providers have to comply with the regulations of the business.

So, is your next big AI project waiting for the right data labeling service?

We believe the success of any AI project depends on the data sets we feed into the machine learning algorithm. And, if the AI project is expected to throw up accurate results and predictions, data annotation and labeling are of paramount importance. By outsourcing your data annotation tasks, we assure you that you can efficiently resolve these challenges.

With our focus on consistently maintaining high-quality data sets, offering closed-loop feedback, and managing the workforce effectively, you will be able to deliver top-notch AI projects that bring in a higher level of accuracy.

[Also Read: In-House or Outsourced Data Annotation – Which Gives Better AI Results?]

Social Share