Advantages of Data Quality Measurement for Tags and Labeling

Data is an integral part of any aspect of machine learning. Frameworks and models may be programmed to aid humans in a variety of ways. But all of these algorithms require high-quality annotated data. With such data, a system learns by optimizing its own settings and utilizing insights to deliver useful results.

According to Forbes, in the recent 5 years, approximately 97.2% of businesses are investing in both big data and development of AI to accelerate their growth and income. However, in the lack of quality data, the optimization of AI algorithms is nowhere near ideal. As the conclusions produced by it are beyond trustworthy. Regardless of how powerful an AI model is, if the training data is of poor quality, the time and the funds invested in developing it would be squandered.

The quality of data is defined not just by its source, but also by how the data is labeled and the quality of that process. In machine learning, the quality of data annotations is an important part of the data pipeline. All subsequent actions are heavily dependent on this one. In this article, we’ll talk about data quality and its measurement for data annotation to achieve the most accurate model training outcomes.
Why Do We Need Quality Data?

Accurate training dataset is essential when working machine learning projects. Companies which choose to build models with low-quality data discover that performance declines as a result. Inadequately labeled data may be not only inconvenient but also expensive, since algorithms must be redeveloped, retrenched, and tested again.

In certain circumstances, the outcomes of decisions made with poorly trained machine learning models can be disastrous. For example, we have no right to allow errors in the algorithm that distinguishes cancer cells from normal ones in medical micrographs. That’s why the quality data labeling process is a cornerstone for any AI-related initiative.

Although it normally becomes apparent later in the model training process, the tagging accuracy must be tackled right away. Maximum data quality must be maintained throughout the labeling process, which requires regularity. Read on to find out about the importance of data annotations, and why it has to be of the highest quality.

The Value of Data Annotation for Reliable Outcomes
Given how much unstructured data exists in the form of text, photos, videos, and audios, data labeling is particularly important. According to Gartner’s recent report, 163 zettabytes of data will be generated by 2025, and around 80% of it will be unstructured.

The majority of models are now trained via supervised learning methods, which uses human-annotated data to provide training instances. Accurate tagging and data processing decrease human involvement, while enhancing effectiveness and precision. It also assists in saving time and costs.

Data tagging is a tool you may employ to raise the excellence of your output. You may use it, for instance, to ensure that the product you’re developing is accurate and free of flaws or errors that could create issues when it’s released. Below, you can find the best practices on how to measure the quality of your data annotation process.

Best Ways to Measure Your Data Quality for Annotation
There are different ways for businesses to evaluate the accuracy of their data. You should start by defining labeling criteria to determine the appropriate tagging of the provided data. So, as you’ve collected your data and are ready to start with the annotation process, how to check if the data is of good quality?

1. Benchmarks
   Benchmarks or golden standards aid in determining whether a collection of labels from an individual or a group fits the approved standard created by experts or computer scientists. Because benchmarks don’t need much effort, they are typically the cheapest QA alternative. Benchmarks can serve as a helpful point of comparison as you seek to assess the quality of your product throughout the process.
2. Subsampling
   Subsampling is a typical statistical strategy for figuring out how data is distributed. It entails choosing a random piece of data and carefully examining it to look for any inaccuracies. It may be possible to identify areas where mistakes are more likely to happen if the sample is random and relevant.
3. Consensus
   The degree of agreement among several human or machine annotators is measured by consensus. Simply divide the amount of matching tags by the overall number of tags to arrive at a consensus score. The objective is to get an agreement on each element. Consensus can be achieved automatically or by allocating a specific number of evaluators to each piece of data.
4. Cronbach’s alpha test
   A method called Cronbach’s alpha test is applied to examine the coherence or average correlation of sample points in a dataset. The results based on the features of the investigation (e.g., homogeneity) can make it easier to rapidly determine the tags’ credibility.
5. Review
   This approach, created to measure the quality of data, is based on a labeling correctness check by a subject specialist. Most reviews are carried out by inspecting a selected amount of labels, however other initiatives examine all tags in a given dataset. The review method allows businesses to quickly assess quality with total transparency and traceability on data integrity. Companies as well have an option to receive a detailed report and give data instructors immediate feedback.

Remember that the likelihood of creating a successful model the very first time rises with close supervision of the validity of the training dataset. There’s also an option to leave the data labeling process to expert vendors that provide data annotation service to meet your project needs.

Summary
The first step in creating a compelling AI solution is getting access to solid sets of data. Make sure you start thinking about data quality control right away. You may position your team to perform well by developing a successful quality check procedure and putting it into practice.
In the field of data annotation it’s feasible to cooperate with pros (aka data labeling companies) to save time, money and achieve the top quality for your projects. In summary, making such an investment will definitely benefit you in the long run.