Automated vs. Manual Data Labeling – Evaluating Pros and Cons

According to a study by Tractica, the Artificial Intelligence (AI) market is expected to grow by over $100 billion by 2025. From self-driving cars to smart home assistants, customers are increasingly demanding products that run on AI technology.

Moreover, AI technologies are becoming more accurate, because they have been fed and trained on carefully labeled data. Unfortunately, a recent report published by Cognylitica confirms that data wrangling consumes over 80% of the time spent on most AI projects.

How does data labeling help? In simple language, it makes data and other digital content recognizable to the machines that are trained through algorithms to learn and utilize the information for making decisions and predictions or executing tasks.

It assumes more importance as businesses invest in AI technologies. According to Global Market Insights, the market size of data labeling tools exceeded $1 billion in 2020 and is projected to grow at an annual rate of over 30% between 2021 and 2027.

When done correctly, data labeling can deliver exceptional market insights, drive sales, and help you reduce costs. Because data labeling can consume so much time and money, automation is developed and deployed as often as possible.

However, there are times when data labeling must be handled manually. Knowing how and when to use each approach is vital both in terms of accelerating your effort and minimizing your costs. Let’s take a look at the pros and cons of both Manual and Automated data labeling.

Manual Data Labeling

Manual data labeling is performed by a team of data experts who are assigned the task of identifying objects of interest and adding metadata to these objects manually. Typically, these experts examine hundreds of thousands of images and objects to construct comprehensive and quality AI training data for your model. Seems labor-intensive and time-consuming, right? Let’s discuss the pros and cons of manual data labeling.

Pros of Manual Data Labeling:

• More Accurate Results

For any business, human annotators are your go-to resource when it comes to precision and quality in labeling data. These experts have several years of experience in tagging data and understanding the requirements of different machine-learning models.

They can also identify anomalies that are otherwise missed by automated processes. Whether you are building computer vision or natural language processing (NLP) models, labeled features will be more accurate when they are consistent with real-world conditions.

• Easier to Customize

Human experts in data labeling and annotation are more in tune with your evolving business requirements and objectives. As a result, they have the flexibility to incorporate changes that are tuned to your end users’ needs, product changes, or modifications in data models. This flexibility allows them to quickly shift gears and tackle data annotation projects corresponding to your specific business needs.

• Better Data Quality Assurance

Data quality is the most critical component when it comes to the accuracy of data labeling. Well-trained individual data labelers review the quality of your labels and release only the approved objects for analysis. This always ensures quality and precision in model training datasets. For example, imagine the task of labeling the various components of a car. Manual labeling tools are better equipped to capture the edge cases of the object that would be missed by automated labeling tools.

• Stronger Data Security

With in-house data labeling, organizations are in control of their data, thus maximizing data security. With a correct and efficient security system and protocol, the risk of data leakage is significantly lower for your business.

Cons of Manual Data Labeling:

• Slower

Labeling big datasets takes time and effort when your enterprise relies on human experts. This is one of the major constraints preventing companies from labeling data manually. For example, let’s say your company wanted to do a sentiment analysis of your customers’ reviews on social media. Now imagine your company wants to use 90,000 reviews to build an accurate data model. If a labeler takes 30 seconds to annotate each comment, they will spend 750 hours completing the task.

• More Expensive

As data science and artificial intelligence are some of the most in-demand industry skills, experienced professionals in data labeling would be highly-paid resources. At times, businesses need to spend an incredible amount of money and resources hiring and training experts to execute relatively simple annotation tasks. Moreover, maintaining a small team of data labeling professionals in-house can be prohibitively expensive for most organizations.

Automated Data Labeling

Automated data labeling simply refers to labeling not performed by people. Machine learning models are self-trained to recognize which labels to attach to which data points. The model needs to self-learn the labeling rules for objects and data points. Machine learning algorithms allow these models to sense, reason, act, and adapt by experience and as much as possible, mimic the human brain.

For instance, for any unstructured customer data or content, automated data labeling can be deployed to identify segments of customers with similar combinations of attributes and treat them similarly in marketing campaigns.

Pros of Automated Data Labeling:

• Faster and Less Expensive

Because there is little (or no) human intervention in automated data labeling, businesses save significant operational costs and time they would otherwise spend to hire technical experts or create an in-house team.

• More Precise Learning and Improvement

Using active learning, a semi-supervised approach, automated data labeling provides highly accurate data annotation. Active learning requires the labeler to select an initial sample from unlabeled data and then label more data based on the results. In addition, automation can be leveraged to continually enhance and improve your manual data labeling processes.

Cons of Automated Data Labeling:

• Problems with Labeling Unseen Data

When you use automated labeling exclusively, machine learning models are trained according to available sample datasets. Objects and data points that are external to the sample set might not be labeled accurately. Human experts are capable of addressing such untrained or unexpected cases.

• Probability of Future Errors

If a data point is incorrectly labeled, future errors tend to occur and go unnoticed, because the machine learning model is being trained according to the existing incorrect results. This will adversely impact the performance of downstream processes and the accuracy of predictive models.

So, What Does This All Mean For You?

In 2020, around 59 zettabytes of data were generated. Data labeling will assume more importance as organizations continue to leverage AI technologies to extract value from all of those information assets. To optimize your results, we suggest using a blend of both manual and automated data labeling approaches depending on the urgency, scale, and potential business impact of the specific business process.

As discussed, both approaches provide important benefits. Finding it difficult to manage and extract business value from an enormous amount of data? At EnFuse Solutions, we offer end-to-end services in data labeling, tagging, and annotations. As a solution provider, we are committed to optimizing your data quality for training your AI and ML models, ultimately improving your business results. Want to learn more about how we can help you succeed? Contact us today.