Automated Data Labeling With Machine Learning

Today, big data has become one of the fundamental pillars of modern business. Those who can cope with large amounts of data have a chance to build communication with the consumer in the most productive way. Big data defines the entire business strategy of large companies, but it’s not enough to get and collect an array of information. For it to acquire value, it is necessary to systematize and classify the data in the right way.

Obtaining information from a variety of sources requires not only a huge capacity of data storage systems, but also various tools and qualifications for correct analysis and use of this information. The effectiveness of the classified or labeled data is a key driver of business growth, and can become the impetus for developing new business infrastructure.


First of all, let’s understand the data labeling definition. In simple terms, data labeling is a way of organizing information depending on its content. This content determines the tag or label to be assigned to a specific piece of information after it has been processed.

For example, one unit of information may contain an image of shoes, while the other unit is textual – a sales manager CV. When a person processes this information, it is logical that in the first case the expert will assign the tag “shoes”, and in the second – “the sales manager’s CV” or something like that.

But when this information is processed automatically, how should the system understand what is depicted in the picture or written in the text? Which tag should be attached to each data unit? To make this possible, a person needs to teach a machine to recognize the patterns automatically by running learning algorithms for labeled datasets. This is designed to simulate the human decision-making process.

Thus, there are two ways of labeling data – manual data labeling by a human, or automated data labeling powered by machine learning. Further, we will analyze each of them.


There are four basic ways to perform data labeling.


In this case, the company’s full-time employees work with big data on their own. The main advantages of this approach are:

  • no additional costs for attracting outside specialists;
  • the ability to personally control the process and the result;
  • quality information received.

If we talk about the shortcomings, then this task will, in any case, be performed slowly due to the human factor.


This is a way to entrust the task execution to a large number of people at once. This task will be completed fairly quickly, but it is impossible to say the same about the quality with confidence. On the other hand, such services are very affordable in terms of both labor resources and prices.


It is convenient to hire a freelancer when you need to complete the one-time task quickly. As for the data labeling, this can also be a reasonable way out, but only in the case when you have the opportunity to check the quality of work. The low price for such services is among the obvious advantages of this approach. However, you will have to manage the process and carefully monitor the security of data that you provide to the external specialist.


If you do not trust freelancers, there is an opportunity to cooperate with companies that offer data labeling as a service. The key advantage of this method is a highly qualified team of data scientists and data analysts. But, it is still necessary to understand the specifics of a particular market and business, and thus, have an in-house expert that will control the process.


Today, experiential learning applies to machines, which are able to sense, reason, act, and adapt by experience trying to mimic the human brain. For this, the researchers use machine learning algorithms that allow AI systems to analyze and learn from input data independently. So, which approach is used for automatic labelling :

  • Reinforcement learning enables AI models to learn by the trial-and-error method within a specific context using feedback from their own experience. It’s widely used in robotics, gaming, data processing, industrial automation, and chatbots which learn from users’ interactions.
  • Supervised learning requires a huge amount of manually labeled data. The system compares the newly received data with the labeled data to find errors and inconsistencies. The model was then modified accordingly. It learns how to predict the probability of future events occurring and is mostly used to anticipate fraudulent credit card transactions or analyze historical data. It is a very sensible though time-consuming approach, where a mistake or inaccuracy in the input data can negatively affect the quality of the output.
  • Unsupervised learning leverages raw, or unstructured data. It is used for more complex processes because its goal is to find the structure on its own and organize the data into a group of clusters. This type of learning is good for transactional data like identifying segments of customers with the same attributes to treat them similarly in marketing campaigns.
Types of machine learning: explained

Deep learning is a subset of machine learning that can learn and improve independently. Now, deep learning programs (DL) efficiently perform multi-level calculations within a series of layers that constitute a neural network. The input layer receives the information from the outside and then transmits it to the ‘hidden’ layers to make a comprehensive analysis of the data by performing mathematical computations on inputs. The more ‘hidden’ layers the network has, the deeper it is. The output layer compiles all the input data and performs data classification.

For example, neural networks that analyze images of buildings can detect edges in one ‘hidden’ layer and then recognize that these edges form a rectangle in another ‘hidden’ layer. In the subsequent layer, they recognize the rectangle as a building and, finally, determine whether the building is a skyscraper or a garage.

Software developers make high-capacity deep neural networks capable of learning by analyzing huge datasets. The raw data itself is not so useful, so the developers annotate or add notes to the input data with the ‘correct’ understanding as if marking it for the machine. AI systems are designed to automate data processing, labeling, and categorization. But, they need to be trained with high-quality and accurate information first to work smoothly and with minimum human intervention.

Thus, data annotation is the most important component of machine learning success. Data annotation and labeling are interrelated.



AI brings fundamental changes to marketing. Today, big data analysis and labeling make it easy to hit the narrowest possible target audience. You can use Facebook and Google advertising platforms to search for specific consumers who will be attracted by the ad, collect and analyze consumer data from several channels. This data should then be stored, classified, filtered and reused. The system itself decides when and what kind of promotion to show and how much to pay for its display. And all this is possible in real time without an army of marketers. So, it is only a matter of time, when AI algorithms will replace most advertising agencies – It’s a joke.


Artificial intelligence works perfectly well for both automating some elements of the recruitment process and predicting the most suitable candidate. AI can be applied to analyze language patterns in job ads, for instance. It can tell why some of the ads don’t work and how to rephrase the text to attract diverse candidates. Moreover, instead of manually browsing through huge stacks of resumes, AI-powered tools flag ideal CVs for the manager to review. They perform automatic resume screening based on keywords related to the skills and experience needed for the job, use online questionnaires, and leverage social data to identify the best candidate.

With the help of AI assessment tools, HR managers can narrow down the list of top candidates using key attributes like abilities, aptitudes, and soft skills. Therefore, AI will facilitate the process of hiring employees, reduce the related costs, and put an end to bad hires.


Data labeling tools help to make the data clearer and more applicable to business. Data collection mechanisms – from the analysis of text to machine learning algorithms that track customer preferences and habits – are now available for any enterprise. Automated data labeling makes it possible to spot the most relevant data, classify, group, sort it by a specific tag, predict customer’s behavior and develop marketing strategies based on it.


The main issues with data processing, labeling, classification, and analysis are related to optimization of data presentation and storage, construction of fast information retrieval algorithms, and design of recommender systems.

As for the training data, there are two main stumbling blocks. Since a person trains the machine, there is no guarantee that this action itself was carried out without errors. The second problem is the impossibility of taking a unified approach to analyzing data in different companies. Since each company uses, analyzes, and structures data by its needs and business processes, each company must also use its unique mechanisms of data labeling for deep learning.


Big data analysis tools allow companies to enhance their infrastructure, as well as reduce labor costs through more efficient methods of data management. These tools make it possible to collect and analyze data from hundreds of different channels immediately, and then use it to improve critical business processes like marketing and sales. As a result, this new and efficient way of doing business leads to a significant increase in profits. That is, at the present stage, Automatic data labeling software adds more value to business operations and provides the company with a competitive edge.

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