When Big Data Is Not Big Enough: Why Do We Need to Generate Synthetic Data?

Humanity surrounded by tons of data, which still not processed and analyzed. Yet synthetic data generation has become common, or even essential for a range of machine learning tasks. It now raises certain questions. The main question is why do we actually need to generate synthetic data despite real data abundance?

In scientific reality, not all data is accessed freely, and not all tasks are solved with the amount of data available. For this reason alone, synthetic data generation has become an independent expert field focusing on the right approaches to imitate data in the most realistic way to make it match the analysis objectives.

The origin of synthetic data usage dates back to the twentieth century, but only now have scientists started retrieving the true potential of generating extra data for ML modeling. 

Synthetic Data Use Cases

Synthetic data application has become most popular among machine learning experts. Machine learning models assume the possibility of incorrect interpretation of the researched phenomena; subsequently, experimental approaches work well in the area. In particular, synthetic data is widely used to provide training datasets, if the real data is not available.

You can go deeper into the latest trends for the synthetic data generation: self-supervised learning.  Since machine learning contributes to a range of industries, below we provide just the top-level examples of how machine learning research benefits from synthetic data generation in some of them.

One of the explicit applications of synthetic data is found in the healthcare industry, which frequently suffers from the lack of real-world datasets on rare and unknown diseases. The world’s medical community shares voluminous databases of patient records from the first symptoms to the outcome of a particular treatment and post-disease condition. However, when such a thing as the COVID-19 pandemic happens, all of these records make no sense for the vaccine development. 

Any inventions that we are not used to, such as self-driving vehicles, require synthetic data tests to simulate the real-life environment and design the vehicles in a safe way. At the initial stage of work, researchers do not implement this kind of testing in real circumstances to avoid catastrophic consequences.

Basically, any innovation benefits from synthetic data generation and speeds up the design and development of new products.

Synthetic data generation powers entire R&D departments and makes ideas come true. Due to the synthetic data generation, we now know that cashier-less stores, warehouse robots, and drone managers are real. 

Urbanistic research has demonstrated an outstanding urge to apply synthetic data generation methods to transportation planning. Scientists take real-world census data and apply natural evolution processes (such as getting older, getting married, reproducing) to this data. Thus, they simulate the household populations over years and their interaction within the researched region. The application of this study varies from transport demand to relocation behavior management. 

Marketing is always ready to take up any big data solution, and synthetic data generation is not an exception. Customer analytics in finance uses synthetic data on transactions to analyze customer data and reveal patterns in customer behavior. Overall marketing spent based on individual behavior is also predicted with synthetic data. It helps companies speed up their decision-making without having to obtain private data. 

Some social media algorithms grow and develop with the help of synthetic data. While social media have many opportunities to test new algorithms online, they cannot do this all the time. Many users rely on their social media configurations to achieve their daily goals.

Constant testing would affect the user’s personalized news feed significantly. That is why social media filtering algorithms are trained with synthetic data to learn how to block fake news, hate speech, and other types of offensive behaviors in the online environment. 

Almost any field, which assumes personal data usage, benefits from synthetic data generation. Today, HR strongly relies on truly private data about candidates to improve HR processes. Data professionals perform analysis with synthetic data, as real-world data usage may require permission from several parties simultaneously. 

Cybersecurity gets more and more ML-driven with time to grow the capability of predicting and preventing sophisticated attacks. The principle for using synthetic data generation remains the same. Cybersecurity researchers have no available real-world data on what kind of attacks to expect, but they can simulate and prevent them autonomously. 

Synthetic Data Generation Methods

Synthetic data appears in three main shapes. Machine learning scientists use fully synthetic data, partially synthetic data, and hybrid synthetic data. The synthetic data generation follows either of the two methods:

– It can generate from the real-world distribution. In reality, the distribution of the real-world data copied and represented with simpler numbers.

– Agent-based modeling used to create models of individuals and their interactions. Australian studies on urban planning and transport demand are the best examples of agent-based modeling applications in real life.

Synthetic Data Hurdles

Unfortunately, synthetic data generation is not a universal solution to the most complex and meaningful machine learning problems. Below are some reasons why synthetic data needs more research and application to grow its contribution into the research world. 

  1. Synthetic data is hard to generate, and it needs a thorough approach to ensure its realistic look. Thus, machine learning specialists in this narrow area are in huge demand. Sometimes a synthetically generated model gets extremely popular and handy in one industry and absolutely overlooked in another. 
  2. The final decision-maker will not always be a scientist. Sometimes it is just hard to prove that synthetic data is trustworthy. And this cautious approach is not wrong as long as synthetic data-driven algorithms influence human lives. Some synthetic data-driven research may even remain unused for a long time before it finally get a chance to be validated with real-world data. 
  3. Even if we do achieve high quality of real-data replication, we need to accept that in the end, we will never replicate natural things by machine learning algorithms fully.
  4. It is hardly possible to use synthetic data outside machine learning training tasks for safety reasons. 

Synthetic data may seem like a new concept, but, in fact, it is already quite essential in several industries for a long time, such as eCommerce,  healthcare, and urban studies. Moreover, some advanced companies have already integrated synthetic data generation in their usual quality assurance, marketing, HR, and other processes.

Despite challenges, we believe that synthetic data is on its way to becoming one of the most powerful components of ML modeling in the following decades. 

Stay tuned to the latest machine learning news, and you will learn more about trendy concepts and their relevance to your business tasks.

Leave a Comment

Exit mobile version