Why Synthetic Data Matters

by Taymour | July 24, 2022

Data is the lifeblood of business today. It's what we use to make informed decisions about where to allocate our resources, how to improve our products and services, and who our target market is. However, not all data is created equal - some data is more valuable than other data. And when it comes to synthetic data, that's the case. Synthetic data can be incredibly beneficial for businesses in several ways, as we'll explore in this blog post.

Synthetic data generation is faster, more adaptable, and more scalable than real-world data. Synthetic data production is also more flexible and efficient than real-world data. Modifying parameters may also be a good method for modeling and generating information that does not exist in the real world.

In finance, it's crucial to be able to anticipate market and trend changes. Modeling a financial catastrophe might help you come up with effective preparations and forecasts long before they are required.

Synthetic data allows data scientists to use machine learning models to represent any scenario by supplying them with real-world data. Synthetic test data can be used to simulate "what if" scenarios, making it a useful tool for testing a hypothesis or developing many outcomes.

The fact is that each data point has a cost. The majority of data is collected manually at a cost that doesn't make sense when you're dealing with massive quantities of information. Simultaneously, much of the most precious data you collect within your company may be protected by privacy laws like GDPR. Data scientists don't just need to find more records; they must also ensure that their research results are correct and legal for them to use as a data set to train machine learning algorithms.

Missing data causes all kinds of issues

Missing values are commonly encountered in real-world data. They can arise due to data entry errors, missing observations, or simply because the variable of interest was not measured. When dealing with missing values, it is important to understand why they matter.

When working with missing values, it is important to understand why they matter. Missing values can introduce bias into your data, and can also lead to decreased accuracy in your models. Additionally, missing values can make it difficult to interpret your results.

Missing values can also have a big impact on the results of machine learning and/or statistical models. There are several methods for dealing with missing values, each of which has its advantages and disadvantages. The most common methods are:

  • Delete missing values: This method simply deletes any rows or columns that contain any missing values. This can be problematic, as it can remove valuable information from your analysis and can be misleading.
  • Replace missing values with averages: This method simply deletes any row or column that contains any missing values. This can be problematic, as it can remove valuable information from your analysis and can be misleading.
  • Replace missing values with synthetic data: Many data sets include records with missing values. You can approximate the missing ones in cases where numerical values are concerned to produce useable records. Many data scientists utilize mean or median values to fill in their data. However, this has a significant influence on the quality of your machine learning model.

Another option for missing values is to let AI fill them in automatically. In this approach, your algorithm will: analyze all of your data to find patterns in its values and create predictive values that are consistent with the statistical characteristics of your entire dataset this, again, relies on having adequate data in the first place. If you don't have enough data for your model to make accurate predictions, you won't be able to auto-complete values to a great degree of accuracy.

Predicting missing values is the first step in supplementing your data – but it misses a lot of the real value that newly generated data brings to the table.

Data scientists frequently struggle to locate high-quality, well-balanced data on the scale they require, rather than the difficulty in finding data. You may replicate the characteristics of a current source dataset with synthetic data, generating more data from a lesser class that's well-balanced. As a consequence, you can generate infinite new data on any scale, complexity, variety, and balance. Meanwhile, it is worth repeating, that you're getting rid of privacy limitations on how you can utilize that information.

Synthetic data is better and more scalable than real-world records, but it can also provide data scientists with a method to accomplish new, creative things that are impossible with real-world data alone, feeding the models that will have an impact on our data-driven future.

In the stock market, anticipating markets and trends is vital. Modeling a potential financial crisis could allow you to make robust plans and forecasts long before they are needed.

Synthetic data can improve model performance

When real data is not available, or when it doesn't meet all the conditions of the original data, synthetic data can be used to build better AI models by removing biases and/or augmenting data that isn't complete. This is because synthetic data can be generated to exactly match the real-world distribution of data, making it an ideal substitute in these situations.

Not only can businesses use synthetic data to build better AI models, but they can also increase productivity by sharing sensitive PII and PHI data. By realistically cloning this data, businesses can develop, test, and QA software without putting their customers' privacy at risk.

Artificial intelligence is all the rage in 2020, but many aspiring technologists are running into a problem: training data.

For most artificial intelligence/machine learning applications, having a huge, curated database is required. Getting that data is frequently difficult. Students, small research teams, and early-stage businesses face significant training data difficulties.

That's where synthetic training data comes in handy. Synthetic data is phony data that duplicates real data. For certain ML applications, generating fake data is simpler than collecting and labeling genuine information. There are three key reasons for this: You may generate as much artificial data as you want; you can produce dangerous-to-collect real-world data; synthetic data is automatically labeled.

What is synthetic data, and how does it work? The need for a large amount of data is one of the most important rules of machine learning. From ten thousand examples to billions of data points, the quantity of data you'll need varies. Collecting a lot of high-quality training material for complex applications like autonomous vehicles is difficult. The choice you make when it comes to the size of your data is a matter of personal preference. Synthetic data works well with all sizes of databases.

In most cases, collecting each extra learning example takes the same amount of time as the previous one. That isn't the case with artificial data, however. Synthetic data may be generated in enormous quantities, which makes it special. How many training examples do you have? No problem at all. A million? Excellent! A billion? There's no problem; you could need a stronger GPU but that's OK. In comparison, generating a billion real training instances may be tough sledding.

Real data may be bad for your health

Another reason you may want to utilize synthetic data vs real data is because the latter may be harmful to obtain. Autonomous vehicle AI cannot solely rely on real data. Simulation is required for businesses working on this technology, such as Alphabet's Waymo. Consider this: to teach an AI how to avoid a vehicle accident, you'll need training data on accidents. However, gathering huge datasets of actual car crashes—especially when those cars are moving at high speeds—is simply too expensive and dangerous; therefore, you simulate accidents instead.

Rare data is rarely complete


The principle of a dangerous collection can also apply to data that can be collected very rarely.

For example, if your AI algorithm is looking for a ‘needle in a haystack,’ synthetic data can generate rare events in sufficient quantity to accurately train an AI model.

Consider this – some of the most beneficial uses of AI are focused on ‘rare’ events. By the nature of these problems, rare events are hard to collect. Going back to the automotive example, car crashes don’t happen so often, and you rarely have a chance to collect this data. With synthetic data, you choose how many crashes you want to simulate.

You are the boss of synthetic data
Everything in a synthetic data simulation can be controlled. It's a gift and a burden. It may be a curse because synthetic data sometimes misses edge cases that can be found in real datasets.

However, it can also be a blessing because you can generate any type of data you need, which can be extremely useful for complex applications like autonomous vehicles or to simulate the sales force of a large multi-tiered business.

Synthetic data can easily be annotated
Another bonus of synthetic data is accurate annotation. You will never have to collect data manually again. For each item in a scene, a variety of annotations can be automatically produced. It may not appear to be much, but it's one of the key reasons why synthetic data is so inexpensive compared to real data. The major cost of synthetic data is the development phase upfront. After that, generating data becomes increasingly more cost-effective than collecting real information