Leveraging Synthetic Data: Strategic Benefits & Use Cases
Amar Kanagaraj
January 8, 2024
What is Synthetic Data?
It is data generated with real data as a base. It doesn’t contain information that will compromise user’s privacy. Recently, with many leaks of social media selling user data to advertising companies, people are generally wary of having anything about them on the Internet. Due to this, many companies have opted to generate synthetic data.
Synthetic data can also be used to train models and get more data points to deal with more hypotheses. The market of synthetic data is expected to be worth $2.8 billion by 2028.
Why is Synthetic Data Crucial for Your Business's Future
In some lines of business, especially in finance and healthcare, gathering data for analysis and statistics and showing them publicly without infringing on privacy policies will be difficult. The same can be said for many businesses operating online. Without statistics and sample data, how would you market yourself and show that your business practice is successful? Because of this, many businesses will access the real data available to them, and based on a few parameters will generate similar data that can pass as real data, without the need to go the extra mile to conceal the user data. This will also cause your data to be less prone to reconstruction attacks.
Protecto can generate synthetic data which is nearly indistinguishable from real data. Try now or Book a Demo with Protecto to generate synthetic data for your business. There are many benefits of using synthetic data for your business explained in detail.
5 Benefits of Synthetic Data
With synthetic data’s undeniable use for testing business models, there are many practical uses to it other than benefitting the business. Let’s look at some of the advantages in detail below.
- Enhanced Privacy and Security
Due to completely removing any traces of data coming back to the users and generating data similar to the real data you have, there is greater privacy granted to the users of your E-commerce business site on the web.
The Security of your data is also protected since, even if hackers get your data, due to it being synthetic, can not be used for nefarious purposes and will be useless for them. You can use Protecto's services to generate synthetic data for your business to protect your user data and provide better privacy.
- Cost-Effective Data Management
Getting real user data is time-consuming is also, very expensive, and laborious. Any ethical business practice must ask for the user’s consent for their business model. This takes time and resources. Synthetic data doesn’t need such high costs or care as business owners need to care for their user data.
It is also way cheaper to source other companies to generate synthetic data based on a few parameters and use that data for statistics and data analysis. They also don’t need to spend money or resources on maintaining and updating their user data to feed their LLMs or ML models as frequently.
- Compliance with Regulations
In many sectors such as Healthcare, to store data and protect users, companies need to follow regulations such as HIPAA (Health Insurance Portability and Accountability Act) and many other Data Storage and Privacy regulations. The government lax their restrictions on synthetic data for your business provided hackers cannot use reconstruction attacks to trace back the original data.
You use only partial data and remove any traces of sensitive data to generate data. As seen in the diagram above, you can divide the real data into chunks, run them through models and come up with estimates with which you can run another algorithm to generate synthetic data based on the real data.
- Unlimited Supply of Data
Real-world data is finite. Sometimes, to develop LLM security and high-computing models with great accuracy for your business, you need to have more data. However, in many cases, such data is limited and restrictive.
Today’s LLMs such as ChatGPT are trained using 13 billion parameters (GPT 4) to bring such high accuracy and precision to their application. The trendy AIs or highly accurate models are all mostly trained by AI. It is said that Synthetic Data will completely take over and be used for Training data for AI models in the near future.
You can get a leg up by signing up with Protecto AI services to generate synthetic data. Gain an Edge with early synthetic data adoption with Protecto.
- Inject more variety into the data
Most data generated by the populace depending on the sector will be inherently biased. In a world where we’re striving for equality among all, ethical AI models cannot be inherently biased. Biased models will perpetuate the inequalities prevalent in our society instead of finding a solution.
To prevent this, businesses try to inject variety in data by artificially creating new data points and then feeding it to the model. Adding variety with some hypotheses can also make your model respond to unusual situations or problems in the business that much better.
In that regard, investing in synthetic data to train your LLM for customer support is definitely worth your time and resources. Try now or book a demo with Protecto AI for free to avail of their services.
But suppose you can generate synthetic data. How would you use it? How is it beneficial in different industries? You can learn more below.
Synthetic Data Use Cases
There are many use cases and advantages of using synthetic data for your business. Some of the use cases are listed below and explained in detail.
- Synthetic Data in Financial
Finance is the backbone of every industry. Hence, any forecast or statistics done or released by the finance industries will be closely monitored by all companies. Finance companies use synthetic data for user anonymization and also inject some variety for different income people to see the plausibility of their model.
With these financial trends, they can also collect more real data and use it as a catapult to generate more synthetic data. Many finance apps use synthetic data to show users sample data points of the most used mode of payment or the general favorite place of the populace by analyzing the place where people swiped their cards the most.
- Synthetic Data in Healthcare
Synthetic data plays an integral role in predicting patient diseases and developing Expert health systems based on AI. A patient’s health information is the most sensitive information out of all data out there. Many people prefer to keep their health information private.
For accessing different cases in the medical field without compromising any user safety, synthetic data is generated by anonymizing users, providing pseudonyms for patients and taking their medical history and prescriptions alone.
With this, greater analysis can be done on the patient data to find new technology to cure previously incurable diseases.
- Synthetic Data in E-commerce
Many startup companies will have access to fewer data points and diversity than big companies such as Google, Microsoft etc do. Hence, startup companies, especially when developing their models or LLMs, can use synthetic data from publicly available data at a price and then generate synthetic data out of it.
With this synthetic data now generated, you can perform analytics and statistical models to showcase the success of your E-commerce business. Synthetic data’s most common use case is in the E-commerce industry as seen in the graph below. E-commerce is only second to Healthcare.
To generate synthetic data suitable for your E-commerce marketing, you can use Protecto’s services to adopt synthetic data generation.
- Synthetic Data in Education and Training
Another vital use case of synthetic data is in Education. With the data Anonymization and Pseudonymization given, you can generate a lot of synthetic data with variable features and use this data to train new employees to contribute to the model or, this synthetic data can be used in various industries such as Healthcare, Finance, Business Analytics and so on to educate newcomers into the industry or provide them the synthetic data as a sort of training wheels for them to learn, analyze and gain insights from it.
As given from a seed, tasks can be generated synthetically to show examples of different problem statements and their solutions in class.
Despite its many benefits for businesses in using synthetic data, the progress has, however, been slow due to a few obstacles it faces in its way to being widespread in use.
What are the challenges of using synthetic data?
There are a myriad of challenges faced by those who would like to improve their properties by using synthetic data for their purposes. Some of them can be viewed below.
- Representation
Once all is said and done after removing the features of real data or anything that might compromise a person’s identity, it may be difficult to depict and analyze trends with the lack of identification and complexities in human data.
- Lack of Contextual Information
In some cases, synthetic data may lack certain features such as age (that may be redacted due to privacy policy) which plays a big role in industries such as finance or healthcare due to which certain types of analysis are important for various statistical implementations.
- Validation
Once synthetic data is generated, there is no reliable means to validate the plausibility of the existence of such a data point unless there is an exact match of that in the real dataset .Also, checking whether every data is valid in a large synthetic dataset is impractical.
- Ethics
With the concern over the validity of data, the usage of synthetic data-sensitive processes always brings in a question of ethicality, especially in the healthcare industry where using synthetic data may result in complications.
The data has to account for any sudden changes in the patient’s condition which it may or may not do depending on the data it is fed and trained. This is a major chokepoint for industries using synthetic data.
- Integration with Real Data
The synthetic data generated should be able to mimic the real data points to a high degree to add it along with real data, making the dataset partially synthetic. The generated data should be able to align with the real data points and should not skew the results or cause any bias. This is challenging to do when in the initial stages of data generation.
Conclusion
As seen in this article, synthetic data has its advantages. It is going in an upward trend where, in some time, companies will be reliant on the synthetic data generated for more powerful models and to test out the plausibility and validity of the LLM Privacy that businesses use. Along with that, it also has some glaring challenges which add to the hurdle. It is up to those who work with synthetic data to test out and find the optimal ratio of real and synthetic data. It is undeniable that your business will benefit from synthetic data. Protecto can help you adopt synthetic data into your business. You will be able to test the waters with a free trial and a demo which can be booked for you temporarily.
Gain an Edge with Early Synthetic Data Adoption with Protecto
What is Synthetic Data?
It is data generated with real data as a base. It doesn’t contain information that will compromise user’s privacy. Recently, with many leaks of social media selling user data to advertising companies, people are generally wary of having anything about them on the Internet. Due to this, many companies have opted to generate synthetic data.
Synthetic data can also be used to train models and get more data points to deal with more hypotheses. The market of synthetic data is expected to be worth $2.8 billion by 2028.
Why is Synthetic Data Crucial for Your Business's Future
In some lines of business, especially in finance and healthcare, gathering data for analysis and statistics and showing them publicly without infringing on privacy policies will be difficult. The same can be said for many businesses operating online. Without statistics and sample data, how would you market yourself and show that your business practice is successful? Because of this, many businesses will access the real data available to them, and based on a few parameters will generate similar data that can pass as real data, without the need to go the extra mile to conceal the user data. This will also cause your data to be less prone to reconstruction attacks.
Protecto can generate synthetic data which is nearly indistinguishable from real data. Try now or Book a Demo with Protecto to generate synthetic data for your business. There are many benefits of using synthetic data for your business explained in detail.
5 Benefits of Synthetic Data
With synthetic data’s undeniable use for testing business models, there are many practical uses to it other than benefitting the business. Let’s look at some of the advantages in detail below.
- Enhanced Privacy and Security
Due to completely removing any traces of data coming back to the users and generating data similar to the real data you have, there is greater privacy granted to the users of your E-commerce business site on the web.
The Security of your data is also protected since, even if hackers get your data, due to it being synthetic, can not be used for nefarious purposes and will be useless for them. You can use Protecto's services to generate synthetic data for your business to protect your user data and provide better privacy.
- Cost-Effective Data Management
Getting real user data is time-consuming is also, very expensive, and laborious. Any ethical business practice must ask for the user’s consent for their business model. This takes time and resources. Synthetic data doesn’t need such high costs or care as business owners need to care for their user data.
It is also way cheaper to source other companies to generate synthetic data based on a few parameters and use that data for statistics and data analysis. They also don’t need to spend money or resources on maintaining and updating their user data to feed their LLMs or ML models as frequently.
- Compliance with Regulations
In many sectors such as Healthcare, to store data and protect users, companies need to follow regulations such as HIPAA (Health Insurance Portability and Accountability Act) and many other Data Storage and Privacy regulations. The government lax their restrictions on synthetic data for your business provided hackers cannot use reconstruction attacks to trace back the original data.
You use only partial data and remove any traces of sensitive data to generate data. As seen in the diagram above, you can divide the real data into chunks, run them through models and come up with estimates with which you can run another algorithm to generate synthetic data based on the real data.
- Unlimited Supply of Data
Real-world data is finite. Sometimes, to develop LLM security and high-computing models with great accuracy for your business, you need to have more data. However, in many cases, such data is limited and restrictive.
Today’s LLMs such as ChatGPT are trained using 13 billion parameters (GPT 4) to bring such high accuracy and precision to their application. The trendy AIs or highly accurate models are all mostly trained by AI. It is said that Synthetic Data will completely take over and be used for Training data for AI models in the near future.
You can get a leg up by signing up with Protecto AI services to generate synthetic data. Gain an Edge with early synthetic data adoption with Protecto.
- Inject more variety into the data
Most data generated by the populace depending on the sector will be inherently biased. In a world where we’re striving for equality among all, ethical AI models cannot be inherently biased. Biased models will perpetuate the inequalities prevalent in our society instead of finding a solution.
To prevent this, businesses try to inject variety in data by artificially creating new data points and then feeding it to the model. Adding variety with some hypotheses can also make your model respond to unusual situations or problems in the business that much better.
In that regard, investing in synthetic data to train your LLM for customer support is definitely worth your time and resources. Try now or book a demo with Protecto AI for free to avail of their services.
But suppose you can generate synthetic data. How would you use it? How is it beneficial in different industries? You can learn more below.
Synthetic Data Use Cases
There are many use cases and advantages of using synthetic data for your business. Some of the use cases are listed below and explained in detail.
- Synthetic Data in Financial
Finance is the backbone of every industry. Hence, any forecast or statistics done or released by the finance industries will be closely monitored by all companies. Finance companies use synthetic data for user anonymization and also inject some variety for different income people to see the plausibility of their model.
With these financial trends, they can also collect more real data and use it as a catapult to generate more synthetic data. Many finance apps use synthetic data to show users sample data points of the most used mode of payment or the general favorite place of the populace by analyzing the place where people swiped their cards the most.
- Synthetic Data in Healthcare
Synthetic data plays an integral role in predicting patient diseases and developing Expert health systems based on AI. A patient’s health information is the most sensitive information out of all data out there. Many people prefer to keep their health information private.
For accessing different cases in the medical field without compromising any user safety, synthetic data is generated by anonymizing users, providing pseudonyms for patients and taking their medical history and prescriptions alone.
With this, greater analysis can be done on the patient data to find new technology to cure previously incurable diseases.
- Synthetic Data in E-commerce
Many startup companies will have access to fewer data points and diversity than big companies such as Google, Microsoft etc do. Hence, startup companies, especially when developing their models or LLMs, can use synthetic data from publicly available data at a price and then generate synthetic data out of it.
With this synthetic data now generated, you can perform analytics and statistical models to showcase the success of your E-commerce business. Synthetic data’s most common use case is in the E-commerce industry as seen in the graph below. E-commerce is only second to Healthcare.
To generate synthetic data suitable for your E-commerce marketing, you can use Protecto’s services to adopt synthetic data generation.
- Synthetic Data in Education and Training
Another vital use case of synthetic data is in Education. With the data Anonymization and Pseudonymization given, you can generate a lot of synthetic data with variable features and use this data to train new employees to contribute to the model or, this synthetic data can be used in various industries such as Healthcare, Finance, Business Analytics and so on to educate newcomers into the industry or provide them the synthetic data as a sort of training wheels for them to learn, analyze and gain insights from it.
As given from a seed, tasks can be generated synthetically to show examples of different problem statements and their solutions in class.
Despite its many benefits for businesses in using synthetic data, the progress has, however, been slow due to a few obstacles it faces in its way to being widespread in use.
What are the challenges of using synthetic data?
There are a myriad of challenges faced by those who would like to improve their properties by using synthetic data for their purposes. Some of them can be viewed below.
- Representation
Once all is said and done after removing the features of real data or anything that might compromise a person’s identity, it may be difficult to depict and analyze trends with the lack of identification and complexities in human data.
- Lack of Contextual Information
In some cases, synthetic data may lack certain features such as age (that may be redacted due to privacy policy) which plays a big role in industries such as finance or healthcare due to which certain types of analysis are important for various statistical implementations.
- Validation
Once synthetic data is generated, there is no reliable means to validate the plausibility of the existence of such a data point unless there is an exact match of that in the real dataset .Also, checking whether every data is valid in a large synthetic dataset is impractical.
- Ethics
With the concern over the validity of data, the usage of synthetic data-sensitive processes always brings in a question of ethicality, especially in the healthcare industry where using synthetic data may result in complications.
The data has to account for any sudden changes in the patient’s condition which it may or may not do depending on the data it is fed and trained. This is a major chokepoint for industries using synthetic data.
- Integration with Real Data
The synthetic data generated should be able to mimic the real data points to a high degree to add it along with real data, making the dataset partially synthetic. The generated data should be able to align with the real data points and should not skew the results or cause any bias. This is challenging to do when in the initial stages of data generation.
Conclusion
As seen in this article, synthetic data has its advantages. It is going in an upward trend where, in some time, companies will be reliant on the synthetic data generated for more powerful models and to test out the plausibility and validity of the LLM Privacy that businesses use. Along with that, it also has some glaring challenges which add to the hurdle. It is up to those who work with synthetic data to test out and find the optimal ratio of real and synthetic data. It is undeniable that your business will benefit from synthetic data. Protecto can help you adopt synthetic data into your business. You will be able to test the waters with a free trial and a demo which can be booked for you temporarily.
Gain an Edge with Early Synthetic Data Adoption with Protecto
Amar Kanagaraj
Founder and CEO of Protecto
Amar Kanagaraj, Founder and CEO of Protecto, is a visionary leader in privacy, data security, and trust in the emerging AI-centric world, with over 20 years of experience in technology and business leadership.Prior to Protecto, Amar co-founded Filecloud, an enterprise B2B software startup, where he put it on a trajectory to hit $10M in revenue as CMO.
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