Benefits of Using Synthetic Data for Your Business

Today, businesses use data as their guide for decisions, strategies, and new ideas. But finding good data for analysis can be tricky, especially when we need to keep private info safe. That’s where synthetic data steps in. It’s like a pretend version of real-world data.

It has been created artificially to mimic the patterns and relationships in real data, but without any secret details. This makes it a handy tool for businesses to test and improve their systems without worrying about security.

Data is a big deal for modern businesses. It helps them figure out what customers want and keeps them on track with changes in the market. Having the right data isn’t just about making plans; it’s about making things work better and staying ahead of the competition.

Synthetic data can be really helpful for your business. It solves problems with data and privacy, which makes it a great tool for analysing your business. Curious to know more? Keep reading…

Understanding Synthetic Data

What is Synthetic Data?

Alright, let’s talk about how synthetic data is made. Think of it like this: An artist who doesn’t always paint things they see in real life. Sometimes, they make up new scenes using their imagination. Synthetic data works a bit like that. Instead of using actual data, it makes new data that looks a lot like the real stuff.

Data Snapshots

Market Growth: The worldwide synthetic data market is expected to reach $4.56 billion by 2024, growing at a fast rate of 32.1%. The Asia-Pacific region is leading this growth, especially in finance, healthcare, and e-commerce.

Industry Adoption:

• Finance: 80% of institutions use synthetic data for fraud detection.
• Healthcare: 60% considering it for clinical trials and disease surveillance.
• Retail: 75% of e-commerce uses it for customer insights.

Business Benefits: Synthetic data offers a triple advantage: up to 70% cost reduction, faster data preparation, testing, and development, and privacy-conscious innovation.

How is Synthetic Data Generated?

Synthetic data is made by computer programs using special methods and rules. These tools copy the patterns we see in real data, kind of like using a stencil to make the same design over and over. What’s interesting is that even though synthetic data seems real, it doesn’t have any real details about people or events. So, it’s safe to use without worrying about privacy issues.

Here are common methods widely used to create accurate synthetic datasets:

1. Sampling from distributions: This is a simple way to make synthetic data. You first look at the stats of your real data, like the average and range. Then, you use tools like NumPy to pick random values that fit those stats. This is good for making simple table data.

2. Generative models: These are more advanced tools that learn the patterns in real data and make new data that looks like those patterns. Here are some popular ones:

• Variational Autoencoders (VAEs): These tools take real data and change it into a hidden form. Then, they change it back into data, making new data points. It’s like taking a picture, drawing it, and then making a new picture from the drawing.
• Generative Adversarial Networks (GANs): These tools have two parts that compete with each other. One part makes data, and the other part tries to tell if the data is real or fake. This helps the tool make more and more realistic synthetic data.

3. Tools and platforms:

There are many online tools and platforms that make synthetic data generation easier, especially for table data. These platforms often have easy-to-use interfaces and built-in tools, making it easier for people who aren’t tech experts to make synthetic data. Examples include MOSTLY AI and Datasynth.

Here are some extra points to think about when choosing a method:

• Type of data: Different methods are better for different types of data (like tables, text, images).
• Complexity: Simpler methods might not capture complex relationships in the data, while advanced models need more tech skills.
• Control and customization: Some methods let you control specific parts of the generated data, like data privacy or specific relationships.

No matter what method you choose, remember to:

Define your specific needs: What type of data do you need, and what properties should it have?

Validate the generated data: Make sure the synthetic data looks like the real data’s characteristics and does what you need it to do.

Check for privacy considerations: Make sure your synthetic data generation method doesn’t break any privacy rules.

In essence, the creation of synthetic data is like being an artist in the data world. Tools and techniques are used to craft data that’s not only useful but also respects privacy. Quite fascinating, isn’t it?

Why Your Business Needs Synthetic Data?

Making the right decisions at right time in business world can lead to success. Here’s how synthetic data can be really helpful:

1. Speeding Up Innovation:

Synthetic data is like a sandbox for new ideas. It lets your team try out new things, build prototypes, and improve models without worrying about not having enough real data or dealing with sensitive information. This speeds up the process of coming up with new ideas, making your business more flexible and quicker to adapt with digital transformation.

2. Solving Privacy Issues:

Privacy is really important, especially now that there are a lot of rules about data. Synthetic data lets you make realistic datasets without using any private information. This is really useful when you’re working with sensitive information because it makes sure you’re following data protection rules.

3. Cost-Efficient Testing and Development:

Testing and developing systems can use up a lot of resources. Synthetic data helps cut down costs by providing a cheaper way to experiment. You can improve algorithms, train models, and check performance without the high costs of dealing with a lot of real data.

4. Making Data More Diverse:

Real-world datasets might not have a lot of variety, which can lead to models that are biased or give skewed results. Synthetic data lets you create a variety of scenarios, making sure your models are strong and can handle different situations. This makes your systems more reliable and versatile.

5. Future-Proofing Against Data Scarcity:

Sometimes, you might not be able to get enough high-quality real-world data. Synthetic data acts as a backup plan, making sure your operations and innovations can keep going smoothly even when it’s hard to get enough real data.

Myth vs Facts: Synthetic Data

Benefits of Using Synthetic Data

1. Privacy and Rules: Synthetic data helps businesses deal with privacy worries and follow data protection rules like GDPR and HIPAA. Since synthetic data is made, not collected, it doesn’t have any real person’s private info, keeping data private and following rules.

2. Cost-Effectiveness: Getting and managing real-world data can be expensive, especially with lots of data or sensitive info. Synthetic data is a cheaper option, cutting down costs linked to getting, storing, and looking after data.

3. Lots of Different Data: Making synthetic data lets businesses create a wide range of scenarios, edge cases, and variations that might not be in real-world datasets. This variety can make AI/ML models stronger and more general.

4. Overcoming Data Scarcity: In many industries, getting enough real-world data for specific uses can be hard because of limited availability or rules. Synthetic data solves this problem by making tailored datasets to help develop and test AI/ML applications.

5. Quick Development and Testing: Making synthetic data can speed up the development and testing stages of AI/ML projects. It gets rid of the need to wait for real data and can speed up the process for improving and checking models.

6. Safe Experimentation: Synthetic data lets businesses try out and fine-tune AI/ML models without the risks linked to using real data, like accidentally exposing sensitive info or unintended biases.

7. Better Security: Since synthetic data doesn’t have real info, there’s a lower risk of data breaches or unauthorized access. It adds an extra layer of security for businesses working with AI/ML models.

Difference Between Synthetic Data and Real Data