In this data-driven world, high-quality data plays an important role because all the applications, like ML algorithms, healthcare diagnostic tools, and financial decision-making platforms, are surrounded by it. However, collecting the data from the real world is a tough and tedious task. What’s more complex is ensuring the dataset’s accuracy, relevancy, and correctness, and an inaccurate dataset can lead to improper outcomes.
Data quality issues, like outdated information, biased data, faulty data collection, and incomplete data, hampers the quality of the dataset. Besides, thorough protocols need to be implemented to ensure the protection of collected data.
Since most AI/ML algorithms learn from the dataset, inaccurate data can negatively impact the learning curve and affect the performance. While there are many techniques, like data cleaning, data validation, and data standardization, all these processes are time-consuming and require resources, which proves to be expensive.
To save time and resources, organizations are turning to synthetic data, which is usually created using new and advanced machine-learning algorithms. Synthetic data is artificially generated data that mimics real-world data, but it doesn’t contain any personal or sensitive information. It allows to creation of a rich and varied dataset without compromising anyone’s privacy and simplifying data management.
What is Synthetic Data?
Synthetic data is artificially generated data that you may consider as a replication of real-world data characteristics and statistical properties. The requirement for creating such data involves using various algorithms such as simulations, machine learning models, or mathematical methods.
Synthetic data is unlike traditional data that is collected from real-world interactions. The former is a fabrication of data that involves mimicking the patterns, distributions and relationships in real data.
Developers and testers can use it for testing software applications, training machine learning models, and filling gaps in data sets when working on analytics projects because synthetic data does not have one-to-one correlations with real data.
How is Synthetic Data Different from Real Data?
Synthetic data and real data may sound similar, but they differ in terms of distribution and variability. The following table explains these differences in detail:
| Category | Synthetic Data | Real Data |
|---|---|---|
| Definition | AI-powered data is generated to simulate real-world data patterns. | Actual data collected from real-world sources. |
| Source | Created through simulations, algorithms, or pattern generation. | Collected from user interactions, transactions, or events directly. |
| Data Privacy | High data privacy, as it contains no actual sensitive information. | It contains identifiable, sensitive information. |
| Accuracy | It may mimic real data patterns but lacks authenticity. | High, as it represents real events and interactions. |
| Risk of Re-identification | Low-risk re-identification, as it doesn’t include real user information. | High-risk re-identification, depending on the presence of personally identifiable information (PII). |
| Data Utility | Effective for model training, testing, and simulations. | Needed for production and insights that may require actual trends. |
| Cost of Acquisition | Lower expenses, as it can be generated programmatically. | Higher expenses often require data collection efforts and regulatory compliance. |
| Bias and Representativeness | Less biased if designed well, often depends on input patterns. | It may contain biases reflective of collection methods and active populations. |
| Scalability | Easily scalable and can be generated in large quantities. | Limited by the availability and accessibility of real data. |
| Use Cases | Ideal for algorithm testing, prototyping, and privacy-preserving analytics. | Essential for production, regulatory reporting, and detailed insights. |
Challenges Faced in Traditional Data Management
Traditional data management practices involve storing data in structured and centralized databases. It often includes using on-premise infrastructure or established database management systems, like Oracle or SQL Server, to manage data within the organization with limited real-time processing capabilities.
However, things have changed, and organizations have switched to cloud-based platforms, but there are data management challenges, like:
- Data Quality Concerns
Poor data quality can impact organizational processes, leading to inaccurate analysis, inefficiencies, and flawed decision-making. Incomplete or inaccurate data may result in misguided strategies, ineffective operations, and even compromised customer experiences.
- Integration Challenges
Integrating different data sources can be challenging for businesses. These sources often have different structures, formats, and technologies, which makes it tough for organizations to merge them smoothly. Data might be stored in various databases or systems, both within and outside the organization, needing efficient processes to extract and transform it.
- Scalability Issues
Scaling data management strategies to meet the upcoming business needs comes with its challenges. Traditional data management approaches often fall short in dealing with infrastructure limitations, growing data volumes, and adapting to new technologies.
- Lack of Data Governance
Lacking data governance is one of the primary data management challenges that business organizations face today. Data governance plays a key role in handling data management issues. You cannot ensure accountability, set data standards, or ensure compliance without proper data governance policies. Their absence may also lead you to face compliance violations, fines, and legal consequences as a functioning business.
How Synthetic Data Simplifies Data Management?
Data is considered one of the most important assets for businesses across industries. Effective data management is the key to ensuring data quality, accessibility, and security with ever-increasing volume, variety, and velocity of data.
The rise of synthetic data management techniques implies an evolution of how organizations approach data. It usually offers multiple innovative ways to address traditional data management challenges and find their solutions.
Synthetic data is specifically preferred for simplifying data management due to the following reasons:
- Synthetic data can be generated in controlled volumes to reduce the need for excessive collection of real-world data. Since the data is generated artificially, it is scalable and high-volume data can be easily generated without overloading the storage or infrastructure.
- Disparate storage systems lead to fragmentation, but synthetic data is centrally generated. It eliminates the need to pull the data from different sources and perform a standardization process to get a structured dataset. It avoids the need to reconcile the storage silos, and integration scenarios can be stimulated for testing purposes.
- Synthetic data is created keeping in mind the high-quality parameters, so the generated dataset is consistent, complete, and free from biases. It can easily replace the training dataset for the ML algorithms to ensure efficient learning.
- There is a lack of standardized and formalized data management frameworks but synthetic data doesn’t require any frameworks in place. The synthetic data generation process provides a sandbox environment which gets iterated as per the data processes.
- Since synthetic data doesn’t link back to any real individual or proprietary information, it can be safely shared among team members, transcending geographic boundaries without the risk of any breach or leak.
- The best part of synthetic data generation is that it is completely artificial and contains no personally identifiable information (PII), ensuring privacy and compliance by design. When it comes to traditional data management, organizations were required to establish data governance frameworks to ensure compliance and continuous monitoring was required as well. However, synthetic data doesn’t require any governance because it eliminates the risk of breach or misuse during testing, sharing, or collaboration.
In Which Scenarios Synthetic Data Can Be Used?
Synthetic data is powerful and beneficial, but there’s always a catch associated. While synthetic data looks like an apt solution to simplify data management, it is critical to understand that it cannot be used all the time.
For instance, synthetic data lacks realism and accuracy. Synthetic data accurately replicates the pattern and captures the correlations, but generating data that captures the nuances of the real world is difficult. Since it cannot capture the complexity of real-world data, it is best to avoid using it to make accurate predictions because it will omit the important details or relationships.
Similarly, synthetic data generation techniques work best when the set of rules is simple. However, sophisticated techniques will be required to generate data for complex tasks like natural language processing. The generated dataset for NLP should have syntactically correct sentences with proper grammer and punctuation rules followed whilst conveying the right meaning.
Also, validating the accuracy of synthetic datasets is another challenge to ensure their reliability. While the generated dataset looks realistic and the data generation techniques work on common trends and patterns, they may miss out on potential anomalies or critical details.
Another critical thing to note is that while we say the generated dataset will not have bias, what if the model-generating dataset is trained on the dataset which has some bias? The algorithms and models are trained on existing datasets, and they may contain bias or inaccuracies. So, one needs to be very critical before using synthetic data to ensure appropriate outcomes.
To make it simple for you, here are some use cases for which you can use synthetic data:
- Data-driven testing for software applications and development
- Privacy-compliant AI/ML model training
- Adherence to data privacy laws, like HIPAA and GDPR
- In heavily data privacy-regulated industries, like healthcare
Bottom Line
As data management methodologies continue to change and evolve, there is a rising need for access to realistic datasets. Synthetic data helps you understand a particular system or program’s logic, functionality and flow before real data is available.
Simplifying data management ensures that Simplifying data management ensures that businesses can efficiently analyse, test, and optimize their systems using synthetic data, even in the absence of real-world datasets. Thus, this AI-powered data helps improve development, reduce costs, and mitigate risks while maintaining data privacy and compliance.
