In today's data-driven world, enterprises of all sizes rely on data for informed decision-making, driving growth, and staying competitive. However, sometimes, they cannot access the exact real-world insights required to generate data-driven decisions.
This creates a demand for artificially generated data that simulates real-world events and patterns, enabling them to have insights and perform predictive modeling. Synthetic data is an increasingly popular approach to leveraging data.
But what is synthetic data, and how can organizations benefit from it?
Introduction - Synthetic Data
Developing successful AI and ML models requires access to high-quality datasets. However, collecting such data is a challenging task because:
- Many business problems that AI/ML models solve require access to sensitive customer data.
- Collecting and using sensitive data presents privacy concerns and leaves businesses vulnerable to data breaches.
- Privacy regulations like GDPR and CCPA restrict the collection and use of personal data and impose fines on enterprises that violate them.
- Some types of data are expensive to collect or are rare. For instance - Collecting data representing real-world road events for an autonomous automobile can be prohibitively expensive.
- Collecting sufficient data to design ML models to predict fraudulent transactions is challenging, as fraudulent transactions are rare.
These growing concerns are compelling businesses to turn to data-centric approaches to AI/ML development, including synthetic data. Generating synthetic data is inexpensive compared to collecting large data sets. It can also help support AI/deep learning model development or software testing without compromising customer privacy. This growing popularity has led to an estimation that by the year 2024, 60% of the data utilized to develop AI and analytics projects will be synthetically generated.
What is Synthetic Data?
Advances in data generation techniques have promoted the creation of synthetic data that is indistinguishable from real-world data. This has opened up new opportunities for enterprises to test and validate their systems and strategies using synthetic test data.
Synthetic data is computer-generated data that imitates the characteristics of real-world data. Instead of using authentic data collected from different sources, synthetic data is developed using computer simulations. This approach is used when real data isn't unavailable or kept private due to data protection laws.
Real-world data is gathered from authentic sources like customer interactions, sensor readings, or financial transactions. While this data is valuable for analysis, it can be challenging to acquire and manage due to privacy concerns and other constraints. In contrast, synthetic test data can be developed on demand, enabling enterprises to bypass these challenges and gain valuable insights for decision-making processes.
In summary, synthetic data has far-reaching importance for businesses across industries. By leveraging synthetic data, organizations can overcome limitations associated with real-world data, equipping them to access high-quality training data for machine-learning models. As a result, enterprises can develop more accurate systems, leading to better decision-making and enhanced outcomes.
How to Create Synthetic Data?
To create synthetic data, data scientists are integrating different synthetic data generation tools. Synthetic data is computer-generated and resembles real-world data in structure and statistical approach without using actual data points from the real world. Let's understand why it is important and how it can benefit businesses.
Why is Synthetic Data Important?
Synthetic data has become increasingly necessary for multiple reasons, such as its potential to overcome limitations associated with real-world data, such as privacy concerns, bias, and cost.
With consumer information privacy becoming more stringent, the need for synthetic data is becoming significant. Today’s businesses are operating in different contingencies and have numerous outcomes for different user scenarios. Synthetic data allows them to respond to potential user situations that may arise.
Synthetic training data is critical in developing machine learning models. The quantity and quality of training data can affect the performance of these models. With synthetic data generation, businesses can generate large volumes of diverse, high-quality training data that represents everyday scenarios. This further equips data scientists to fine-tune their models effectively, leading to better predictions and outcomes. Data models can assess previously used training data to optimize it for future applications. Data models can use synthetic data as a refinement for existing training data sets to root out negative iterations.
Synthetic Data Generation
When selecting the best method for generating synthetic data, it is important to first consider what type of synthetic data an organization aims to have. There are three general categories to choose from, each offering different benefits and drawbacks:
- Fully synthetic: This data does not include any original data. This indicates that re-identifying any single unit is almost impossible, and all variables in the data are still fully available.
- Partially synthetic: In this data, only sensitive data is substituted with synthetic data. This demands a heavy dependency on the imputation model. It can lead to decreased model dependence. However, this does imply that disclosure is possible due to the true values that remain within the dataset.
- Hybrid Synthetic: Hybrid synthetic data is derived from real and synthetic data. In a hybrid synthetic dataset, the underlying distribution of original data is investigated, and the nearest neighbor of every data point is formed. A near-record in the synthetic data is selected for each record of real data, and the two are converged to generate hybrid data.
There are two broad strategies to build synthetic data. They are as follows:
- Drawing numbers from a distribution: This approach involves observing real statistical distributions and reproducing fake data. This can include the creation of generative models.
- Agent-based modeling: To achieve synthetic data in this method, a model is developed that explains an observed behavior, and then random data is produced using the same model.
Synthetic Data Use Cases
Diverse industries and sectors are benefiting from the integration of synthetic data. From healthcare to fraud detection, synthetic data is equipping applications almost everywhere.
- Machine Learning: Synthetic data is employed to train machine learning systems when real data is expensive and poses privacy risks.
- Healthcare: Within a highly regulated industry like healthcare, synthetic data can help practitioners and researchers access valuable insights without violating their patient privacy.
- Finance: Synthetic data can be utilized to predict financial trends, test algorithms, and ensure compliance with regulations.
- Retail and Marketing: Businesses are using synthetic data to optimize pricing strategies, understand consumer behavior, and enhance marketing automation.
- Automotive: Synthetic data is important in developing self-driving vehicles, as it authorizes extensive testing and validation without the need for real-world testing.
Challenges of Synthetic Data
Despite the numerous advantages of synthetic data, it has some limitations.
- Creating accurate and representative synthetic data can be a challenging task.
- There are concerns about the validity of the generated data when compared to real-world data.
- Synthetic data generation tools are still evolving, which indicates there is room for improvement in accuracy and efficiency.
- Synthetic data presents a range of advantages and use cases for businesses of all sizes.
- By leveraging synthetic data, they can overcome limitations associated with sensitive data, enhance data privacy, and discover new opportunities.
There are different tools and services that can help businesses take advantage of synthetic data. Integrating synthetic data into business strategy can further help unlock new insights, optimize operations, and make informed decisions.
Benefits of Synthetic Data
The ability to generate diverse data is a key benefit of synthetic data. By creating synthetic data that imitates the characteristics of real-world data, enterprises can test their systems for different scenarios, thereby ensuring that they are robust and reliable. This can be useful across industries where access to real-world data is limited or poses privacy risks.
Let's understand some of the key advantages of integrating synthetic datasets into business operations:
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Cost-effective
Generating synthetic data is more cost-efficient than collecting real data. It does not require the same resources or effort.
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Data privacy
Synthetic data equips businesses to comply with data regulations and protect sensitive customer data. Not having to deal with the privacy concerns or legal complications that often arise with real-world data indicates fewer hurdles for the company to use data.
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Scalability
Synthetic data can be developed in large volumes. This provides more opportunities for testing and training machine learning models. With the right algorithms, organizations can work on training models, and an output generator can further help create infinite synthetic data for ongoing use.
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Diversity of data
Businesses can test their systems across different scenarios by generating various synthetic data. Synthetic data generation can help produce diverse datasets that represent realistic situations that probably would not have been able to be sourced from authentic data.
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Reduction of bias
Data bias poses a big concern for any organization as it does not accurately represent insights. However, data bias can be removed by generating synthetic data carefully designed to be representative and unbiased.
The Future of Synthetic Data Generation
Synthetic data generation processes are evolving rapidly. The following areas pledge to introduce innovation that delivers better business outcomes.
- Synthetic data operations: Artificial data generation is just one of the significant steps in the synthetic data lifecycle. Data teams are seeking new solutions to manage and automate the entire synthetic data lifecycle.
- Improved data quality and reliability: Data professionals rely on high-quality data for their workloads. Due to this, synthetic data companies are being driven to optimize their synthetic data generation algorithms, and new solutions are emerging that will help generate vertical-specific synthetic data.
- Ethical and legal perspectives: With the growth in synthetic data, regulators are paying more attention to its ethical implications. Businesses need to be aware of these growing concerns and take them into account as they develop.
- Integration with production data: By integrating artificial data with real-life data, data teams can generate more comprehensive datasets. Fake or artificial data can be used to close gaps in actual datasets and augment real-life details to cover a broader scope of edge cases. They can also create test data to cover the new application functionality that is being developed.
Summary - Synthetic Data Generation
With increasingly stringent data privacy laws and the ever-increasing complexity of accessing multi-source production data, the need for synthetic data generation and management is rising.
Organizations should not settle for a pointed tool to generate tabular synthetic data for a specific use case. They should rather seek a future-ready solution that can assist in addressing many use cases with the needed accuracy and agility while also managing their entire synthetic data lifecycle.
A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance.
About SG Analytics
SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.
Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
