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Creating New Frontiers with Synthetic Data Solutions

The Growth Potential of Synthetic Data

While synthetic data is not necessarily new, it may be a solution with current data scarcity issues along with the development of AI. Just what is synthetic data? Cleanlab defines it in their blog, “Navigating the Synthetic Data Landscape: How to Enhance Model Training and Data Quality,”: “Synthetic data is artificially generated data that mimics real-world data’s structure and statistical properties. The main advantage of synthetic data is its usage as a Privacy Enhancing Technique (PET). PETs are collections of software and hardware tools/approaches that do all the data processing while protecting the confidentiality, integrity and availability.” The privacy of such data also makes it ideal in specific industries, such as healthcare and financial services.

With the advent of artificial intelligence systems, synthetic data is seemingly coming into its own, yet along with the potential there are also challenges. Cleanlab suggests several ways that companies can meet those challenges:

  1. Evaluating synthetic data quality. Data quality is a complex concept encompassing several factors: accuracy, consistency, completeness, and reliability. When evaluating and monitoring synthetic data produced by data-gen tools, it is essential to consider various criteria such as class distribution, inconsistencies, and similarity to real data.
  2. Validate and review synthetic datasets regularly. Synthetic datasets, by nature, are artificial constructs that approximate the characteristics of real-world data. As such, they must be subjected to continuous scrutiny to ensure they have not drifted from their intended representativeness. One should utilize dataset monitoring using visualization tools that can be used to monitor distribution of features and feature drift analysis.
  3. Implement model audit processes. Model audits are a crucial aspect of working with synthetic datasets. Regular model audits can uncover biases in the synthetic dataset used to train the model. You should measure accuracy, bias, and error rates. Several audit tools allow you to assess more fine-grained aspects of model performance.
  4. Use multiple data sources. Using multiple data sources will increase the diversity of the generated synthetic dataset, making it representative of real-world data. The same diversity will also fill gaps in the dataset, adding newer dimensions.

Exploring the Data Pipeline

In “Defining Governance to Deliver Data Benefits,” All Things Insights examined big data. The rise of artificial intelligence and other technological developments have kept the analytics and data science community front and center as organizations aim to grapple with this influx of data. Whether it’s a data science team or by automated means, just how to manage, monitor and organize data on a daily basis becomes an important issue. Data democratization is becoming a prevalent trend, as organizations look to empower their employees, from experienced data professionals to novices, with data-driven insights. Another bedrock principle is data governance. Keeping data secure, private and maintaining high quality data standards are a must-have for any data-driven company.

Looking forward to TMRE 2024? The conference, which will be held October 8 to 10, will feature the session, “Navigating the Synthetic Data Landscape: Unleashing New Frontiers in Market Research,” presented by Yogesh Chavda, Director, Center for Marketing Solutions at University of South Carolina. This presentation will share the landscape of synthetic data, shedding light on its fundamentals, generation processes, and ethical implications. Chavda will explore the transformative role of synthetic data in shaping the future of market research, offering detailed insights into its applications for training AI models, facilitating privacy-compliant data sharing, and bolstering consumer testing. By dissecting the advantages and addressing the challenges—including bias and accuracy concerns—this talk aims to unveil the full potential of synthetic data as a pivotal tool for innovation. Furthermore, he will peer into the horizon, discussing emerging trends, ethical considerations, and the evolving regulatory framework surrounding synthetic data. Register for TMRE 2024 here.

Identifying Synthetic Data Benefits

Synthetic data is increasingly becoming a valuable resource for insights professionals. According to ChatGPT, here are the top benefits of using synthetic data:

  1. Data Privacy and Security: One of the primary advantages of synthetic data is its ability to protect privacy. Since synthetic data is artificially generated and does not contain real personal information, it eliminates the risk of data breaches and ensures compliance with privacy regulations such as GDPR and CCPA.
  2. Access to Rich and Diverse Datasets: Synthetic data can be generated to simulate a wide range of scenarios and demographic variations that might be underrepresented in real-world data. This allows insights professionals to have a more comprehensive dataset, improving the robustness and reliability of their analyses.
  3. Cost Efficiency: Generating synthetic data can be more cost-effective than collecting and managing real-world data, especially when large volumes of data are needed. It reduces the need for extensive data collection campaigns, which can be expensive and time-consuming.
  4. Accelerated Development and Testing: Synthetic data can be created quickly, enabling faster development cycles for testing and refining models. This is particularly beneficial for training machine learning algorithms and conducting simulations without waiting for real-world data collection.
  5. Elimination of Bias: Real-world data often contains inherent biases that can skew analysis and insights. Synthetic data can be designed to minimize or eliminate these biases, providing a more balanced dataset that leads to fairer and more accurate insights.
  6. Scalability: Synthetic data generation can easily scale to produce large datasets required for extensive testing and analysis. This is particularly useful for big data applications where vast amounts of data are needed to train algorithms effectively.
  7. Flexibility and Customization: Synthetic data can be tailored to specific needs and scenarios. Insights professionals can generate data that meets precise specifications, ensuring that the data aligns perfectly with the objectives of their analysis.
  8. Enhanced Innovation and Experimentation: With synthetic data, insights professionals can experiment with new ideas and methodologies without the risk of compromising sensitive information. This fosters a culture of innovation, as researchers can explore various hypotheses and scenarios freely.
  9. Improved Model Performance: For machine learning and predictive modeling, synthetic data can help improve model performance by providing a diverse and extensive training set. This leads to more generalized and accurate models that perform well on real-world data.
  10. Data Augmentation: Synthetic data can be used to augment real-world datasets, providing additional examples that can improve the robustness of analytical models. This is especially useful in cases where collecting more real data is impractical or impossible.
  11. Handling Edge Cases: Synthetic data allows the creation of rare or edge cases that might not be present in real-world data but are critical for testing and validating models. This ensures that models can handle a wide range of scenarios, including unexpected ones.
  12. Time-Efficient: Synthetic data can be generated on-demand, saving time compared to the lengthy processes of data collection, cleaning, and preparation associated with real-world data.

A Synthetic Solution to a Real-World Dilemma

As data science becomes more complex, the world of synthetic data offers insights professionals various benefits, including enhanced privacy and security, cost efficiency, scalability, elimination of bias, and the ability to experiment and innovate freely. Of course, there are also challenges such as data quality. AI generated systems may be the key to such concerns. “Synthetic data offers a promising avenue for overcoming many data-related challenges, but it is critical to approach it by focusing on data quality. In the era of data-centric AI, quality trumps quantity,” says Cleanlab.

Yet by leveraging synthetic data, insights professionals can gain deeper, more accurate insights and drive more effective decision-making in industries ranging from healthcare to finance. As Solomon Partners puts it in their blog, “Navigating the Synthetic Data Landscape,” “The future of synthetic data in AI and data analytics will be driven by advances in technology that enhance its sophistication, diversity, and realism. However, the challenges of ensuring accuracy and avoiding data misrepresentation loom large, necessitating meticulous validation against real-world scenarios to prevent skewed outcomes.”

Video courtesy of IBM Technology

Contributor

  • Matt Kramer

    Matthew Kramer is the Digital Editor for All Things Insights & All Things Innovation. He has over 20 years of experience working in publishing and media companies, on a variety of business-to-business publications, websites and trade shows.

    View all posts

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