Acquiring real-world data can be challenging. Limited availability, privacy concerns, and cost constraints are the usual suspects making life difficult for the average data consumer. Generative AI synthetic data has emerged as a powerful solution to overcome these limitations. However, it’s not enough to add yet another tool to the tech stack. In order to serve the data consumer better, the data architecture also needs to change. 

While traditional approaches involve synthesizing data from centralized storage or data warehouses, a more effective and efficient strategy is to bring generative AI synthetic data closer to the data consumer. In this blog post, we explore the importance of this approach and how it can unlock new possibilities in data-driven applications.

Data consumer limitations: traditional data synthesis

Traditional data synthesis approaches usually rely on centralized storage, creating bottlenecks and delays in data access. The centralized governance model hinders the agility and autonomy of data consumers, limiting their ability to respond to evolving needs quickly. Moreover, traditional synthesis methods need help to scale and accommodate diverse data requirements, making it challenging to meet the specific needs of individual data consumers. The one size fits all approach doesn’t work.

In many organizations, data owners focus on replacing legacy data anonymization processes with generative AI synthetic data to populate lower environments, mistaking data availability for data usability. Generating full versions of their production databases resolves the data accessibility problem but locks the power of generative AI synthetic data. It's crucial to move beyond the mindset of merely replacing original data with synthetic data and instead focus on bringing generative AI synthetic data closer to the data consumer. Not only in terms of proximity but also in terms of usability.

The 3 benefits of generating synthetic data closer to the data consumer

1. Enhanced agility and autonomy

Organizations empower their teams with increased autonomy and agility. Data consumers gain greater control and flexibility in generating synthetic data tailored to their requirements, enabling faster decision-making, experimentation, and innovation.

Generative AI models can upsample minority classes for better representation, downsample high quantities of data for smaller but still representative datasets, and augment the data by filling the gaps in the original data. This level of customization and control allows data consumers to improve overall data quality and diversity and address the following data challenges:

  • Customized data generation 
    Bringing generative AI synthetic data closer to the data consumer allows for customized data generation. Data consumers can define the desired attributes, distributions, and relationships within the synthetic data to align closely with their specific use cases. This customization enables the generation of synthetic data that reflects the unique characteristics and challenges of the target use case, improving the relevance and applicability of the generated data.
  • Adaptable data generation
    Generative AI synthetic data brought closer to the data consumer allows for flexible data generation. As requirements change or new scenarios arise, data consumers can easily modify the generative AI models to produce synthetic data that meets evolving needs. This adaptability enables data consumers to respond rapidly to shifting business demands, emerging trends, or regulatory changes, without relying on external parties or complex data provisioning processes.
  • Empowered data exploration
    Bringing generative AI synthetic data closer to the data consumer allows data exploration and analysis. Data consumers can interact directly with the generative AI models, generating and exploring synthetic data on demand. This direct engagement enables data consumers to gain deeper insights, uncover hidden patterns, and perform exploratory analysis more effectively, fostering data-driven decision-making and innovation.

2. Reduced latency and improved efficiency

Proximity to the data consumer minimizes delays in accessing and synthesizing data. Rather than relying on centralized storage, generative AI models can be deployed closer to the data consumer, ensuring faster generation and synthesis of synthetic data. This reduced latency results in more efficient workflows and quicker insights for data consumers.

  • On-demand data generation
    Generating synthetic data on demand brings significant efficiency and resource utilization benefits. Instead of relying on pre-generated datasets stored in centralized repositories, data consumers can request specific synthetic data subsets or scenarios as needed. This reduces the storage requirements and allows for more targeted and efficient data generation, saving computational resources and time.
  • Scalability via parallelization
    Bringing generative AI synthetic data closer to the data consumer enables scalability via parallelization. Data consumers can distribute the data generation process across multiple systems or utilize cloud computing resources to generate synthetic data in parallel, significantly reducing the time required for large-scale data synthesis. This scalability allows organizations to handle growing data volumes and efficiently meet the increasing demands of data consumers.
  • Streamlined data exploration and experimentation
    The proximity between generative AI synthetic data generators and data consumers streamlines data exploration and experimentation. Data consumers can interact directly with generative AI models to explore various data scenarios, adjust parameters, and iterate quickly. This iterative approach enables faster hypothesis testing, model refinement, and experimentation, driving innovation and accelerating the development of data-driven solutions.

3. Data collaboration and innovation

The proximity between generative AI synthetic data generators and data consumers fosters data collaboration and innovation. Data consumers can work closely with the generative AI model creators, providing feedback and insights to improve the quality and relevance of the synthetic data. This collaborative approach facilitates faster innovation, experimentation, and prototyping, unlocking new possibilities in various domains.

  • Collaborative data exploration
    Generative AI synthetic data brought closer to the data consumer allows for collaborative data exploration. Teams from different departments or disciplines can access and interact with the synthetic data, gaining valuable insights and perspectives. This collaborative environment promotes knowledge sharing, cross-pollination of ideas, and interdisciplinary collaboration, leading to discoveries and innovative solutions.
  • Cross-domain innovation
    Generative AI synthetic data is available closer to the data consumer and encourages cross-domain innovation. Different teams or stakeholders can leverage the synthetic data to explore ideas, concepts, or approaches from their respective domains and apply them to other fields.
  • Innovation democratization
    Bringing generative AI synthetic data closer to the data consumer democratizes innovation. It allows individuals or teams to explore and experiment with data-driven ideas without significant dependencies on external data sources or specialized expertise. This democratization of data access and experimentation fosters a culture of innovation, empowering a more comprehensive range of stakeholders to contribute to problem-solving and decision-making processes.

Data consumers: real-world examples

Healthcare diagnosis and treatment

In healthcare, generating synthetic data from patient data closer to the data consumer can revolutionize diagnostic and treatment research. Researchers and data scientists can utilize generative AI models to create synthetic patient data that captures a wide range of medical conditions, demographics, and treatment histories embedded in the real data. This synthetic data can be used to train and validate predictive models, enabling more accurate diagnosis, personalized treatment plans, and drug development without compromising patient privacy or waiting for access to original patient data. A healthcare data platform populated with synthetic health data can empower data consumers even outside the organization, like in the case of Humana's synthetic data exchange, accelerating innovation, research and development.

Financial fraud detection

In the financial industry, synthetic data generated from privacy sensitive financial transaction data brought closer to the data consumer can significantly improve fraud detection capabilities. Financial institutions can train machine learning models to identify and prevent fraudulent transactions by generating synthetic data representing various fraudulent activities, including upsampling fraud patterns. Using this upsampled synthetic data, organizations can stay ahead of evolving fraud techniques without compromising the privacy and security of original customer data.

Data consumers & synthetic data

There needs to be more than the traditional approach of data synthesis from centralized storage or data warehouses to meet the evolving needs of organizations. Bringing generative AI synthetic data closer to the data consumer offers a paradigm shift in data synthesis. More autonomy, less latency, improved privacy, and higher levels of customization are all among the benefits. Organizations must embrace this approach to promote collaboration, experimentation, and innovation, empowering organizations to unlock new possibilities and leverage the full potential of synthetic data.

By bringing generative AI synthetic data closer to the data consumer, we can embark on a transformative journey that empowers data consumers and accelerates the development of intelligent applications in various industries.