Innodata Featured in Forrester’s Report on Synthetic Data

The increasing demand for high-quality, privacy-compliant data has put synthetic data in the spotlight, particularly in AI/ LLM model training, analytics, and software testing. Innodata is proud to be mentioned in Forrester’s The State of Synthetic Data, 2024 report, which underscores the growing adoption of synthetic data across industries and highlights vendors in the space — including Innodata.

The Role of Synthetic Data in AI Development

In model training and AI development, synthetic data is artificially generated datasets that mimic real-world data while breaking direct links to sensitive or personally identifiable information. Utilizing synthetic data in model development and AI training addresses critical challenges such as data scarcity, privacy regulations, and bias mitigation. Synthetic data is particularly valuable in scenarios where:

Organizations lack access to sufficient real-world data for AI training.
Privacy concerns limit the use of real data, especially in regulated industries.
Companies need diverse datasets to improve model accuracy and fairness.

Forrester’s Recognition of Innodata in the Synthetic Data Landscape

In Forrester’s report, Innodata is a recognized vendor in the synthetic data generation space. The report highlights how organizations can combine real and synthetic data to scale AI model training and enhance dataset quality.

Innodata is an expert in providing high-fidelity synthetic data solutions to help enterprises improve AI model performance while maintaining compliance with stringent data privacy regulations.

Key Applications of Synthetic Data in Enterprise AI

There are several use cases where synthetic data is making an impact, including:

AI Model Training: Organizations can significantly expand their datasets by generating synthetic data, improving model accuracy and reducing bias in training workflows.

Advanced Analytics: Synthetic data enables organizations to run sophisticated analytics without exposing real customer data, helping industries such as finance, healthcare, and retail improve decision-making.

Software Development and Testing: Using synthetic data for testing eliminates the risk of exposing sensitive production data, making quality assurance and application development more secure and efficient.

Synthetic Data Adoption Across Industries

Synthetic data adoption varies across industries. Sectors such as healthcare, financial services, insurance, education, and the public sector often implement synthetic data at different levels in their data infinitives. Additionally, global privacy teams are actively implementing synthetic data. This growing momentum signifies the increasing role of synthetic data in compliance and data governance initiatives.

Why This Matters for Enterprises

There is an undeniable increase in the adoption of synthetic data, with a majority of global business and technology leaders currently working on initiatives involving this technology. As AI continues to evolve, leveraging synthetic data will be essential for organizations seeking to scale AI solutions while ensuring privacy and compliance.

Innodata’s inclusion in this report recognizes our presence in the field and our commitment to providing innovative synthetic data solutions. As synthetic data adoption accelerates, we remain focused on helping enterprises unlock the full potential of AI with high-quality, privacy-first datasets.

Looking Ahead

With synthetic data becoming a cornerstone of AI development, businesses must navigate challenges such as balancing data fidelity and privacy, eliminating bias, and ensuring regulatory compliance. Innodata addresses these challenges by offering tailored data collection and synthetic data generation services that power AI-driven innovation.

Our synthetic data solutions help enterprises:

Augment real-world data by generating high-quality synthetic variations to enrich AI models with diverse scenarios and edge cases.

Ensure privacy compliance by creating synthetic replicas of sensitive data, enabling secure and compliant model training.

Overcome access barriers by generating synthetic data from restricted domains, unlocking insights previously out of reach.

Develop domain-specific datasets to address industry-specific AI model training needs, ensuring robust and adaptable AI/ML development.

With extensive expertise in text, speech, audio, image, video, and sensor data, Innodata enables enterprises to build high-performing AI models across multiple domains. Our global reach, language capabilities, and industry expertise ensure that we deliver scalable, high-quality datasets tailored to each organization’s unique needs.

To learn more about how Innodata’s data collection and synthetic creation solutions can enhance your AI initiatives, talk to an expert today.

You can find the full Forrester report below (access may require a Forrester account or purchase):

The State Of Synthetic Data, 2024. By Enza Iannopollo, Amy DeMartine, Carlos Casanova, Diego Lo Giudice, Rowan Curran, Georgia Caplice, Michael Belden. Published Nov 04, 2024.

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