Synthetic Data – The Master Key to AI’s Future
All the vital news, analysis, and commentary curated by our industry experts.
Synthetic Data Report Overview
The synthetic data revolutionaries are the secret saviors in the rapidly evolving AI domain, serving a data feast where traditional sources starve. They’re breaking through the shackles of data quality and regulation, becoming the trusty substitutes for AI training. The Startup Series – “Synthetic data: the master key to AI’s future” emphasizes the transformative power of synthetic data and its significance in the AI landscape. Businesses are utilizing synthetic data to train AI and ML models, test software, monetize data, and much more.
The report delves into the dynamic application landscape of synthetic data by startups, highlighting their solutions and disruptive potential. By providing valuable insights into this burgeoning field, GlobalData’s report aids industry stakeholders in identifying promising synthetic data startups and understanding the implications of their technologies on various sectors.
|Synthetic Data Exploration||By Data Type and By Input Type|
|Synthetic Data Use Cases||AI/ML Training, Data Retention, Data Analytics, Simulations, Benchmarking, Software Testing, Data Sharing, and Data Monetization.|
|Synthetic Data Venture Capitalist Investors||AURAML, betterdata, Nyris, Hazy, Infinity AI, PRIVATE AI, Parellel Domain, and Diveplane|
|Synthetic Data Industry||Healthcare, Financial Services, Automotive, Retail, and Telecom|
Synthetic Data Dynamics
Synthetic data has emerged as a game-changing catalyst for AI development. Synthetic data is algorithmically generated artificial data which captures patterns, distributions, and relationships of real data. Its unique benefits can train AI models more accurately and facilitate faster turnaround times for AI testing. The synthetic data proves to be compliant, scalable, unbiased, cost-effective, and customizable.
Business leaders are increasingly exploring partial and hybrid synthetic data solutions as data regulation gets stringent. Synthetic data has been further explored by data type and by input type.
By data type: Synthetic data explored by data type includes text, media, and tables or data logs.
By input type: Partially synthetic, fully synthetic, and hybrid synthetic are some of the key input types for exploring synthetic data.
For more insights on the synthetic data dynamics, download a free sample report
Synthetic Data Use Cases
Businesses are deploying synthetic data for a wide range of use cases such as AI/ML training, data retention, data analytics, simulations, benchmarking, software testing, data sharing, and data monetization.
Data Monetization: Synthetically generated datasets can be shared and monetized with 3rd parties without any regulatory or real-data-related contractual issues.
AI/ML Training: Synthetic data is customizable, cost-effective, and can be fine-tuned for training AI and ML models.
Data Retention: Organizations can retain synthetic data for a longer time without violating any data retention-related regulatory guidelines.
For additional insights on the synthetic data use cases, download a free sample report
Synthetic Data - Venture Capitalists Investors
Some of the key venture capitalist investors engaging in leveraging the benefits of synthetic data are AURAML, betterdata, Nyris, Hazy, Infinity AI, PRIVATE AI, Parellel Domain, and Diveplane among others.
To get more information on the VC investors in the synthetic data domain, download a free sample report
Synthetic Data Industry Startup Landscape
Various industries such as healthcare, financial services, automotive, retail, and telecom among others are witnessing the emergence of synthetic data startups.
Healthcare: AINDO, betterdata, TONIC, SYNTEGRA, YData, Sarus are some of the emerging startups offering synthetic data in the healthcare industry.
For more synthetic data startups across several industries, download a free sample report
GlobalData’s Startup Series – “Synthetic data: the master key to AI’s future” emphasizes the transformative power of synthetic data and its significance in the AI landscape. The report delves into the dynamic application landscape of synthetic data by startups, highlighting their solutions and disruptive potential. By providing valuable insights into this burgeoning field, GlobalData’s report aids industry stakeholders in identifying promising synthetic data startups and understanding the implications of their technologies on various sectors.
Reasons to Buy
- Stay updated: The field of synthetic data is rapidly evolving with game-changing innovations, notably from startups.
- Discover new startups: The report covers promising startups that are working on groundbreaking technologies and solutions.
- Identify emerging applications: Identify different application areas of synthetic data.
- Learn about products: Deep-dive into synthetic data startups leveraging cutting-edge technologies to offer distinct products.
- Assess investment potential: Access valuable information for assessing the investment potential of different startups in synthetic data.
Octopize - Mimethik Data
Sky Engine AI
Table of Contents
1. Rise of synthetic data
-Generative AI models
-Emerging use cases
-Twitter buzz of startups
-VC investments into startups
2. Key startup profiles
Frequently asked questions
What are the different synthetic data exploration methods?
Synthetic data is explored by input type and data type.
Which are the key synthetic data use cases?
AI/ML training, data retention, data analytics, simulations, benchmarking, software testing, data sharing, and data monetization are the key synthetic data use cases.
Who are the leading venture capitalists investing in the synthetic data industry?
AURAML, betterdata, Nyris, Hazy, Infinity AI, PRIVATE AI, Parellel Domain, and Diveplane are some of the leading venture capitalists investing in the synthetic data industry.
Which are the key industries deploying synthetic data?
Healthcare, financial services, automotive, retail, and telecom are some of the top industries deploying synthetic data.
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