Syntho - Detailed Review
Data Tools
Syntho - Product Overview
Syntho Overview
Syntho is a comprehensive AI-driven platform specialized in generating synthetic data, making it an invaluable tool in the data tools category. Here’s a brief overview of its primary function, target audience, and key features:
Primary Function
Syntho’s primary function is to generate high-quality synthetic data that mimics the statistical patterns of original data. This is achieved using artificial intelligence, ensuring that the synthetic data is accurate, private, and compliant with data protection regulations. The platform is designed to protect sensitive information by removing or modifying personally identifiable information (PII) and other sensitive data types.
Target Audience
Syntho is geared towards enterprise clients across various industries, including Healthcare, Pharma, Finance, Government, and Manufacturing. It is particularly useful for organizations that need to create, maintain, and control representative test data for non-production environments without compromising data privacy.
Key Features
AI-Generated Synthetic Data
Syntho uses AI to generate synthetic data that mirrors the statistical patterns of the original data. This includes time series data and the ability to upsample minority classes.
Data Protection
The platform includes a PII Scanner to automatically identify and protect sensitive information. It also offers synthetic mock data and consistent mapping to preserve referential integrity in relational data ecosystems.
Deployment Flexibility
Syntho can be deployed in various environments, including on-premise, private clouds (such as AWS, Azure, Google Cloud), and Syntho’s own cloud. This ensures that sensitive data never leaves the customer’s trusted environment.
Connectors and Integration
Syntho provides out-of-the-box connectors to connect with leading databases and filesystems, supporting over 20 database connectors and 5 filesystem connectors.
Quality Assurance
The synthetic data generated by Syntho is assessed and approved by data experts from SAS, ensuring high accuracy, privacy, and speed.
Test Data Management
The platform allows for the creation of representative test data for non-production environments through de-identification and synthetization. It also supports rule-based synthetic data generation and subsetting of relational databases.
Overall, Syntho offers a comprehensive solution for synthetic data generation, data protection, and test data management, making it a valuable asset for organizations needing to handle sensitive data securely and efficiently.
Syntho - User Interface and Experience
User Interface Overview
The user interface of Syntho, an AI-driven synthetic data generation platform, is designed with a focus on ease of use and a seamless user experience.Deployment and Connectivity
Syntho deploys within the customer’s secure environment, ensuring that sensitive data never leaves the trusted environment. The platform offers out-of-the-box connectors that allow users to connect to various databases and filesystems, supporting over 20 database connectors and 5 filesystem connectors. This makes it straightforward to link to the source data and target environments.User-Friendly Workflow
The process of generating synthetic data is streamlined into clear steps:1. Deploy in your environment
Ensure the platform is set up within your secure environment.2. Connect to your database
Use the provided connectors to link to your data sources.3. Generate your data
Define the type of synthetization needed, whether it’s AI-generated synthetic data, de-identification, or rule-based synthetic data. The platform automatically detects sensitive data types and allows for realistic masking or synthesis of new values.4. Share/Use the protected data
Utilize and share the protected data securely, maintaining compliance and privacy throughout its usage.Intuitive Synthetization Process
Users can easily configure AI-generated synthetic data using a user-friendly interface. For instance, to synthesize data, users simply drag the target table into the “Synthesize” section in the workspace. This drag-and-drop functionality simplifies the process of generating synthetic data that mimics the statistical patterns and relationships of the original data.Quality Assurance and Feedback
Syntho provides a quality assurance report that assesses the generated synthetic data on accuracy, privacy, and speed. This report helps users ensure that the synthetic data meets their requirements and maintains the necessary level of privacy and compliance.Overall User Experience
The platform is engineered to be easy to use, even for users who may not have extensive technical backgrounds. The clear, step-by-step process and intuitive interface make it accessible for a wide range of users. Additionally, the support for various data types and complex structures, such as time series data, ensures that the platform can handle diverse data needs efficiently. In summary, Syntho’s user interface is characterized by its simplicity, connectivity options, and a clear workflow that guides users through the process of generating and using synthetic data, all while ensuring high accuracy and privacy standards. Syntho - Key Features and Functionality
Syntho: An Advanced AI-Driven Platform
Syntho is an advanced AI-driven platform for generating synthetic data, offering a range of key features and functionalities that make it a comprehensive tool for data management and privacy protection.
AI-Generated Synthetic Data
Syntho uses artificial intelligence to generate synthetic data that mimics the statistical patterns of the original data. This process involves creating new datapoints that preserve the characteristics, relationships, and statistical patterns of the original dataset, making the synthetic data highly realistic and usable as if it were the original data.
Protection of Sensitive Information
The platform includes features to protect sensitive information by removing or modifying personally identifiable information (PII). Syntho’s AI-powered PII Scanner automatically identifies PII, and the system can substitute sensitive PII, PHI, and other identifiers with synthetic mock data. This ensures that the generated data maintains referential integrity within the entire relational data ecosystem.
Quality Assurance and Evaluation
Syntho provides a quality assurance report that assesses the generated synthetic data on accuracy, privacy, and speed. Additionally, the synthetic data is evaluated and approved by data experts from SAS, ensuring high standards of quality and reliability.
Time Series Data Synthesis
The platform is optimized to synthesize time-series data accurately. This is particularly useful for datasets that involve temporal relationships and patterns, ensuring that the synthetic data retains the same temporal characteristics as the original data.
Upsampling and Subsetting
Syntho offers upsampling capabilities to increase the minority class in a dataset to a desired amount. It also allows for subsetting, where large datasets can be reduced to create smaller, representative subsets while maintaining referential integrity.
Rule-Based Synthetic Data Generation
In addition to AI-generated data, Syntho supports rule-based synthetic data generation. This allows users to generate synthetic data based on predefined rules and constraints, which can be useful for mimicking real-world or targeted scenarios.
De-identification and Test Data Management
The platform enables the creation, maintenance, and control of representative test data for non-production environments. This involves de-identification and synthetization of data to generate test data that is comprehensive and representative of real scenarios.
Data Type Support
Syntho supports a wide range of data types, including structured tabular data (categorical, numerical), geographic location data, time series data, multi-table databases with referential integrity, and open text data. This versatility makes it suitable for handling complex data structures and various data formats.
On-Premise Deployment
For organizations with strict security requirements, Syntho supports on-premise deployment, ensuring that all features are available in an environment that meets the organization’s security standards.
Conclusion
In summary, Syntho integrates AI to generate high-quality synthetic data that is accurate, private, and representative of the original data. Its comprehensive features ensure that sensitive information is protected, and the platform is versatile enough to handle various types of data and deployment scenarios.
Syntho - Performance and Accuracy
Performance and Accuracy
Syntho’s synthetic data has been rigorously evaluated by SAS data experts, who found that it performs highly similarly to real data. Here are some key findings:
Model Performance
Models trained on Syntho’s synthetic data showed accuracy and performance metrics, such as the Area Under the Curve (AUC), that were consistent with those trained on original data. This indicates that synthetic data can effectively replace real data for model training without significant loss in performance.
Statistical Patterns
The synthetic data generated by Syntho captures both basic and deep statistical patterns present in the original data. This includes preserving correlations and relationships between variables, which is crucial for advanced analytics tasks.
Variable Importance
The variable importance, which reflects the predictive power of variables in a model, remained intact when comparing synthetic data to the original dataset. This suggests that the insights derived from synthetic data are reliable and consistent with those from real data.
Flexibility and Customization
Syntho offers significant flexibility in generating synthetic data, allowing organizations to create datasets that match their specific needs. This includes:
Custom Data Types
Syntho supports generating synthetic data across various complex data types, such as time-series data and large multi-table datasets. Users can define specific conditions to produce datasets that closely match their unique requirements.
Data Management
The platform is useful for test data management, enabling the creation of realistic non-production environments that mirror actual data without exposing sensitive information. This is particularly beneficial for industries like healthcare and finance where privacy is a major concern.
Limitations and Areas for Improvement
While Syntho’s synthetic data shows promising performance and accuracy, there are some limitations and areas to consider:
Dependency on Real Data Quality
The quality of the synthetic data is heavily dependent on the quality and diversity of the real dataset it is modeled after. If the original dataset lacks quality, the synthetic data generated may also be flawed.
Accuracy and Representation Issues
Not all synthetic data generation tools ensure the preservation of statistical properties and referential integrity of real data. Therefore, thorough validation and stress tests are necessary to ensure the reliability of the synthetic data.
Generative AI Hallucinations
AI algorithms can sometimes produce misleading or incorrect data points that appear statistically sound. Regular human reviews are essential to catch these anomalies and maintain data integrity.
Amplified Anomalies
If the original data contains anomalies or outliers, there is a risk that the synthetic versions could either exaggerate these issues or obscure them. This can affect the generalization and performance of models trained on this data.
To address these challenges, Syntho provides features such as quality assurance reports, PII scanners, and the ability to adjust synthetic data generation rules. These tools help ensure that the synthetic data meets high standards of accuracy, privacy, and speed.
In summary, Syntho’s synthetic data demonstrates strong performance and accuracy, making it a viable alternative to real data for various applications. However, it is crucial to ensure the quality of the original dataset and to implement thorough validation processes to maintain the integrity of the synthetic data.
Syntho - Pricing and Plans
The pricing structure for Syntho’s AI-driven synthetic data product is based on a feature-based model, with no consumption-based charges. Here’s a breakdown of the different plans and their features:
Pricing Tiers
Syntho offers three main pricing tiers: Basic, Standard, and Ultimate.Basic Plan
- License: Syntho Engine License
- Deployment Fee: One free deployment
- Number of Users: Unlimited
- Connectors: One connector
- Features:
- PII Scanner open text
- Mockers
- Consistent mapping
- Time-series
- Up sampling
- Support:
- Documentation
- Ticket system
- Communication channel
Standard Plan
- License: Syntho Engine License
- Deployment Fee: One free deployment
- Number of Users: Unlimited
- Connectors: Two connectors
- Features: All features included in the Basic plan
- Support: Same as the Basic plan
Ultimate Plan
- License: Syntho Engine License
- Deployment Fee: One free deployment
- Number of Users: Unlimited
- Connectors: Unlimited connectors
- Features: All features included in the Standard plan
- Support: Same as the Basic and Standard plans, with comprehensive support options
Key Points
- No Consumption-Based Charges: The pricing does not depend on the amount of synthesized data generated. You can use synthetic data as much as needed without additional costs.
- Deployment: While the initial deployment is free, additional deployments in multiple locations will incur extra costs.
- Scalability: The licensing model is scalable, allowing businesses to upgrade or adjust their license tiers as they grow.
Free Options
There is no mention of a free plan or trial specifically for the data synthetization product on the Syntho website. However, Syntho does offer a platform evaluation period as part of their licensing agreements, which can help customers assess the product before committing to a full license. Syntho - Integration and Compatibility
Syntho Overview
Syntho, the AI-driven synthetic data generation platform, is designed to integrate seamlessly with a variety of tools and systems, ensuring broad compatibility and ease of use.
Deployment Options
Syntho can be deployed in several environments to meet different needs:
- On-premise: For organizations that prefer to keep their data within their own infrastructure.
- Any private cloud: Such as AWS, Azure, or Google Cloud, allowing flexibility in cloud environments.
- Syntho cloud: For those who prefer a cloud-based solution managed by Syntho.
- Other environments: Syntho can be adapted to fit into any other environment where the customer’s data is stored.
Database and Filesystem Connectors
Syntho supports a wide range of database and filesystem connectors, making it easy to connect to source and target environments. It includes connectors for over 20 leading databases and 5 filesystems, ensuring compatibility with most data storage solutions.
Integrations
Syntho integrates with various software and services, including:
- Kubernetes
- Amazon S3
- Java
- Apache Hive
- MySQL
- Amazon Web Services (AWS)
- MongoDB
- Docker
These integrations enable smooth interaction with different technologies and platforms, enhancing the usability and versatility of the Syntho platform.
Data Management
Syntho allows users to connect to source data stored in databases or filesystems and generate synthetic data that can be written to target environments. This process is facilitated by out-of-the-box connectors that simplify the connection and data transfer process.
Security and Compliance
To ensure data security and compliance, Syntho deploys within the customer’s safe environment, so sensitive data never leaves the trusted environment. This approach protects personally identifiable information (PII) by removing or modifying it, and the synthetic data is assessed and approved by data experts from SAS.
Conclusion
In summary, Syntho’s integration capabilities and compatibility across various platforms and devices make it a versatile and secure solution for generating and managing synthetic data. Its ability to connect with a wide range of databases, filesystems, and cloud services ensures that it can be easily integrated into existing workflows.
Syntho - Customer Support and Resources
Customer Support
Syntho does not provide explicit details on direct customer support contact methods on their website. However, for general inquiries or issues related to their product, you can typically reach out to companies through their main contact channels. Here are some indirect ways you might consider:
Contact Form or Email
While specific support email addresses are not mentioned, you can try contacting them through their general contact form or by reaching out to their main email address if available.
Partners and Clients
Syntho collaborates with several enterprise clients globally, such as Cedars-Sinai, Erasmus MC, and others. If you are associated with these organizations, you might be able to get support through these channels.
Additional Resources
Syntho offers several resources to help users get the most out of their synthetic data generation platform:
Documentation
Syntho provides user documentation that includes detailed guides on how to use their platform. This documentation can be accessed through their website and covers various aspects of synthetic data generation, data monetization, and more.
Product Demos
Users can create product demos using AI-generated synthetic data. This feature helps in showcasing the capabilities of the product without compromising sensitive information.
Quality Assurance Reports
Syntho generates quality assurance reports to assess the accuracy, privacy, and speed of the generated synthetic data. This ensures that the synthetic data meets the required standards.
PII Scanner and Data Protection
The platform includes an AI-powered PII Scanner to identify and protect sensitive information by removing or modifying personally identifiable information (PII). This ensures data privacy and compliance.
Webinars and Tutorials
Although not explicitly mentioned, many companies in the data tools sector offer webinars, tutorials, or blog posts to educate users about their products. You might find such resources on Syntho’s blog or by subscribing to their newsletter.
For more detailed information, you would need to contact Syntho directly or explore their website further for any additional resources they may offer.
Syntho - Pros and Cons
Advantages
Greater Control and Customization
Syntho allows organizations to create synthetic datasets that match their specific needs, ensuring consistency and coverage of rare scenarios that might be missing in real-world data. This flexibility enables the simulation of diverse time-based scenarios and handling of structured tabular data, which is particularly useful for test data management and creating realistic non-production environments without exposing sensitive information.Enhanced Privacy and Security
Synthetic data generated by Syntho significantly enhances privacy and security, especially in sensitive industries like healthcare and finance. By mimicking the statistical properties of real datasets without disclosing actual personal details, organizations can conduct analyses and develop AI models without the risk of sensitive data leaks. This approach complies with various data privacy laws such as GDPR and HIPAA.Improved Machine Learning Performance
Synthetic data helps in balancing datasets, addressing issues like data imbalance and limited examples of rare events. For instance, in AI-driven fraud detection, synthetic data can generate additional samples similar to real fraud cases, improving the performance of machine learning models without compromising privacy.Faster Data Access and Development
Synthetic data generation speeds up the development process by quickly producing high-quality datasets for analytics, research, and machine learning applications. This reduces the time spent on data collection, cleaning, and preparation, allowing teams to focus more on analysis and model development.Data Compatibility and Scalability
Synthetic data can be created in various formats, ensuring seamless integration into existing workflows and tools. It also allows for the generation of unlimited data for a fixed price, making it ideal for scaling datasets quickly to meet the needs of various projects.Disadvantages
Dependency on Real Data Quality
The effectiveness of synthetic data heavily relies on the quality and diversity of the real dataset it is modeled after. If the original dataset lacks quality, the synthetic data generated will likely be flawed, resulting in ineffective outcomes.Accuracy and Representation Issues
Not all synthetic data generation tools ensure the preservation of the statistical properties and referential integrity of real data. This can lead to inaccurate predictions and misguided analyses. Regular validation and human reviews are necessary to catch anomalies and ensure reliability.Generative AI Hallucinations
AI algorithms used to generate synthetic data can sometimes produce misleading or incorrect data points that appear statistically sound. This requires regular human oversight to identify and correct such anomalies.Computational Resources and Expertise
Synthetic data generation, especially using complex algorithms, demands significant computational resources and expertise. This can be a limitation for teams needing quick data access for testing and development.Potential Lack of Realism
Synthetic data may not capture the full complexity and nuances of real-world datasets, potentially omitting important details or relationships needed for accurate predictions. Ensuring the data generation model is well-calibrated is crucial to mitigate this issue. By weighing these advantages and disadvantages, organizations can make an informed decision about whether Syntho’s synthetic data generation platform aligns with their specific needs and goals. Syntho - Comparison with Competitors
Unique Features of Syntho
- Comprehensive Synthetic Data Generation: Syntho offers a broad range of synthetic data generation approaches, including fully AI-generated synthetic data, synthetic mock data, and rule-based synthetic data. This versatility allows users to mimic statistical patterns of original data, protect sensitive information by removing or modifying personally identifiable information (PII), and create representative test data for non-production environments.
- AI-Powered PII Scanner: Syntho’s AI-powered PII Scanner automatically identifies and modifies sensitive data, ensuring data privacy and compliance. This feature is particularly valuable in privacy-conscious sectors like healthcare and finance.
- External Validation: Syntho’s synthetic data is assessed and approved by data experts at SAS, providing an additional layer of quality assurance on accuracy, privacy, and speed.
- Time Series and Subsetting: Syntho can synthesize time-series data accurately and reduce records to create smaller, representative subsets of relational databases while maintaining referential integrity.
Potential Alternatives
Domo
While Domo is more focused on end-to-end data platforms and AI-enhanced data exploration, it does not specialize in synthetic data generation. However, its comprehensive suite of tools and AI service layer could be beneficial for users needing broader data management and analysis capabilities. Domo’s strengths include its ability to deliver data insights through AI-enhanced exploration and pre-built AI models for forecasting and sentiment analysis.
Tableau
Tableau, a leading business intelligence platform, uses AI to enhance data analysis and preparation but does not specifically focus on synthetic data generation. Tableau’s AI capabilities, such as Tableau GPT and Tableau Pulse, are more geared towards intuitive data analysis and visualization. If your primary need is advanced data visualization and analysis rather than synthetic data, Tableau might be a better fit.
IBM Cognos Analytics
IBM Cognos Analytics offers AI-powered automation and insights, including automated pattern detection and natural language query support. However, it is not specialized in synthetic data generation. This tool is more suited for creating dashboards, reports, and making data-driven decisions based on real-time insights. It has a complex interface and a steep learning curve, which might not be ideal for all users.
AnswerRocket
AnswerRocket is a search-powered AI data analytics platform that allows users to ask questions in natural language to get rapid insights. While it is user-friendly and suitable for business users without technical expertise, it lacks the advanced features and functionalities related to synthetic data generation. If your focus is on quick insights and report generation through natural language queries, AnswerRocket could be an alternative, but it does not replace the specialized synthetic data capabilities of Syntho.
Summary
Syntho stands out for its specialized focus on synthetic data generation, offering a range of methods to create and manage synthetic data while ensuring data privacy and accuracy. For users who need a comprehensive solution specifically for synthetic data, Syntho is a strong choice. However, if your needs extend beyond synthetic data to broader data management, analysis, and visualization, alternatives like Domo, Tableau, or IBM Cognos Analytics might be more suitable.
Syntho - Frequently Asked Questions
Frequently Asked Questions about Syntho
What is Syntho and what does it do?
Syntho is an AI-generated synthetic data platform that helps accelerate data-driven solutions. It generates synthetic data twins that mimic the statistical patterns, characteristics, and relationships of original data, ensuring high accuracy and privacy.
What types of synthetic data can Syntho generate?
Syntho supports multiple approaches to synthetic data generation, including fully AI-generated synthetic data, synthetic mock data, and rule-based synthetic data. This allows users to generate data that accurately represents real-world or targeted scenarios.
How does Syntho handle sensitive information like PII?
Syntho protects sensitive information by automatically detecting and masking personally identifiable information (PII) using its AI-powered PII Scanner. It also supports de-identification and synthetization to ensure that sensitive data is securely managed.
Can Syntho handle large and complex datasets?
Yes, Syntho is optimized to handle massive datasets with minimal computing resources. It supports structured, tabular data, including time series data, multi-table databases with referential integrity, and geographic location data. The platform also auto-scales to synthesize huge databases efficiently.
Does Syntho support various data types and structures?
Syntho supports a wide range of data types, including categorical, numerical, time series, and geographic location data. It also handles multi-table databases and preserves referential integrity through automatic table relationship inference and synthesis.
How accurate is the synthetic data generated by Syntho?
The synthetic data generated by Syntho is assessed and approved by the data experts of SAS. It mimics the statistical patterns and relationships of the original data, ensuring high accuracy and quality. Users can also generate quality assurance reports to evaluate the accuracy, privacy, and speed of the generated synthetic data.
Can Syntho be used for creating test data for non-production environments?
Yes, Syntho helps create, maintain, and control representative test data for non-production environments. It supports de-identification and synthetization to generate test data that is realistic and compliant with privacy regulations.
Does Syntho require significant computational resources?
No, Syntho is optimized to minimize computational requirements. It does not require GPU resources and supports auto-scaling, making it efficient for synthesizing large datasets.
How does Syntho connect to databases and filesystems?
Syntho provides out-of-the-box connectors that allow users to connect to their source data and target environments. It supports over 20 database connectors and 5 filesystem connectors, making it easy to integrate with various data sources.
What industries does Syntho typically serve?
Syntho serves a variety of industries, including Healthcare, Pharma, Finance, Government, and Manufacturing. It works with enterprise clients globally, such as Cedars-Sinai, Erasmus MC, and LifeLines.
How can I get started with Syntho?
To get started with Syntho, you can connect to your database, define the type of synthetization you need, and then generate and share the protected synthetic data. You can also request a product demo or contact Syntho’s team for more information.
Syntho - Conclusion and Recommendation
Final Assessment of Syntho in the Data Tools AI-Driven Product Category
Syntho is a comprehensive synthetic data generation platform that leverages artificial intelligence (AI) to create high-quality, privacy-compliant data. Here’s a detailed look at what Syntho offers and who would benefit most from using it.
Key Features
- AI-Generated Synthetic Data: Syntho uses AI to mimic the statistical patterns of original data, ensuring that the synthetic data is accurate and reliable. This includes generating time-series data and upsampling minority classes.
- Data Protection: The platform includes an AI-powered PII Scanner to identify and protect sensitive information by removing or modifying personally identifiable information (PII). It also substitutes sensitive PII, PHI, and other identifiers while preserving referential integrity in relational databases.
- Rule-Based Synthetic Data: Users can generate synthetic data to mimic real-world or targeted scenarios using predefined rules and constraints. This is particularly useful for creating test data for non-production environments.
- Subsetting: Syntho allows users to reduce records to create a smaller, representative subset of a relational database while maintaining referential integrity.
Who Would Benefit Most
Syntho is highly beneficial for organizations across several industries, including:
- Healthcare and Pharma: These sectors can use synthetic data to protect patient information while still conducting comprehensive testing and analysis.
- Finance: Financial institutions can generate synthetic data to test applications and build data foundations without compromising sensitive financial information.
- Government: Government agencies can utilize synthetic data for testing and development, ensuring compliance with data privacy regulations.
- Manufacturing: Companies in this sector can use synthetic data to simulate real-world scenarios and test their systems without exposing actual data.
Overall Recommendation
Syntho is an excellent choice for any organization that needs high-quality synthetic data while ensuring data privacy and compliance. Here are some key reasons why:
- Accuracy and Quality: Syntho’s AI-generated synthetic data is assessed and approved by data experts at SAS, ensuring high accuracy, privacy, and speed.
- Comprehensive Solutions: The platform offers a range of features, including de-identification, synthetization, and rule-based data generation, making it a one-stop solution for synthetic data needs.
- Enterprise Clientele: Syntho already works with prominent enterprise clients such as Cedars-Sinai, Erasmus MC, and CBS, which speaks to its reliability and effectiveness.
In summary, Syntho is a reliable and versatile tool for generating synthetic data, making it an excellent option for organizations that need to balance data quality with data privacy. Its diverse range of features and industry-specific applications make it a valuable asset in various sectors.