Betterdata provides programmatic synthetic data solutions for transforming production data into privacy-preserving and realistic synthetic data. The company enables sharing, accessing, and building with data safely across teams, businesses, and international borders.

Betterdata

Data sharing is a huge challenge for data & AI teams in financial services because 2/3rds of the world has passed privacy laws. Our AI Programmable Synthetic Data Platform makes data sharing instant and safe by anonymising sensitive real data into privacy preserving synthetic data that looks, feels and behaves just like real data. As synthetic data belongs to no real individuals, it can be shared globally with 100% compliance while putting data privacy first.

We're hiring for AI researchers, engineering and business roles at https://tinyurl.com/betterdatajobs.

Huawei's Spark Incubator

Bon Auxilium

Investible

Xcel Next Ventures

Tenity

Entrepreneur First

Plug and Play

Singapore University of Technology & Design

Franklin Templeton

We are hiring for many full-time roles - check them out! 

Business
- Business Development (Enterprise Sales)
- Founder's Associate
- Growth Hacker
- Sales Engineer

Technology
- Senior Software Engineer (Backend)
- Applied Machine Learning Engineer

AI & Privacy Excellency Lab
- Research Engineer (Bachelor/Master Graduate) 
- Research Fellow (Ph.D. Graduate)

If working with a team in an early-stage startup that is creating a responsible data economy with Generative AI and Privacy Engineering excites you, then we would like to hear from you!

Apply here: tinyurl.com/betterdatajobs

Feel free to DM me if you have any questions and share this with relevant people.

#startup #betterdata #techhiring 
#generativeai #privacyengineering #syntheticdata 
#dataprivacy #dataprotection #dataprotectionlaw

'When it comes to protecting data privacy, regulations do not define how to do this, that's where it's quite a bit of a gray area. And that's where privacy technologies take the spotlight in terms of remaining compliant with privacy laws and appreciating your users' privacy. So zooming out, in the future, when we talk about AI, the foundations will be built not only on greater quantities of data but also on good or better data.' 

Here are the highlights from our co-founder and CEO, Dr. Uzair Javaid, Ph.D.  recent panel discussion on the impact of AI on data privacy at the International Conference 2024, organized by #PDPA #Thailand. The event focused on 'Key Global Trends in Data Privacy.'

Uzair Javaid, Ph.D. shared his insights on the accelerating adoption of AI, the challenges posed by the gap between data privacy regulations and AI deployment, and the implications for machine learning and privacy protection.

He emphasized the importance of privacy engineering and highlighted how #SyntheticData can play a crucial role in safeguarding privacy while enabling the rapid development of #EnterpriseML.

Watch the full talk here: 
https://lnkd.in/gArgn9BP

#pdpa #pdpc #dataprivacy #privacyprotection  #syntheticdata #artificialintelligence #datasecurity #machinelearning 
#genai

In today's data-centric world, ensuring privacy while sharing data globally is crucial. Synthetic data is transforming how we manage data privacy, offering significant advantages over traditional methods. Here's why:

🔹 Time Efficiency: Unlike traditional anonymization techniques that require extensive manual effort, synthetic data can be generated quickly, enabling instant sharing without compromising on privacy.

🔹 Effortless Implementation: Traditional methods often involve complex processes and regulatory hurdles. Synthetic data, on the other hand, simplifies data sharing by providing ready-to-use, privacy-compliant datasets.

🔹 Reduced Complexity: Managing data privacy using conventional methods can be intricate and resource-intensive. Synthetic data streamlines this process, reducing the need for specialized expertise and minimizing operational complexity.

🔹 Cost-Effectiveness: Traditional privacy-preserving methods can be costly due to the need for continuous monitoring and updating. Synthetic data offers a cost-effective alternative by eliminating the need for constant oversight and compliance adjustments.

🔹 Enhanced Security: While other methods might still pose risks of re-identification, synthetic data ensures complete privacy as it doesn't contain any real personal information, mitigating the risk of data breaches.

🔹 Global Accessibility: Sharing data across borders is often hampered by varying privacy laws. Synthetic data circumvents these issues, allowing seamless and lawful global data sharing.

Let's unlock the potential of instant, worldwide data sharing! Contact us on 

contact@betterdata.ai 

#DataPrivacy #SyntheticData #GlobalDataSharing #Innovation #DataProtection #TechTrends #CostEfficiency #Security #AI

The integration of synthetic data into healthcare research marks a paradigm shift in how we approach the study of rare conditions. By bridging the data gap, synthetic data empowers researchers, clinicians, and pharmaceutical companies to push the boundaries of medical science. This, in turn, promises a brighter future for patients with rare diseases, offering hope for better treatments, improved diagnostic tools, and ultimately, enhanced quality of life.

Read more on enhancing data through synthetic data for healthcare in our latest issue of the Synthetica Brief. 

#syntheticdata #healthcare #dataenhancement #machinelearning #innovation

In the dynamic world of data-driven decision-making, ensuring data quality is paramount. Synthetic data, generated through advanced algorithms, is emerging as a powerful tool to address data quality challenges and drive innovation. Here’s how:

🔹 Consistency and Accuracy: Synthetic data eliminates inconsistencies and errors in real-world data, providing a reliable foundation for analytics and decision-making.

🔹 Scalability: It allows for the generation of vast amounts of data, enabling robust testing and validation of models without the limitations of real data availability.

🔹 Cost Efficiency: Synthetic data offers a cost-effective alternative for creating diverse and representative datasets by reducing the need for expensive data collection processes.

🔹 Bias Reduction: Synthetic data can help remove biases present in real-world data, leading to more fair and accurate machine learning models and improving overall model performance.

🔹 Innovation and Experimentation: With synthetic data, businesses can simulate various scenarios and conditions, fostering innovation and experimentation without real-world constraints.

Embracing synthetic data can significantly enhance the quality, security, and efficiency of your data processes, paving the way for smarter, data-driven strategies and solutions. Let's harness the power of synthetic data to drive forward into a more innovative and secure future!

#DataQuality #SyntheticData #Innovation #DataDriven #TechTrends #DataPrivacy #AI #MachineLearning

Highlights from the Thailand #PDPA International Conference 2024 on 'Key Global Trends in Data Privacy' where our CEO Uzair Javaid, Ph.D. was invited to join the panel discussion on the impact of #AI on #DataPrivacy. 

The discussion revolved around the rapid growth of AI adoption, the gap between data privacy regulations and AI implementation, and the impact on data for ML and privacy protection. 

Dr. Uzair presented his views on the topic from a privacy engineering perspective and the importance of #SyntheticData to preserve privacy while empowering rapid advancement in #EnterpriseML. 

Watch the full talk here: 
https://lnkd.in/gArgn9BP

#pdpa #pdpc #dataprivacy #privacyprotection #syntheticdata #artificialintelligence #datasecurity #machinelearning #genai

Co-founder and CEO of Betterdata, Uzair Javaid, Ph.D. has been invited to the much anticipated #Thailand #PDPA International Conference 2024 'Key Global Trends in Data Privacy' to join the discussion on 

- Impact of #AI on Data Privacy
- Balancing AI Innovation with Privacy Protection
- Navigating Privacy Risks
- Adopting Responsible AI Practices 

in line with global data protection regulations. 

#pdpa #pdpc #dataprivacy #privacyprotection #syntheticdata #artificialintelligence #datasecurity

In today's data-driven world, the ability to generate high-quality, synthetic data is a game-changer for businesses. Whether you aim to test software, train machine learning models, or ensure data privacy and compliance, synthetic data provides a robust solution. Here are four cutting-edge methods to synthesize structured, tabular data that can revolutionize your data operations:

1. Generative AI

Generate Realistic Data: Create data that mirrors real-world scenarios without compromising security.

Enhance Testing Environments: Use synthetic data to test software applications in a controlled, risk-free manner.

Improve ML Models: Train your machine learning models on diverse, high-quality data sets to boost performance.

2. Rules Engine:

Custom Data Generation: Tailor data sets to meet specific business needs and scenarios.

Policy Compliance: Ensure data consistency and adherence to organizational policies.

Streamlined Provisioning: Simplify the process of data generation and management.

3. Entity Cloning

Data Integrity: Maintain the relationships and structures within your data sets.

Secure Anonymization: Protect sensitive information by masking identifiable details.

Efficient Replication: Quickly replicate data for various use cases without risking privacy.

4. Data Masking

Privacy Protection: Safeguarding individual privacy by anonymizing sensitive data.

Regulatory Compliance: Ensure adherence to data protection regulations and standards.

Safe Data Sharing: Share data internally and externally without exposing sensitive information.

Transform Your Data Strategy Today!

Embracing these innovative methods to synthesize structured, tabular data can propel your business forward. By generating secure, realistic, and compliant data sets, you can enhance your testing environments, improve machine learning models, and ensure privacy and regulatory compliance.

Contact Betterdata to access data 10x quicker with synthetic data:

contact@betterdata.ai


#DataPrivacy  #DataSecurity  #MachineLearning #DataScience #DataAnonymization #ArtificialIntelligence

➡ Fully Synthetic Data:

Fully synthetic data is entirely generated by algorithms without direct reliance on real-world data. This type of data is created from models trained on real data but includes no real data points.

Key Characteristics:

1. Privacy: No direct link to real-world individuals, ensuring privacy.
2. Versatility: Can be generated in large quantities to meet specific requirements.
3. Quality Control: The quality of fully synthetic data depends heavily on the underlying model's accuracy and robustness.

Use Cases:

1. Training machine learning models when real data is sensitive or limited.
2. Testing software systems under scenarios that real data might not cover.
3. Creating datasets for benchmarking algorithms.

➡ Partially Synthetic Data

Partially synthetic data, also known as hybrid synthetic data, combines real data with synthetic elements. This approach generates synthetic values for some parts of the dataset while retaining actual data points for other parts, maintaining a balance between data utility and privacy.

Key Characteristics:

1. Combination of Real and Synthetic: Contains both real and synthetic elements.
2. Enhanced Privacy: Reduces the risk of re-identification by altering sensitive parts of the data.
3. Utility Retention: Maintains the utility of the original dataset by preserving key patterns and correlations.

Use Cases:

1. Replacing sensitive attributes in a dataset to protect privacy while keeping non-sensitive data intact.
2. Augmenting real datasets with synthetic data to address class imbalances or to test specific conditions.
3. Sharing datasets for research or collaboration without exposing sensitive information.


#syntheticdata #dataprivacy #datasecurity

Metric
Company Name	Betterdata
Symbol
MSH ID	AI:BETTERDATA
Market	PRIVATE
Sector	Technology
Industry	Data Management and Analytics
Country	DE
Stage
Employee Count
Website	betterdata.ai
LinkedIn	betterdataai
Founders

Metric
Market Cap
Enterprise Value
Monthly Web Traffic
Web Traffic Growth
Valuation
Raised Capital

Metric
Revenue
Revenue (LTM)
Revenue (NTM)
Gross Profit
EBITDA
Operating Income
Net Income
EPS
Diluted EPS
Revenue 2022
Revenue 2023
Revenue 2024
Revenue 2025
Revenue 2026

Metric
Revenue Growth (LTM)
Revenue Growth (NTM)
Gross Profit Growth (LTM)
EBITDA Growth (LTM)
Net Income Growth (LTM)
14-Day Growth Score
28-Day Growth Score
90-Day Growth Score
365-Day Growth Score

Metric
Gross Profit Margin
Operating Profit Margin
EBITDA Margin
Net Profit Margin
Return on Equity
Return on Assets
Return on Capital Employed

AI:BETTERDATA

Betterdata

Company Overview

Market Metrics

Financial Performance

Growth Metrics

Profitability Ratios

Valuation Multiples

Operational Metrics

Cash Flow Metrics

Balance Sheet Metrics

Efficiency Metrics

Metric
P/E Ratio
P/B Ratio
Revenue Multiple (LTM)
Revenue Multiple (NTM)
EBITDA Multiple (LTM)
EBITDA Multiple (NTM)
Price to Sales Ratio
EV Multiple

Metric
Days Sales Outstanding
Days Payables Outstanding
Days Inventory Outstanding
Operating Cycle
Cash Conversion Cycle
Asset Turnover

Metric
Operating Cash Flow
Free Cash Flow
Cash Flow to Debt
Operating Cash Flow/Sales
Free Cash Flow Yield

Metric
Cash & Equivalents
Accounts Receivable
Inventory
Goodwill
Debt to Capitalization
Debt to Assets
Current Ratio
Quick Ratio

Metric
Rule of 40 (LTM)
Rule of 40 (NTM)
Stock Comp to Revenue
R&D to Revenue
SG&A to Revenue