In this special guest feature, Dr. Sigal Shaked, Co-founder and CTO, at Datomize , discusses the best approach to overcome data challenges and achieve strong data governance so you’re in line with regulations – especially important for the banking and healthcare industries. Sigal has extensive experience of more than 15 years working with data in different fields and for various needs, both as a researcher and as an implementer, with a deep understanding of the underlying issues behind working with data. Sigal aspires to lead the best solutions for existing needs, armed with learning machines and their super powers, and equipped with human creativity that no machine would compete with. Artificial intelligence can add $15.7 trillion to the world economy by 2030, equivalent to China and India’s combined output. However, without a steady stream of complete and reliable data, machine learning models can’t provide valuable and trusted insights. Preparing data is a huge challenge. Data scientists often spend 80% of their time cleaning and managing data rather than training models. Here is a drill-down of the most common data challenges facing data scientists. AI/ML models are starved for data 
According to a  McKinsey survey , out of 100 organizations that have piloted AI in at least one of their functions, 24 stated that the largest barrier in AI implementation is the lack of usable and relevant data. Linear algorithms need hundreds of examples per class, while more complex algorithms need tens of thousands to millions of data sets. When a model is trained with insufficient data, there is a high risk that it won’t work effectively when new data is added. Even in cases where a large quantity of data available, there is still a chance that the data will not be usable due to personal privacy laws. There are many regulations that prevent the use of sensitive data to feed machine learning models, including: General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), Health Insurance Portability and Accountability Act ( HIPAA ), Federal Information Security Management Act of 2002 (FISMA), Family Educational Rights and Privacy Act (FERPA), Gramm–Leach–Bliley Act (GLBA). It’s especially challenging to find safe data to feed AI/ML models for the medical and finance industries since the data is so sensitive. Data can also be biased. A machine learning model will make assumptions based on whatever data it reads. If that data tells a skewed or incomplete story, the rules it creates will be fundamentally unsound. Training data needs to be an accurate representation of the population, including data sets from every category. For example, even though black women are 42 percent more likely to die from breast cancer , machine learning models were fed by mammography images overwhelmingly from white women, making them inaccurate when reaching conclusions for the all women. There is also the issue of low data quality. Even if the data is safe and representative of every segment in the population, it can still be unusable because it’s incomplete, irrelevant, or out of date. Many enterprises have an inconsistent data vocabulary because data resides in silos in different regions, business units, and geographies. Steps to Overcoming Data Challenges
In order to collect the data that’s needed it’s possible to create systems to harvest data from different sources. If you know the tasks that a machine learning algorithm will perform, then you can create a data-gathering mechanism in advance to collect the data internally. However, this data may be unusable due to regulations, so it will have to be anonymized. At the most basic level, you have techniques like data generalization, pseudo-anonymization, and data masking, but none of these methods are secure enough to deter hackers. Data swapping reassigns data points from one person to another, making the data more secure, but less useful for analysis to glean insights about real-life scenarios. Similarly, perturbation and differential privacy add random noise to obscure details that need to be kept confidential, making the data difficult to analyze. Enterprises, government agencies, and academic institutions provide open-source data. But intellectual property issues can limit the use of data only for research and not for commercial purposes. This data also tends to be useful only for industries that are not highly regulated. Therefore it can be hard to find data used in medical and financial industries. Another option is for enterprises is to generate their own synthetic data sets that contain the same schema and statistical properties as their “real” counterpart. Enterprises can then have more control over data quality and can tweak the data set characteristics based on AL/ML objectives. Generating synthetic data can also provide the scale global organizations need to create the quantity, variety, and granularity of data required so that the resulting models are unbiased, accurate, and complete. Even after all the necessary is collected, a data governance system needs to be implemented to keep the data pipeline working. Without a firm data strategy and enterprise-wide supply of data on-demand, machine learning models will no longer be relevant. Data governance costs need to be built into projects to ensure machine learning models stay on track. In our data-driven world, machine learning models are becoming a given for reaching insights that streamline operations, identify new revenue streams and provide engaging customer experiences. But until enterprises have a healthy pipeline of quality data, the quality and wisdom of the insights gleaned by AI/ML models are at risk. Sign up for the free insideBIGDATA  newsletter . Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1

Data Roadblocks for AI – Most common challenges and how to avoid them

Datomize , a Tel Aviv, Israel-based developer of synthetic data solution to accelerate time to market for AI/ML models and new products, completed its $6m seed funding round. The round was led by TPY Capital, with participation from its first investor F2 Venture Capital. The company intends to use the funds for the commercialization of its synthetic data solution that accelerates time to market for artificial intelligence (AI)/machine learning (ML) models and new products that drive business growth. Co-founded by Avi Weiss, CEO, Sigal Shaked, CTO, and Roy Yogev, Datomize synthesizes new data to improve the efficiency and speed of developing and training AI/ML models and applications for hundreds of use cases. Data preserves the behavioral features of the original data without violating personal privacy regulations. Based on real customer data and insights from global banks, the company is able to process complex data structures with multiple dependencies and is fully scalable to process thousands of tables with millions of records. FinSMEs

Datomize Raises $6M in Seed Funding

Israeli startup Datomize develops synthetic data to give financial institutions access to user data, without jeopardizing their privacy...
Oshry Alkeslasi /
• 2 min read
Datomize, an Israeli startup developing a synthetic data platform for financial institutions, secured a $6 million Seed round. The investment was led by TPY Capital, with F2 Venture Capital also participating. Who are you synthetic data? Datomize synthesizes new data that preserves the behavioral features of the original data without violating personal privacy regulations. Designed based on real customer data and insights from global banks, Datomize is uniquely able to process highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables with millions of records. Datomize improves the efficiency and speed of developing and training AI/ML models and applications for hundreds of use cases, as companies become more data-driven in the new online economy. The Datomize platform synthesizes new data that preserves the behavioral features of the original data without violating personal privacy regulations. In a conversation with Geektime, CTO and co-founder Sigal Shaked PhD explained that it’s essentially artificial data, formed through artificial intelligence. “Machine Learning algorithms extract behavioral characteristics from the original data, and based on the behavior create synthetic data. This creates an almost identical data twin, which cannot be used to speculate anything from the original data.”
The synthetic data is used alternatively through various stages of development, allowing secure collaboration between organizations, along with stable and secure cloud environment. In addition, the synthetic data can be used to generate data dedicated for bottleneck tests or to improve the ML based processes required for massive amounts of data. "Datomize’s synthetic data revolutionizes the AI/ML and IT lifecycle by removing the major bottleneck that prevents the successful deployment of AI/ML models and continuous delivery of evolving applications,” said Avi Weiss, Founder and Chief Executive Officer (CEO). The company noted that Datomize’s difference maker lies in the company’s user-friendly synthetization process. "Datomize makes the generation and management of synthetic data simpler, more effective, and efficient, so that highly trained and hard to find data scientists can focus on analysis and strategy,” added Weiss. Datomize was founded in 2020 by CEO Avi Weiss, co-founder of ObserveIT - acquired in 2019; CTO Sigal Shaked; and VP of Business & Marketing Roy Yogev. Datomize has 17 employees working out of the Israel based headquarters, and is currently establishing a foothold in the U.S. and Europe. co-founders left to right- Roy Yogev, VP Business development and Marketing, Sigal Shaked CTO, Avi Weiss CEO
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This Israeli startup creates synthetic data to protect user privacy

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Datomize Secures $6 Million Seed Round Funding for Enterprise Grade Synthetic Data Solution
Recent Posts By Deborah Schalm
Tel Aviv – February 3, 2021 – Today  Datomize  announced the completion of its $6 million seed funding round for the commercialization of its synthetic data solution that accelerates time to market for artificial intelligence (AI)/machine learning (ML) models and new products that drive business growth. This funding round was led by  TPY Capital , as part of its thematic focus on groundbreaking data and analytics startups, with participation from its first investor  F2 Venture Capital , which backs visionary big data and AI companies. “Datomize’s synthetic data revolutionizes the AI/ML and IT lifecycle by removing the major bottleneck that prevents the successful deployment of AI/ML models and continuous delivery of evolving applications,” said Avi Weiss, Founder and Chief Executive Officer (CEO). “Datomize makes the generation and management of synthetic data simpler, more effective, and efficient, so that highly trained and hard to find data scientists can focus on analysis and strategy.”
There is a massive data shortage caused by data privacy laws that is limiting the effectiveness of development and training of AI/ML models. According to the Gartner Hype Cycle for Privacy released July 2020, authored by  Bernard Woo  and  Bart Willemsen , “One of the major problems with AI development today is the burden in obtaining real-world data and labeling it so that AI may be trained effectively. Synthetic data addresses the problem of volume and variety for sparse, nonexistent or difficult to get data”. Datomize synthesizes new data that preserves the behavioral features of the original data without violating personal privacy regulations. Designed based on real customer data and insights from global banks, Datomize is uniquely able to process highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables with millions of records. Datomize improves the efficiency and speed of developing and training AI/ML models and applications for hundreds of use cases, as companies become more data-driven in the new online economy. “Datomize has the potential to enable enterprises to unlock the value of one of the most prized assets – their data. It eliminates one of the biggest concerns of executives and regulators, jeopardized privacy, by producing synthetic data which can be used for multiple business applications,” commented Dekel Persi from TPY. “We are particularly happy to back a founding team which combines a successful entrepreneurship track record with technological depth.”
Jonathan Saacks from F2 commented, “We are backing Datomize because they have the right solution at the right time. They are supplying exactly what organizations need to accelerate their digital transformation initiatives. Designed using real word data from international banks, Datomize has the ability to unlock the value organizations can receive from their data with the scale and speed global organizations need so that their AI/ML models are more powerful, accurate, and can become fully operational faster.”
The three Datomize co-founders include Avi Weiss, CEO, previously co-founder of ObserveIT, acquired by Proofpoint, Sigal Shaked, CTO and PhD in Software and Systems Engineering with over 15 years of experience in research and implementation of data solutions, and Roy Yogev, who has helped Fortune 500 companies bring new ideas to market for over 25 years. About Datomize 
Datomize’s synthetic data solution accelerates and streamlines the development, training and testing of AI/ML models and applications that drive business growth. Datomize preserves the behavioral features of the original production data without violating personal privacy regulations, to speed up time to market for new products and services and rapidly generate insights that drive digital transformation. Providing the speed and scale required by global financial institutions and highly regulated industries, Datomize processes highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables and millions of records. Datomize enables enterprises to rapidly release new services, move analytics to the cloud, integrate third-party solutions, and glean insights from AI/ML models to become market leaders. For more information, visit the company’s website at  www.datomize.com . Media Assets can be found  here

Datomize Secures $6 Million Seed Round Funding for Enterprise Grade Synthetic Data Solution

Nickie Louise Posted On February 3, 2021
There is a massive data shortage caused by data privacy laws that are limiting the effectiveness of the development and training of AI/ML models. According to the Gartner Hype Cycle for Privacy released July 2020, authored by Bernard Woo and Bart Willemsen, “One of the major problems with AI development today is the burden in obtaining real-world data and labeling it so that AI may be trained effectively. Synthetic data addresses the problem of volume and variety for sparse, nonexistent or difficult to get data.”
Datomize is a Tel Aviv, Israel-based startup that produces synthetic data so that financial organizations can use users’ data without compromising their privacy. Datomize liberates the use of sensitive data in order to reap its value, without compromising privacy. The startup solves this problem by synthesizing new data that preserves the behavioral features of the original data without violating personal privacy regulations. Designed based on real customer data and insights from global banks, Datomize is uniquely able to process highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables with millions of records. Datomize improves the efficiency and speed of developing and training AI/ML models and applications for hundreds of use cases, as companies become more data-driven in the new online economy. Today, Datomize announced it has closed a $6 million seed funding round for the commercialization of its synthetic data solution that accelerates time to market for artificial intelligence (AI)/machine learning (ML) models and new products that drive business growth. This funding round was led by TPY Capital, as part of its thematic focus on groundbreaking data and analytics startups, with participation from its first investor F2 Venture Capital, which backs visionary big data and AI companies. Founded a year ago by Avi Weiss, Sigal Shaked, Roy Yogev, Datomize’s synthetic data solution accelerates and streamlines the development, training, and testing of AI/ML models and applications that drive business growth. Avi Weiss, the CEO, was previously the co-founder of ObserveIT before it was acquired by Proofpoint. Datomize preserves the behavioral features of the original production data without violating personal privacy regulations, to speed up time to market for new products and services and rapidly generate insights that drive digital transformation. Providing the speed and scale required by global financial institutions and highly regulated industries, Datomize processes highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables and millions of records
“Datomize’s synthetic data revolutionizes the AI/ML and IT lifecycle by removing the major bottleneck that prevents the successful deployment of AI/ML models and continuous delivery of evolving applications,” said Avi Weiss, Founder and Chief Executive Officer (CEO). “Datomize makes the generation and management of synthetic data simpler, more effective, and efficient, so that highly trained and hard to find data scientists can focus on analysis and strategy.”
“Datomize has the potential to enable enterprises to unlock the value of one of the most prized assets – their data. It eliminates one of the biggest concerns of executives and regulators, jeopardized privacy, by producing synthetic data which can be used for multiple business applications,” commented Dekel Persi from TPY. “We are particularly happy to back a founding team which combines a successful entrepreneurship track record with technological depth.”
Jonathan Saacks from F2 commented, “We are backing Datomize because they have the right solution at the right time. They are supplying exactly what organizations need to accelerate their digital transformation initiatives. Designed using real-world data from international banks, Datomize has the ability to unlock the value organizations can receive from their data with the scale and speed global organizations need so that their AI/ML models are more powerful, accurate, and can become fully operational faster.”
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Israeli artificial intelligence startup Datomize raises $6 million for its real-time codeless machine learning platform

Datomize

Leumi & Co.

BGN Technologies

Designit

Nascent Group

ActivePath

ObserveIT

Hewlett Packard Enterprise

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.

Artificial Intelligence

Datomize analyzes the use of Personal Data (PII) for the purpose of business growth and operational optimization,

Name	Derek Rego
Firm
Work History	Previously CTO of CreditEnable, Rego has also held senior technology leadership roles for over 25+ years with Citi, where his career spanned positions as Global Head of Treasury and Trade Solutions Technology and Global Electronic Banking Channels Head. He brings a wealth of experience driving successful delivery of large transformation and growth programs solving complex business challenges using technology across large-scale enterprise and fintech organisations.
Other Seats	Datomize

Name	Work History	Title	Status
Sigal Shaked	Leumi & Co., and BGN Technologies	Founder, Chief Technology Officer	Current
Roy Yogev	Designit, and Nascent Group	Founder	Current
Avi Weiss	ActivePath, ObserveIT, and Hewlett Packard Enterprise	Chief Executive Officer	Current

Name	Sigal Shaked	Roy Yogev	Avi Weiss
Work History	Leumi & Co., and BGN Technologies	Designit, and Nascent Group	ActivePath, ObserveIT, and Hewlett Packard Enterprise
Title	Founder, Chief Technology Officer	Founder	Chief Executive Officer
Status	Current	Current	Current

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