“Synthetic Data Software Industry Report″ is a direct appreciation by The Insight Partners of the market potential. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. We generate synthetic data for training fraud detection and financial risk models. Iterate on ideas rapidly. Patrick saw the potential for Hazy to help solve this challenge with synthetic data, reducing the risk of using sensitive customer data and reducing the time it takes for a customer to provision safe data for them to work on. Synthetic data use cases. For that purpose we use the concept of Mutual Information that measures the co-dependencies — or correlations if data is numeric — between all pairs of variables. Information can be counterintuitive. Synthetic data use cases. Histogram Similarity is the easiest metric to understand and visualise. We generate synthetic data for training fraud detection and financial risk models. Synthetic data innovation. Follow their code on GitHub. Let’s explore the following example to help explain its meaning. Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models. Hazy uses generative models to understand and extract the signal in your data. The autocorrelation of a sequence $$y = (y_{1}, y_{2}, … y_{n})$$ is given by: $AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2$. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. is the entropy, or information, contained in each variable. That's drop-in compatible with your existing analytics code and workflows. After removing personal identifiers, like IDs, names and addresses, Hazy machine learning algorithms generate a synthetic version of real data that retains almost the same statistical aspects of the original data but that will not match any real record. How can we be sure the synthetic data is really safe and can’t be reverse engineered to disclose private information. Accenture were aiming to provide an advanced analytics capability. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. Author of the book "Business Applications of Deep Learning". We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. For these cases, it is essential that queries made on synthetic data retrieve the same number of rows as on the original data. These models can then be moved safely across company, legal and compliance boundaries. Most machine learning algorithms are able to rank the variables in that data that are more informative for a specific task. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Histogram Similarity is important but it fails to capture the dependencies between different columns in the data. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Contribute to hazy/synthpop development by creating an account on GitHub. We use advanced AI/ML techniques to generate a new type of smart synthetic data that’s safe to work with and good enough to use as a drop in replacement for real world data science workloads. In 2018, Hazy won the$1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Quantifying information is an abstract, but very powerful concept that allows us to understand the relationship between variables when we don’t have another way to achieve that. Hazy is a synthetic data generation company. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. Hazy synthetic data quality metrics explained By Armando Vieira on 15 Jan 2021. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Sell insights and leverage the value in your data without exposing sensitive information. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. The synthetic data should preserve this temporal pattern as well as replicate the frequency of events, costs, and outcomes. Hazy is the market-leading synthetic data generator. In the example below, we see that within Hazy you are able to see the level of importance set by the algorithm and how accurately Hazy retains that level. Autocorrelation basically measures how events at time $$X(t)$$ are related to events at time $$X(t - \delta)$$ where $$\delta$$ is a lag parameter. “Hazy can help accelerate our work with synthetic datasets,” he … Zero risk, sample based synthetic data generation to safely share your data. It can be shown that, $H = - \sum_{-i} p_{i} \log_{2} p_{i}$. Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. Synthetic data solves this problem by generating fake data while preserving most of the statistical properties of the original data. Another blogpost will tackle the essential privacy and security questions. In the series of events (head, tails) of tossing a coin each realization has maximum information (entropy) — it means that observing any length of past events would not help us predict the very next event. Follow their code on GitHub. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. The metrics above give a good understanding of the quality of synthetic data. For temporal data, Hazy has a set of other metrics to capture the temporal dependencies on the data that we will discuss in detail in a subsequent post. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g. Typically Hazy models can generate synthetic data with scores higher than 0.9, with 1 being a perfect score. This Query Quality score is obtained by running a battery of random queries and averaging the ratio of the number of rows retrieved in the original and in the synthetic data. Synthetic data of good quality should be able to preserve the same order of importance of variables. Whatever the metric or metrics our customers choose, we are happy that they are able to check the quality of our synthetic data for themselves, building trust and confidence in Hazy’s world-class, enterprise-grade generators. Read about how we reduced time, cost and risk for Nationwide Building Society. Hazy Generate scans your raw data and generates a statistically equivalent synthetic version that contains no real information. Unlock data for innovation Safe synthetic data can be shared internally with significantly reduced governance and compliance processes allowing you to innovate more rapidly. Hazy synthetic data is already being used at major financial institutions for app developers to simulate realistic client behavior patterns before there are even users. Mutual Information is not an easy concept to grasp. Synthetic data comes with proven data compliance and risk mitigation. Our core product is synthetic data - data generated artificially using machine learning techniques, that retains the statistical properties of the real data and can be safely used for analytics and innovation without compromising customers privacy and confidential information. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. 2 talking about this. Our most common questions are: In order to answer these questions, Hazy has developed a set of metrics to quantify the quality and safety of our synthetic data generation. For instance, in healthcare the order of exams and treatments must be preserved: chemotherapy treatments must follow x-rays, CT scans and other medical analysis in a specific order and timing. Good synthetic data should have a Mutual Information score of no less than 0.5. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real data. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. The Mutual Information score is calculated for all possible pairs of variables in the data as the relative change in Mutual Information between the original to the synthetic data: $MI_{score} = \sum_{i=1}^{N} \sum_{j=1}^{N} \left[ \frac{ MI(x_{i},x_{j}) } { MI(\hat{x_{i}},\hat{x_{j}}) } \right] Where $$\bar{y}$$ is the mean of $$y$$. For us at Hazy, the most exciting application of synthetic data is when it is combined with anonymised historical data (e.g.$. How do you know that the synthetic data preserves the same richness, correlations and properties of the original data? Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data. Access specialist external data analysts and externally hosted tools and services. Hazy. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. When talking about fraud detection, it’s important that seasonality patterns, like weekends and holidays, are preserved. This unblocked Accenture’s ability to analyse the data and deliver key business insight to their financial services customer. Hazy generated a synthetic version of their customer’s data that preserved the core signal required for the analytics project. where $$x$$ is the original data and $$\hat{x}$$ is the synthetic data. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data, with Synthetic data innovation. Before then being used to generate statistically equivalent synthetic data. Hazy has pioneered the use of synthetic data to solve this problem by providing a fully synthetic data twin that retains almost all of the value of the original data but removes all the personally identifiable information. As a side note, if X and Y are normal distributions with a correlation of $$\rho$$ then the mutual information will be $$–\frac{1}{2}log(1–\rho^2)$$ - it grows logarithmically as $$\rho$$ approaches 1. However, some caution is necessary as, in some cases, a few extreme cases may be overwhelmingly important and, if not captured by the generator, could render the synthetic data useless — like rare events for fraud detection or money laundering. I recently cohosted a webinar on Smart Synthetic Data with synthetic data generator Hazy’s Harry Keen and Microsoft’s Tom Davis, where we dove into the topic. Hazy is an AI based fintech company that generates smart synthetic data that’s safe to use, and works as a drop in replacement for real data science and analytics workloads. In this session, we will introduce some metrics to quantify similarity, quality, and privacy. Synthetic data innovation. It originally span out of UCL just two years ago, but has come a long way since then. identifiable features are removed or masked) to create brand new hybrid data. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. Generating Synthetic Sequential Data Using GANs August 4, 2020 by Armando Vieira Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. Today we will explain those metrics that will bring rigour to the discussion on the quality of our synthetic data. Advanced GAN technology Hazy Generate incorporates advanced deep learning technology to generate highly accurate safe data. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. "Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. Armando Vieira is a PhD has a Physics and is being doing Data Science for the last 20 years. Since 2017, Harry and his team have been through several Capital Enterprise programmes, including ‘Green Light’, a programme run by CE and funded by CASTS. Because synthetic data is a relatively new field, many concerns are raised by stakeholders when dealing with it — mainly on quality and safety. Physicist, Data Scientist and Entrepreneur. Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions. However, their ability to do so was blocked by data access constraints. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data… $H(X) – H(X | Y) = 2 – 11/8 = 0.375bits$. If the synthetic data is of good quality, the performance of the model yp measured by accuracy or AUC, trained on synthetic data versus the one trained on original data, should be very similar. Hazy has 26 repositories available. Hazy. With this in mind, Hazy has five major metrics to assess the quality of our synthetic data generation. It’s important to our users that they are able to verify the quality of our synthetic data before they use it in production. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. Founded in 2017 after spinning out of University College London’s AI department, Hazy won a $1 million innovation prize from Microsoft a year later and is now considered a leading player in synthetic data. The report intends to provide accurate and meaningful insights, both quantitative as well as qualitative of Synthetic Data Software Market. Through the testing presented above, we proved that GANs present as an effective way to address this problem. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Run analytics workloads in the cloud without exposing your data. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). Hazy has 26 repositories available. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. The Hazy team has built a sophisticated synthetic data generator and enterprise platform that helps customers unlock their data’s full potential, increasing the speed at which they are able to innovate, while minimising risk exposure. Each sample contains measurements from 64 electrodes placed on the subjects’ scalps which were sampled at 256 Hz (3.9-msec epoch) for 1 second. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. The Hazy team has built a sophisticated synthetic data generator and enterprise platform that helps customers unlock their data’s full potential, increasing the speed at which they are able to innovate, while minimising risk exposure. Armando Vieira Data Scientist, Hazy. In other words, the synthetic data keeps all the data value while not compromising any of the privacy. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. Hazy is a synthetic data company. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. Once you onboard us, you can then spin up as many synthetic data sets as you want which you can then release to your prospects. Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Class imbalanced data sets are a major pain point in financial data science, including areas like fraud modelling, credit risk and low frequency trading. Even more challenging is the replication of seemingly unique events, like the Covid-19 pandemic, which proves itself a formidable challenge for any generative model. A further validation of the quality of synthetic data can be obtained by training a specific machine learning model on the synthetic data and test its performance on the original data. 88 percent match for privacy epsilon of 1. To capture these short and long-range correlations the metric of choice is Autocorrelation with a variable lag parameter. This is a reimplementation in Python which allows synthetic data to be generated via the method .generate() after the algorithm had been fit to the original data via the method .fit(). Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. In the case of Hazy, synthetic data is generated by cutting-edge machine learning algorithms that offer certain mathematical guarantees of both utility and privacy. “Hazy has the potential to transform the way everyone interacts with Microsoft’s cloud technology and unlock huge value for our customers.”, “By 2022, 40% of data used to train AI models will be synthetically generated.”, “At Nationwide, we’re using Hazy to unlock our data for testing and data science in a way that signicantly reduces data leakage risk.”. Assuming data is tabular, this synthetic data metric quantifies the overlap of original versus synthetic data distributions corresponding to each column. Hazy is the market-leading synthetic data generator. To evaluate these quantities we simply compute the marginals of X and Y (sums over rows and columns): And then the information H for variable X is obtained by summing over the marginals of X, $- \sum_{i=1, 4} pi.log_{2} (pi) = 7/4 bits. We assume events occur at a fixed rate, but this restriction does not affect the generality of the concept. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. Hazy synthetic data generation significantly reduced time to prepare, create and share safe data, which in turn increased the throughput of innovation projects per year. If both distributions overlap perfectly this metric is 1, and it’s 0 if no overlap is found. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. This dataset contains records of EEG signals from 120 patients over a series of trials. Normally this involves splitting the data into a Training Set to train the model and a Test Set to validate the model, in order to avoid overfitting. Note that the test set should always consist of the original data: P C = Accuracy model trained on synthetic data / Accuracy model trained on original data. Data science and analytics It is equivalent to the uncertainty or randomness of a variable. | Hazy is a synthetic data company. Mutual information between a pair of variables X and Y quantifies how much information about Y can be obtained by observing variable X: \[MI(X;Y) = \sum_{x \in X} \sum_{y \in Y} p(x, y) log \frac{p(x, y)}{p(x)p(y)}$, where $$p(x)$$ is the probability of observing x, $$p(y)$$ is the probability of observing y and $$p(x,y)$$ the probability of observing x given y. An enterprise class software platform with a track record of successfully enabling real world enterprise data analytics in production. We use advanced AI/ML techniques to generate a new type of smart synthetic data that's both private and safe to work with and good enough to use as a drop in replacement for real world data science workloads. identifiable features are removed or … We are pleased to be cited as having helped improve on their exceptional work. Hazy – Fraud Detection. Zero risk, sample based synthetic data generation to safely share your data. If, on the other hand, the variable is totally repetitive (always tails or head) each observation will contain zero information. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. Hazy. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Hazy is the market-leading synthetic data generator. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. Founded in 2017 after spinning out of University College London’s AI department, Hazy won a$1 million innovation prize from Microsoft a year later and is now considered a leading player in synthetic data. For instance, if we query the data for users above 50 years old and an annual income below £50,000, the same number of rows should be retrieved as in the original data. In some situations, synthetic data is used for reporting and business intelligence. Hazy synthetic data is leveraged by innovation teams at Nationwide and Accenture to allow these heavily regulated multinationals to quickly, securely share the value of the data, without any privacy risks. 2 talking about this. Our core product is synthetic data - data generated artificially using machine learning techniques, that retains the statistical properties of the real data and can be safely used for analytics and innovation without compromising customers privacy and confidential information. As can be seen in Figure 4 the data has a complex temporal structure but with strong temporal and spatial correlations that have to be preserved in the synthetic version. 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