Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. Hazy | 1 429 abonnés sur LinkedIn. If, on the other hand, the variable is totally repetitive (always tails or head) each observation will contain zero information. We specialise in the financial services data domain. It originally span out of UCL just two years ago, but has come a long way since then. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Synthetic data of good quality should be able to preserve the same order of importance of variables. Join Hazy, Logic20/20, and Microsoft for our upcoming webinar, Smart Synthetic Data, on October 13th from 10:00 am-11:00 am PST to learn more. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. 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 is the most advanced and experienced synthetic data company in the world with teammates on three continents. 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. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. 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. This dataset contains records of EEG signals from 120 patients over a series of trials. 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. Histogram Similarity is the easiest metric to understand and visualise. Follow their code on GitHub. | Hazy is a synthetic data company. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). is the entropy, or information, contained in each variable. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. In this session, we will introduce some metrics to quantify similarity, quality, and privacy. 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 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. 2 talking about this. Accenture were aiming to provide an advanced analytics capability. Synthetic data use cases. To capture these short and long-range correlations the metric of choice is Autocorrelation with a variable lag parameter. 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. In these cases we may need to skew the sampling mechanism and the metrics to capture these extremes. For instance, we may use the synthetic data to predict the likelihood of customer churn using, say, an XGBoost algorithm. It’s important to our users that they are able to verify the quality of our synthetic data before they use it in production. It originally span out of UCL just two years ago, but has come a long way since then. The DoppelGANger generator had hit a 43 percent match, while the Hazy synthetic data generator has so far resulted in an 88 percent match for privacy epsilon of 1. The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. 2 talking about this. 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 smart synthetic data that helps financial service companies innovate faster. Hazy is a synthetic data company. It is equivalent to the uncertainty or randomness of a variable. The result is more intelligent synthetic data that looks and behaves just like the input data. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. 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. 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. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. Synthetic data innovation. 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. Hazy is the market-leading synthetic data generator. For these cases, it is essential that queries made on synthetic data retrieve the same number of rows as on the original data. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. The report intends to provide accurate and meaningful insights, both quantitative as well as qualitative of Synthetic Data Software Market. However, their ability to do so was blocked by data access constraints. 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. Physicist, Data Scientist and Entrepreneur. Good synthetic data should have a Mutual Information score of no less than 0.5. 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. Zero risk, sample based synthetic data generation to safely share your data. We assume events occur at a fixed rate, but this restriction does not affect the generality of the concept. “Synthetic Data Software Industry Report″ is a direct appreciation by The Insight Partners of the market potential. 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. 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. If the events are categorical instead of numeric (for instance medical exams), the same concept still applies but we use Mutual Information instead. Run analytics workloads in the cloud without exposing your data. 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. 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. Synthetic data is data that’s artificially manufactured relatively than generated by real-world events. 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. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. 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. Hazy – Fraud Detection. Histogram Similarity is important but it fails to capture the dependencies between different columns in the data. 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. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real 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. Share with third parties Generate data that can be shared easily with third parties so you can test and validate new propositions quickly. If you are dealing with sequential data, like data that has a time dependency, such as bank transactions, these temporal dependencies must be preserved in the synthetic data as well. Follow their code on GitHub. Enable enterprise analytics of their collective profiles and behaviors are preserved on real data head ) each will. Data solves this problem statistically equivalent synthetic version that contains no real.... 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