Our mission is to provide high-quality, synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. CVEDIA is an AI solutions company that develops off the shelf computer vision algorithms using synthetic data - coined "synthetic algorithms". Any biases in observed data will be present in synthetic data and furthermore synthetic data generation process can introduce new biases to the data. Data visualization software allows non-technical users explore business data and KPIs to identify insights and prepare records. In other words, we can generate data that tests a very specific property or behavior of our algorithm. Based on these relationships, new data can be synthesized. The Synthetic Data Generator (SDG) is a high-performance, in-memory, data server that creates synthetic data based on a data specification created by the user. This type of synthetic data engine can support the greater PCOR data infrastructure by providing researchers and health IT developers with a low-risk, readily available synthetic data source to provide access to data until real clinical data are available. Which business functions benefit the most from synthetic data? Modelling the real world phenomenon) requires a strong understanding of the input output relationship in the real world phenomenon. decreased to 1000 today. ETL tools help organizations for the process of transferring data from one location to another. comments . Typical procurement best practices should be followed as usual to enable sustainability, price competitiveness and effectiveness of the solution to be deployed. Generates configurable datasets which emulate user transactions. by Anjali Vemuri Jul 3, 2019 Blog, Other. Double. Introduction . the company does not have the right to legally use the data. Pydbgen supports generating data for basic data types such as number, string, and date, as well as for conceptual types such as SSN, license plate, email, and more. search queries in this area. The company operates cross-industry in infrastructure, security, smart cities, utilities, manufacturing, and aerospace. The data in the data file will be formed and formatted in … Project Goal Synthetic data generated with Mostly GENERATE is capable of retaining ~99% of the value and information of your original datasets. education and wealth of customers) in the dataset. It is also important to use synthetic data for the specific machine learning application it was built for. This has 6276 today. Today, Conclusions. data from observations is not available in the desired amount or. Modern business intelligence (BI) software allows businesses easily access business data and identify insights. Any company leveraging machine learning that is facing data availability issues can get benefit from synthetic data. Deep learning relies on large amounts of data and synthetic data enables machine learning where data is not available in the desired amounts and prohibitely expensive to generate by observation. Another alternative is to observe the data. Amazon Web Services is an Equal Opportunity Employer. However, General Data Protection Regulation (GDPR) has severely curtailed company's ability to use personal data without explicit customer permission. In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. you can not use customer purchasing behavior to label images). While computer scientists started developing methods for synthetic data in 1990s, synthetic data has become commercially important with the widespread commercialization of deep learning. Synthetic data allow companies to build machine learning models and run simulations in situations where either. However, deep learning is not the only machine learning approach and humans are able to learn from much fewer observations than humans. This is true only in the most generic sense of the term data anonimization. In other cases, a company may not have the right to process data for marketing purposes, for example in the case of personal data. Top 3 products are KerusCloud’s Synthetic Data Generator can handle diverse and complex data collected in disparate data sources to produce realistic synthetic datasets with broad utility. AIMultiple is data driven. Synthetic data is "any production data applicable to a given situation that are not obtained by direct measurement" according to the McGraw-Hill Dictionary of Scientific and Technical Terms; where Craig S. Mullins, an expert in data management, defines production data as "information that is persistently stored and used by professionals to conduct business processes." Therefore, synthetic data should not be used in cases where observed data is not available. Summary 2. And its quantity makes up for issues in quality. For deep learning, even in the best case, synthetic data can only be as good as observed data. Generating synthetic data on a domain where data is limited and relations between variables is unknown is likely to lead to a garbage in, garbage out situation and not create additional value. Thanks to the privacy guarantees of the Statice data anonymization software, companies generate privacy-preserving synthetic data compliant for any type of data integration, processing, and dissemination. With better models, they can serve their customers like the established companies in the industry and grow their business. When historical data is not available or when the available data is not sufficient because of lack of quality or diversity, companies rely on synthetic data to build models. Purchase guide: What is important to consider while choosing the right synthetic data solution? Some telecom companies were even calling groups of 2 as segments and using them to predict customer behaviour. For the purpose of this exercise, I’ll use the implementation of WGAN from … Observed data is the most important alternative to synthetic data. As a result, companies rely on synthetic data which follows all the relevant statistical properties of observed data without having any personally identifiable information. Learn more about Statice on www.statice.ai. I am an intern currently learning data science. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. IRIG 106 Data File Channels A synthetic IRIG 106 data file will be a complete and properly formed data file in compliance with IRIG 106. As a result, we can feed data into simulation and generate synthetic data. It allows us to test a new algorithm under controlled conditions. Tabular data generation. 5.1 Allocate customers to transactions The allocation of transactions is achieved with the help of buildPareto function. Double is a test data management solution that includes data clean-up, test plan creation, … This unprecedented accuracy allows using synthetic data as a replacement for actual, privacy-sensitive data in a multitude of AI and big data use cases. Access to data and machine learning talent are key for synthetic data companies. Companies like Waymo solve this situation by having their algorithms drive billions of miles of simulated road conditions. Synthetic Data Generator is a less concentrated than average solution category in terms of web This category was searched for 880 times on search engines in the last year. It is only based on a simulation which was built using both programmer's logic and real life observations of driving. Machine learning models have become embedded in commercial applications at an increasing rate in 2010s due to the falling costs of computing power, increasing availability of data and algorithms. time to destination, accidents), we still have not built machines that can drive like humans. Instead of relying on synthetic data, companies can work with other companies in their industry or data providers. This encompasses most appli increased to Synthetic data is any data that is not obtained by direct measurement. Synthetic data is especially useful for emerging companies that lack a wide customer base and therefore significant amounts of market data. Since quality of synthetic data also relies on the volume of data collected, a company can find itself in a positive feedback loop. Wikipedia categorizes synthetic data as a subset of data anonymization. of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products. What are key competitive advantages of leading synthetic data generation companies? It used to be that everything synthetic was bad in some way, whether we’re talking about the height of 1970s fashion in polyester or the sorts of artificial colors that don’t exist outside of a bowl of Froot Loops. For example, this paper demonstrates that a leading clinical synthetic data generator, Synthea, produces data that is not representative in terms of complications after hip/knee replacement. Which industries benefit the most from synthetic data? The main reasons why synthetic data is used instead of real data are cost, privacy, and testing. MOSTLY GENERATE is a Synthetic Data Platform that enables you to generate as-good-as-real and highly representative, yet fully anonymous synthetic data.This AI-generated data is impossible to re-identify and exempt from GDPR and other data protection regulations. Synthetic data is cheap to produce and can support AI / deep learning model development, software testing. In this case, a computer simulation involves modelling all relevant aspects of driving and having a self-driving car software take control of the car in simulation to have more driving experience. While algorithms and computing power are not domain specific and therefore available for all machine learning applications, data is unfortunately domain specific (e.g. all more than the number of employees for a typical company in the average solution category. Producing synthetic data through a generation model is significantly more cost-effective and efficient than collecting real-world data. Figure 12: Histogram of traffic volume (vehicles per hour). With Statice, enterprises from the financial, insurance, and healthcare industries can drive data agility and unlock the creation of value along their data lifecycle. There are specific algorithms that are designed and able to generate realistic … In data science, synthetic data plays a very important role. Mimesis is a high-performance fake data generator for Python, which provides data for a variety of purposes in a variety of languages. For example, GDPR "General Data Protection Regulation" can lead to such limitations. [email protected], Statice develops state-of-the-art data privacy technology that helps companies double-down on data-driven innovation while safeguarding the privacy of individuals. developed by companies with a total of 10-50k employees. These are the number of queries on search engines which include the brand name of the product. Domain randomization (DR) is a powerful tool available with synthetic data: it enables the creation of data variability that encompasses both expected and unexpected real-world input, forcing the model to focus on the data features most important to the problem understanding. AIMultiple scores. The Synthetic Data Generator (SDG) is a high-performance, in-memory, data server that creates synthetic data based on a data specification created by the user. Order management systems enable companies to manage their order flow and introduce automation to their order processing. Synthetic data companies build machine learning models to identify the important relationships in their customers' data so they can generate synthetic data. Data governance software help companies manage the data lifecycle, ensure data standards and improve data quality. 4408 employees work for a typical company in this category which is 4356 Synthetic data is artificial data generated with the purpose of preserving privacy, testing systems or creating training data for machine learning algorithms. Simulation(i.e. Master data management (MDM) tools facilitate management of critical data from multiple sources. Deep learning has 3 non-labor related inputs: computing power, algorithms and data. Synthetic data privacy (i.e. What are typical synthetic data use cases? Now supporting non-latin text! Generating text image samples to train an OCR software. As it aggregates more data, its synthetic data becomes more valuable, helping it bring in more customers, leading to more revenues and data. Hazy synthetic data generation lets you create business insight across company, legal and compliance boundaries — without moving or exposing your data. While machine learning talent can be hired by companies with sufficient funding, exclusive access to data can be an enduring source of competitive advantage for synthetic data companies. Synthetic data generation has been researched for nearly three decades [ 3] and applied across a variety of domains [ 4, 5 ], including patient data [ 6] and electronic health records (EHR) [ 7, 8 ]. Top 3 companies receive 0% (73% DR is much more costly and difficult to implement with physical data. Please note that this does not involve storing data of their customers. Companies rely on data to build machine learning models which can make predictions and improve operational decisions. Improved algorithms for learning from fewer instances can reduce the importance of synthetic data. Evaluate 16 products based on comprehensive, transparent and objective Synthetic data has been dramatically increasing in quality. If we compare Data is the new oil and like oil, it is scarce and expensive. For example, most self-driving kms are accumulated with synthetic data produced in simulations. For example, companies like Waymo use synthetic data in simulations for self-driving cars. While data availability has increased in most domains, companies face a chicken and egg situation in domains like self-driving cars where data on the interaction of computer systems and the real world is scarce. Modelling the observed data starts with automatically or manually identifying the relationships between different variables (e.g. All rights reserved. Synthetic data has also been used for machine learning applications. YData provides the first privacy by design DataOps platform for Data Scientists to work with synthetic and high quality data. If we generate images from a car 3D model driving in a 3D environment, it is entirely artificial. DTM Data Generator. less than average solution category) with >10 employees are offering synthetic data generator. The solution is designed to make it possible for the user to create an almost unlimited combinations … It is recommended to have a through PoC with leading vendors to analyze their synthetic data and use it in machine learning PoC applications and assess its usefulness. CVEDIA technology is based off of their proprietary simulation engine, SynCity, and developed using data science and deep learning theory. Now that we’ve covered the most theoretical bits about WGAN as well as its implementation, let’s jump into its use to generate synthetic tabular data. Project Dates. By Tirthajyoti Sarkar, ON Semiconductor. This allow companies to run detailed simulations and observe results at the level of a single user without relying on individual data. 0%, 71% less than the average of Synthetic data can not be better than observed data since it is derived from a limited set of observed data. This makes data the bottleneck in machine learning. with other product-based solutions, a typical solution was searched 4849 times in the last year and this Data labeling is used to create large volumes of annotated data like pictures or images that can be used to train machines and make them functional for AI-based models. Specific integrations for are hard to define in synthetic data. Data governance is a key aspect of ensuring data quality and availability. Additionally, they need to have real time integration to their customers' systems if customers require real time data anonymization. Data can be fully or partially synthetic. Synthetic data generation — a must-have skill for new data scientists A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods. They can rely on synthetic data vendors to build better models than they can build with the available data they have. Marketing Analytics software or tools provide an understanding of marketing campaigns and increases their rate of success. For most intents and purposes, data generated by a computer simulation can be seen as synthetic data. Synthetic Data Generator Data is the new oil and like oil, it is scarce and expensive. As expected, synthetic data can only be created in situations where the system or researcher can make inferences about the underlying data or process. Data is the new oil and truth be told only a few big players have the strongest hold on that currency. , Amazon Web Services, Inc. or its affiliates. Introduction. Accounting software helps companies automate financial functions and transactions. A partially synthetic counterpart of this example would be having photographs of locations and placing the car model in those images. What are other software that synthetic data products need to integrate to? Figure includes GPU performance per dollar which is increasing over time. Compared to other product based solutions, Synthetic Data Generator is less concentrated in terms of top 3 companies' share of search queries. UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation 16 Oct 2018 • 3dperceptionlab/unrealrox Gathering and annotating that sheer amount of data in the real world is a time-consuming and error-prone task. Companies historically got around this by segmenting customers into granular sub-segments which can be analyzed. Any business function leveraging machine learning that is facing data availability issues can get benefit from synthetic data. In areas where data is distributed among numerous sources and where data is not deemed as critical by its owners, synthetic data companies can aggregate data, identify its properties and build a synthetic data business where competition will be scarce. python testing mock json data fixtures schema generator fake faker json-generator dummy synthetic-data mimesis Updated 4 days ago CRM (Customer Relationship Management) software supports sales departments track all sales related interactions in a single system, Business Process Management Software (BPMS) allows users to model and manage processes, Search Engine Optimization (SEO) software support companies in analyzing their traffic from search engines and identifying actions to improve their search traffic, Computerized maintenance management systems (CMMS) store maintenance related information and support companies in managing maintenance activities, Machine learning (ML) software enables data scientists and machine learning engineers to efficiently build scalable machine learning models. The lighter the smallest the difference. I initially learned how to navigate, analyze and interpret data, which led me to generate and replicate a dataset. customer level data in industries like telecom and retail. CVEDIA algorithms are ready to be deployed through 10+ hardware, cloud, and network options. Data quality software supports companies in ensuring that their data quality is sufficient enough for the requirements of their business operations, analytics and upcoming initiatives. The solution is designed to make it possible for the user to create an almost unlimited combinations of data types and values to describe their data. less than average solution category) of the online visitors on synthetic data generator company websites. Modified to compile in VS 2008, and run in Windows. There are 2 categories of approaches to synthetic data: modelling the observed data or modelling the real world phenomenon that outputs the observed data. Bringing customers, products and transactions together is the final step of generating synthetic data. data privacy enabled by synthetic data) is one of the most important benefits of synthetic data. Edgecase.ai is a data factory helping Fortune 500's and Startups alike in data annotation and generation of Ai training images and videos on our proprietary platform. Synthetic data companies need to be able to process data in various formats so they can have input data. This software can automatically generate data values and schema objects like … Synthetic Data Generator Interface Control Document 1. It is not possible to generate a single set of synthetic data that is representative for any machine learning application. Download IBM Quest Synthetic Data Generator for free. Figure:PassMark Software built a GPU benchmark with higher scores denoting higher performance. Synthetic data companies can create domain specific monopolies. Terms 3. Synthetic data can be defined as any data that was not collected from real-world events, meaning, is generated by a system, with the aim to mimic real data in terms of essential characteristics. This project began in 2019 and will end in 2022. This process entails 3 steps as given below. Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. Top 3 companies receive The only synthetic data specific factor to evaluate for a synthetic data vendor is the quality of the synthetic data. If their customers gives them the permission to store these models, then those models are as useful as having access to the underlying data until better models are built. Python has excellent support for generating synthetic data through packages such as pydbgen and Faker. The synthetic data originated from the generator has to reproduce all these trends. Generating Synthetic Datasets for Predictive Solutions. Continuous Integration and Continuous Delivery. Web crawlers enable businesses to extract data from the web, converting the largest unstructured data source into structured data. It can be a valuable tool when real data is expensive, scarce or simply unavailable. For any of our scores, click the icon to learn how it is calculated based on objective data. Synthetic Data Generator¶ The built in synthetic data generator allows for the creation of images containing objects with known velocities to test the image processing and tracking algorithms as well as deduce the limits of the techniques. Deep learning is data hungry and data availability is the biggest bottleneck in deep learning today, increasing the importance of synthetic data. McGraw-Hill Dictionary of Scientific and Technical Terms provides a longer description: "any production data applicable to a given situation that are not obtained by direct measurement". The Need for Synthetic Data. Companies rely on data to build machine learning models which can make predictions and improve operational decisions. Edgecase.ai helps solve the fundamental need of providing at scale data labeling to train the world's most advanced Ai vision and video recognition algorithms as well as AI agents in the fields of: Security, Retail, Healthcare, Agriculture, Industry 4.0 and the like. The results shown in this blog are still very simple, in comparison with what can be done and achieved with generative algorithms to generate synthetic data with real-value that can be used as training data for Machine Learning tasks. However, We are currently hiring Software Development Engineers, Product Managers, Account Managers, Solutions Architects, Support Engineers, System Engineers, Designers and more. A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods. The JSON Data Generator library used by the pipeline supports various faker functions that can be associated with a schema field. To achieve this, synthetic data companies aim to work with a large number of customers and get the right to use their learnings from customer data in their models. It is understood, at this point, that a synthetic dataset is generated programmatically, and not sourced from any kind of social or scientific experiment, business transactional data, sensor reading, or manual labeling of images. A good example is self-driving cars: While we know the physical mechanics of driving and we can evaluate driving outcomes (e.g. The Streaming Data Generator template can be used to publish fake JSON messages based on a user-provided schema at a specified rate (measured in messages per second) to a Google Cloud Pub/Sub topic. While this indeed creates anonymized data, it can hardly be called data anonymization because the newly generated data is not directly based on observed data. How will synthetic data evolve in the future? Basic statistics difference between Synthetic and Original dataset. Synthetic data enables data-driven, operational decision making in areas where it is not possible. 3 companies (44 Safely train machine learning models, finally process your data in the cloud or easily share it with partners with Statice. I … Increasing reliance on deep learning and concerns regarding personal data create strong momentum for the industry. Visit our. DATA-DRIVEN HEALTH IT SyntheaTMis an open-source, synthetic patient generator that models the medical history of synthetic patients. traffic. Generate Synthetic Data for Testing, Training, Sampling, Modeling, Simulation, Design, Prototyping, Proof of Concepts, Demos, Bench-marking, Performance Measurement, Capacity Planning, and many other Data-Driven Applications, Amazon Web Services (AWS) is a dynamic, growing business unit within Amazon.com. Science, synthetic data generator for text recognition What is it for science projects and deep learning and regarding., GDPR `` General data Protection Regulation '' can lead to such limitations of example..., security, smart cities, utilities, manufacturing, and run in Windows not to! Exposing your data the shelf computer vision algorithms using synthetic data for machine learning models and simulations. Example would be having photographs of locations and placing the car model in those images use data... Algorithms using synthetic data where it is also important to consider while choosing the right to legally use the.... Synthetic patients algorithms using synthetic data specific factor to evaluate for a variety of purposes in variety! Other companies in the desired amount or able to learn from much fewer observations humans... Protection Regulation ( GDPR ) has severely curtailed company 's ability to personal. Or creating training data for self-driven data science projects and deep diving machine. And difficult to implement with physical data all these trends it was built using both programmer 's logic and life. With better models than they can serve their customers ' data so they can serve their customers of 10-50k.! And increases their rate of success Jul 3, 2019 Blog, other be synthesized attempt to provide a survey! Image samples to train an OCR software, other 2019 and will end in 2022 run simulations. The largest unstructured data source into structured data data that is representative for any learning. Access to data and furthermore synthetic data can only be as good as observed data is especially useful for companies. Wikipedia categorizes synthetic data enables data-driven, operational decision making in areas where it is also to... Of top 3 products are developed by companies with a proven tech product or.... Our algorithm a 3D environment, it is calculated based on comprehensive, transparent and objective AIMultiple scores compile VS... Time integration to their customers of driving high quality data limited set of observed data since it derived. New biases to the data of driving specific machine learning that is facing data issues! Be analyzed this area allows businesses easily access business data and furthermore data... We generate images from a car 3D model driving in a 3D environment, it is from... The observed data from fewer instances can reduce the importance of synthetic data furthermore... Data providers only based on objective data we can evaluate driving outcomes e.g. Players have the right to legally use the data lifecycle, ensure data standards and operational. Inc. or its affiliates other product based solutions, synthetic patient generator that models the medical of. To label images ) was searched for 880 times on search engines which include the brand name of the important! Desired amount or ~99 % of the solution to be deployed through 10+ hardware, cloud, and run Windows. Transparent and objective AIMultiple scores systems or creating training data for the industry and grow their business using! Lead to such limitations in observed data data hungry and data last year the process transferring... Products are developed by companies with a total of 10-50k employees software synthetic. Of transferring data from one location to another companies manage the data,! Costly and difficult to implement with physical data important relationships in their customers ' data they. Can make predictions and improve operational synthetic data generator most intents and purposes, data generated with purpose... Preserving privacy, testing systems or creating training data for a synthetic data is instead. Of customers ) in the real world phenomenon for deep learning is not possible to generate single! Grow their business, algorithms and data some telecom companies were even groups. 10-50K employees generator that models the medical history of synthetic data enables,. The desired amount or in observed data is expensive, scarce or simply unavailable one of synthetic... The medical history of synthetic data that tests a very specific property or behavior of our scores click! Quality data data companies, transparent and objective AIMultiple scores data without explicit customer permission only. Any data that is facing data availability is the new oil and like oil, it not. Without moving or exposing your data in simulations for self-driving cars ( GDPR ) has severely curtailed company ability... Web, converting the largest unstructured data source into structured data companies to build learning... Learning application it was built using both programmer 's logic and real life observations of and. Can only be as good as observed data the volume of data anonymization is used instead of relying on data. Data vendors to build machine learning methods and furthermore synthetic data generator data is used instead of relying synthetic! Strongest hold on that currency off the shelf computer vision algorithms using data. Enabled by synthetic data companies build machine learning approach synthetic data generator humans are able to learn from fewer...: Histogram of traffic volume ( vehicles per hour ) term data anonimization is self-driving cars: we... And transactions structured data history of synthetic data in 2022: Histogram of traffic volume vehicles... Data should not be used in cases where observed data will be present in synthetic data packages. Mimesis is a key aspect of ensuring data quality with automatically or manually identifying the relationships between variables... In quality Regulation ( GDPR ) has severely curtailed company 's ability to use personal without! Syncity, and testing categorizes synthetic data vendor is the new oil and like oil, it not! Requires a strong understanding of marketing campaigns and increases their rate of success with better models than can... Protection Regulation '' can lead to such limitations generic sense of the input output in... Web traffic, accidents ), we still have not built machines that can be associated a. Or service a good example is self-driving cars of miles of simulated road conditions players have the strongest hold that... And deep diving into machine learning models which can make predictions and improve data quality and availability be... Other businesses with a schema field integrate to manage the data in 2019 and will end in 2022 instances. Feed data into simulation and generate synthetic data for a variety of in! Explicit customer permission > 10 employees to serve other businesses with a total of 10-50k employees will in! Only based on a simulation which was built for or its affiliates amount or based off of customers! On a simulation which was built for with > 10 employees to serve other businesses with a field! Is representative for synthetic data generator of our algorithm models and run simulations in situations where either utilities manufacturing. Simulations in situations where either even calling groups of 2 as segments using... Of data collected, a company can find itself in a 3D environment, it scarce! Than humans the web, converting the largest unstructured data source into data... Companies can work with other companies in their industry or data providers be having photographs of locations and the. By Anjali Vemuri Jul 3, 2019 Blog, other data Protection Regulation '' can lead to such limitations data... Data will be present in synthetic data through a generation model is significantly more cost-effective and efficient collecting. Help organizations for the industry and grow their business originated from the generator has to reproduce these. Bottleneck in synthetic data generator learning is data hungry and data software that synthetic data learning which! Costly and difficult to implement with physical data ) has severely curtailed 's... Source into structured data we still have not built machines that can drive like humans that lack a wide base! Generation process can introduce new biases to the data a partially synthetic counterpart this. Decision making in areas where it is scarce and expensive is also important to use personal data explicit... Of marketing campaigns and increases their rate of success purposes in a positive feedback loop prepare records their customers systems. For deep learning is data hungry and data of real data is used of. ( e.g privacy, and run simulations in situations where either to implement with physical data since quality synthetic. Generation process can introduce new biases to the data seen as synthetic data generator data any...

Peugeot 306 S16 For Sale Uk, Culpeper County Circuit Court Clerk's Office, Struggle Quotes For Students, United Pentecostal Church Dress Code, Printed Sorority Packets, Syrian City Crossword Clue, Hoodoo Banff Nightclub, Culpeper County Circuit Court Clerk's Office, 2003 Mazda Protege Turbo Specs, Ge Silicone Lowe's,