BIG DATA MANAGEMENT, DATA SCIENCE
Amazon | September 16, 2022
Today at Accelerate, Amazon’s annual seller conference, Amazon announced new features to Manage Your Experiments, a tool that helps sellers optimize content on product detail pages to drive higher rates of conversion, increasing their sales by up to 25%. Amazon also enhanced the Product Opportunity Explorer and Search Analytics Dashboard with new capabilities that help brands analyze marketing campaigns and identify areas to acquire new customers and drive repeat purchases. This new set of industry-leading tools makes it easier for sellers to tap into customer insights and analytics data to launch new products and increase sales.
“We’re focused on supporting sellers as they work to build and grow their business,” said Benjamin Hartman, vice president of Amazon North America Selling Partner Services. “The tools we’re announcing today are a direct result of seller feedback and target every step of their Amazon sales funnel, from new customer acquisition to increased lifetime value. We’re committed to continuing to develop tools and features that deliver actionable insights for sellers.”
“We have been working with Amazon since the beginning, leveraging data to build our business into one of the largest jewelry sellers on Amazon,” said Tal Masica, founder of PAVOI Jewelry. “Thanks to enhancements to the Search Analytics Dashboard and Product Opportunity Explorer, we now have the ability to analyze search trends at a granular level, giving us actionable insights to improve both trend forecasting and design for future collections – so we can continue delivering quality sustainable jewelry that our customers love to wear every day.”
Amazon offers a range of industry-leading tools that empower sellers to optimize their listings, better understand customers, differentiate their brands, and grow their business. The following new tools were announced at Accelerate 2022:
Manage Your Experiments is designed to increase the quality of product detail pages and drive higher conversion. With Manage Your Experiments, brands are able to run A/B tests on their titles, main images, and A+ content to see what performs best. Now, brands can also A/B test bullet points and descriptions, and review machine learning-based recommendations for product images and titles to drive better conversion. Additionally, brands can now opt-in to auto-publish winning experiments to the product detail page, automating their A/B tests. Sellers benefit from traffic from hundreds of millions of Amazon customers, and the new Manage Your Experiments features make it easier to test more content, faster.
Search Analytics Dashboard has expanded since its launch in early 2022 to offer a new insights dashboard that provides sellers with anonymized data to better understand customers’ interests and shopping habits. For the first time, brands can download Search Query and Catalog Performance data and new ASIN-level details. This new capability enables brands to easily assess marketing campaigns to identify areas to drive repeat purchases and acquire new customers—either directly from within Amazon’s tools or by combining Amazon data with the seller’s own business data. The enhanced Search Analytics Dashboard is launching worldwide in September.
Product Opportunity Explorer builds on its successful beta introduction in 2021, continuing to offer rich, accurate data that helps sellers understand, gauge, and evaluate product opportunities in the Amazon store. Sellers can assess the likelihood of a new product gaining traction with customers and forecast sales potential. For the first time, Amazon has now introduced an enhanced Product Opportunity Explorer with a new feature, Customer Reviews Insight. This feature helps sellers work backward from the customer, using customer feedback from product review insights and product star ratings, to help brands determine what features they should build and prioritize as they launch new products or modify existing ones.
Marketplace Product Guidance, initially announced in 2021, has been enhanced to provide Selection Recommendations—products in high demand—for U.S. sellers looking to expand to France, Italy, and Spain. Selection Recommendations give sellers insight as to products not currently offered that fit a seller’s portfolio, surfacing new growth opportunities. The tool takes the guesswork out of which products should be considered in those stores, based on customer demand. These recommendations are personalized and ranked based on their opportunity score as calculated by machine learning models that are designed to predict the best opportunities for new selection.
Every year, Amazon invests billions of dollars to improve the infrastructure, tools, services, fulfillment solutions, and resources dedicated to helping sellers succeed. Sellers are responsible for more than half of Amazon’s physical product sales; sellers in our store employed and provided jobs for more than 1.5 million people in the United States.
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Amazon strives to be Earth’s Most Customer-Centric Company, Earth’s Best Employer, and Earth’s Safest Place to Work. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology, Amazon Studios, and The Climate Pledge are some of the things pioneered by Amazon.
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT
Privacera | September 12, 2022
Privacera, the unified data access governance leader founded by the creators of Apache Ranger™, today announced the availability of its AWS Lake Formation integration in private preview, which offers complete data governance automation and fine-grained data access for AWS services including Amazon S3, Amazon Redshift and Amazon RDS. Privacera helps enterprise data teams protect sensitive data and enable privacy across all on-premise, hybrid and multi-cloud data sources while reducing time to insights by automating outdated, manual governance processes.
Privacera is expanding its support and native integration for diverse AWS environments with the new AWS Lake Formation integration to simplify data access governance for complex and heterogeneous data lake and data mesh environments by extending Lake Formation enforcement to third-party services like Databricks, enabling additional governance use-cases. With this new integration, organizations will be able to accelerate their migration to the cloud by leveraging Privacera to securely manage data access policies within a single governance platform across diverse on-premise and cloud data sources. This will significantly reduce the efforts around data migrations to the cloud through increased automation and consistent policy management, and the ability to ensure compliance through an open, consistent and proven standard.
"Organizations operate in diverse data ecosystems, and it's becoming increasingly challenging to not only manage the data from a governance perspective, but ensure that organizations are gleaning timely insights securely through appropriate access controls and automation, and that's why Privacera exists," said Privacera CEO Balaji Ganesan. "As an AWS partner, expanding our capabilities with this new integration allows us to deliver a solution that leverages the strengths of both Privacera and AWS Lake Formation, helping organizations with a secure and simple approach to data access while delivering business value."
The latest integration will give users:
A unified data governance strategy including your lake formation data assets
AWS Lake Formation policy enforcement extended to popular data analytics systems like Databricks
An intuitive and easy-to-use interface to build data access policies on top of AWS Lake Formation
Financial services company Sun Life uses Privacera to accelerate AWS migration and unify data access governance and compliance. "Because Apache Ranger is critical to the success of our entire analytics platform, so is Privacera as it allows us to capitalize on existing technology and deliver critical data to our analytic teams quicker," said a Director of Cloud Infrastructure & Operations at Sun Life. "Our goal was to get our data into a data lake as quickly as possible and then apply access rules so approved Sun Life professionals can actually use the data to generate important insights. Requests that used to take three to four weeks to program can now be reacted to in less than two days."
Founded in 2016 by the creators of Apache Ranger™, Privacera's SaaS-based data security and governance platform enables analytics teams to simplify data access, security, and privacy for data applications and analytical workloads. The Privacera platform supports compliance with regulations such as GDPR, CCPA, LGPD, and HIPAA. Privacera provides a unified view and control for securing sensitive data across multiple cloud services such as AWS, Azure, Databricks, GCP, Snowflake, and Starburst. The Privacera platform is utilized by Fortune 500 customers across finance, insurance, life sciences, retail, media, and consumer industries, as well as government agencies to automate sensitive data discovery, mask sensitive data, and manage high-fidelity policies at petabyte scale on-premises and in the cloud.
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, PERFORMANCE MANAGEMENT
Talend | November 07, 2022
Talend, a global leader in data integration and data management, today announced it has once again been recognized by Gartner, Inc. as a Leader in data quality solutions in the 2022 Magic Quadrant for Data Quality Solutions 1. This is the fifth consecutive time that Talend has been positioned in the Leaders Quadrant based on the company's ability to execute and completeness of vision.
According to Gartner, Inc., "Driven by transition to more augmented data quality features, the market for data quality solutions is further consolidating with adjacent data and analytics markets such as metadata management, governance platforms, data integration tools and master data management (MDM) solutions. As a result, data and analytics (D&A) leaders and practitioners expect seamless interoperability between these products driven by consolidation and sharing of metadata."
"The ability to establish a trusted data culture and democratize access to trusted data relies on a collaborative approach to data quality. "We believe that this new recognition as a Leader demonstrates the value Talend brings in supporting organizations to take a proactive and integrated path to healthy data."
Jamie Fiorda, CMO, Talend
Talend Data Fabric is the only end-to-end data management platform that provides integrated data quality capabilities to guarantee access and usage of trusted and governed data throughout the data life cycle. In the most recent release, the platform adds advanced features for automation and AI-driven augmentation for data enrichment, data profiling, and data remediation, as well as data observability to face the growing complexity of data stacks and the need for proactively managing data quality.
In the report, Gartner also notes, "By 2024, 90% of data quality technology buying decisions will focus on ease of use, automation, operational efficiency and interoperability as the critical decision factors." Additionally, "By 2026, 20% of large enterprises will use a single data and analytics governance platform to unify and automate discrete governance programs."
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
GARTNER and Magic Quadrant are registered trademarks and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
Talend, a global leader in data integration and data management, is taking the work out of working with data.
Talend offers the only end-to-end platform that combines enterprise-grade data integration, integrity, and governance capabilities to unify data across any cloud, hybrid, or multi-cloud environment. With Talend's no-code and low-code modules, data experts and business users actively collaborate to make data more discoverable, usable, and valuable organization-wide. Over 7,250 customers around the world rely on Talend for healthy data and a healthy business.
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT
Comet | November 17, 2022
Comet, provider of the leading MLOps platform for machine learning (ML) teams from startup to enterprise, today announced a bold new product: Kangas. Open sourced to democratize large scale visual dataset exploration and analysis for the computer vision and machine learning community, Kangas helps users understand and debug their data in a new and highly intuitive way. With Kangas, visualizations are generated in real time; enabling ML practitioners to group, sort, filter, query and interpret their structured and unstructured data to derive meaningful information and accelerate model development.
Data scientists often need to analyze large scale datasets both during the data preparation stage and model training, which can be overwhelming and time-consuming, especially when working on large scale datasets. Kangas makes it possible to intuitively explore, debug and analyze data in real time to quickly gain insights, leading to better, faster decisions. With Kangas, users are able to transform datasets of any scale into clear visualizations.
“A key component of data-centric Machine Learning is being able to understand how your training data impacts model results and where your model predictions are wrong. “Kangas accomplishes both of these goals and dramatically improves the experience for ML practitioners.”
Gideon Mendels, CEO and co-founder of Comet
Putting Large Scale Machine Learning Dataset Analysis at Your Fingertips
Developed with the unique needs of ML practitioners in mind, Kangas is a scalable, dynamic and interoperable tool that allows for the discovery of patterns buried deep within oceans of datasets. With Kangas, data scientists can query their large-scale datasets in a manner that is natural to their problem, allowing them to interact and engage with their data in novel ways.
Noteworthy benefits of Kangas include:
Unparalleled Scalability: Kangas was developed to handle large datasets with high performance.
Purpose Built: Computer Vision/ML concepts like scoring, bounding boxes and more are supported out-of-the-box, and statistics/charts are generated automatically.
Support for Different Forms of Media: Kangas is not limited to traditional text queries. It also supports images, videos and more.
Interoperability: Kangas can run in a notebook, as a standalone local app or even deployed as a web app. It ingests data in a simple format that makes it easy to work with whatever tooling data scientists already use.
Open Source: Kangas is 100% open source and is built by and for the ML community.
Kangas was designed for the entire community, to be embraced by students, researchers and the enterprise. As individuals and teams work to further their ML initiatives, they will be able to leverage the full benefits of Kangas. Being open source, all are able to contribute and further enhance it as well.
“Interoperability and flexibility are inherent in Comet’s value proposition, and Comet aims to expand on that value through open source contributions,” added Mendels. “Kangas is a continuation of all of our efforts, and we couldn’t wait to get its capabilities into the hands of as many data scientists, data engineers and ML engineers as possible. We believe by open sourcing it, Comet can help teams get the most out of their ML projects in ways that have not been possible previously.”
Kangas is available as an open source package for any type of use case. It will be available under Apache License 2 and is open to contributions from community members.
Comet provides an MLOps platform that data scientists and machine learning teams use to manage, optimize, and accelerate the development process across the entire ML lifecycle, from training runs to monitoring models in production. Comet’s platform is trusted by over 150 enterprise customers including Affirm, Cepsa, Etsy, Uber and Zappos. Individuals and academic teams use Comet’s platform to advance research in their fields of study. Founded in 2017, Comet is headquartered in New York, NY with a remote workforce in nine countries on four continents. Comet is free to individuals and academic teams. Startup, team, and enterprise licensing is also available.