BIG DATA MANAGEMENT

ADP® DataCloud has been Named "Data Analytics Invention of the Year" in the 2021 Data Breakthrough Awards

ADP | March 25, 2021

ADP, an industry leader in data technology, was named a winner in the 2021 Data Breakthrough Awards for its innovative workforce analytics solution. The solution was named the Data Analytics Innovation of the Year by the independent market intelligence organization for its ability to harness data to address some of the most challenging problems businesses face today.

The Data Breakthrough Awards objective is to honor data technology innovators, pioneers, and visionaries from around the world in a variety of categories such as Data Analytics, Big Data, Business Intelligence, Data Storage, and more. This year, the competitive program received over 1,450 nominations from around the world.

"Businesses have traditionally struggled to identify trends within their people data, and ADP DataCloud represents a significant 'breakthrough' in this area that can result in significant savings and improved retention for organizations," said James Johnson, managing director, Data Breakthrough. "ADP DataCloud addresses some of the biggest challenges that businesses face today, including shifting economic policy, employee retention, and the pay equity gap. We are thrilled to award ADP DataCloud with our 'Data Analytics Innovation of the Year' award and we extend a hearty congratulations to the entire ADP team."

"This past year has demanded that businesses become more agile, as they adapted to the implications of the global pandemic and its impact on the economy and their people," said Jack Berkowitz, senior vice president of product development at ADP. "Businesses have the data they need to make informed decisions, but too often those insights are hidden beneath manual processes. ADP DataCloud uncovers those insights and embeds them directly within the flow of work to give businesses the transparency they need to thrive. From new features to target pay equity gaps and drive inclusion to predictive analytics-driven storyboards that analyze turnover rates, we're constantly raising our aim to give businesses the edge they need."

ADP DataCloud analyzes aggregated, anonymized, and timely HR and salary data from over 740,000 organizations across the country using AI, allowing businesses to benchmark and compare compensation data, turnover rate, and overtime. The approach provides actionable ideas to those who will operate on them by leveraging the scope and depth of "live" data in the broader market.

To assist in navigating the new world of work, the solution includes real-time insights pushed directly to leaders through the ADP Mobile Solutions app; story-driven suggestions on workforce patterns and trends; and Data Mashups, which allow businesses to pull financial and sales data directly through their HCM systems to reveal relationships.


About ADP

ADP's primary emphasis is on providing better ways to work through cutting-edge products, premium services, and exceptional experiences that allow people to reach their full potential. Human Resources, Talent Management, Time Management, Benefits, and Payroll are  Informed by data and the solutions are created with keeping people in mind.

Spotlight

DataRobot offers a machine learning platform for data scientists of all skill levels to build and deploy accurate predictive models in a fraction of the time it used to take. The technology addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics.


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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Privacera Expands Data Governance Capabilities for Cloud Data Lakes with Native Aws Lake Formation Integration

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." About Privacera 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.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Comet Introduces Kangas, An Open Source Smart Data Exploration, Analysis and Model Debugging Tool for Machine Learning

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. About Comet 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.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Neo4j Announces General Availability of its Next-Generation Graph Database Neo4j 5

Neo4j | November 09, 2022

Neo4j®, the leader in graph technology, announced today the general availability of Neo4j 5, the next-generation cloud-ready graph database. Neo4j 5 widens the performance lead of native graphs over traditional databases while providing easier scale-out and scale-up across any deployment, whether on-premises, in the cloud, hybrid, or multi cloud. The result empowers organizations to more quickly create and deploy intelligent applications at large scale and achieve greater value from their data. "Graph technology adoption is accelerating as organizations seek better ways to leverage connections in data to solve complex problems at scale," said Emil Eifrem, CEO and Co-founder of Neo4j. "We designed Neo4j 5 to deliver the type of scalability, agility, and performance that enable organizations to push the envelope on what's possible for their data and their business." Neo4j 5's specific benefits include: Query language improvements and up to 1000x faster query performance. New syntax makes it even easier to write complex pattern-matching queries. Improvements in indexes, query planning, and runtime make Neo4j 5 the fastest implementation ever. For example, multi-hop queries can now be executed up to 1000x faster than Neo4j 4. These improvements are above and beyond the already exponentially faster Neo4j's graph results over traditional databases. Together, these benefits enable more real-time results at scale. Automated scale-out across hundreds of machines, enabling self-managed customers to grow and handle a massive number of queries with little manual effort and significantly less infrastructure cost. This benefit is achieved via new and enhanced features like Autonomous Clustering and Fabric, enabling organizations to efficiently operate very large graphs and scale out in any environment. Neo4j 5 also automates the allocation and reassignment of computing resources. Continuous updates across all deployments, whether in the cloud, multi-cloud, hybrid, or on-prem. Neo4j 5 ensures ongoing compatibility between self-managed and Aura workloads managed by Neo4j. In addition, a new tool called Neo4j Ops Manager provides a unified single pane for easy monitoring and management of global deployments, giving customers full control over their environments. Neo4j 5 performance lead sets a new industry bar More than 1,300 organizations trust Neo4j's technology to power mission-critical applications while maintaining performance, security, and data integrity. Neo4j 5 extends the company's leadership even further at a time when graph adoption is exploding. "Switching to Neo4j was a huge win for us," said David Fox, Senior Software Engineer at Adobe and Co-founder & Engineering Lead at devRant. "We've seen significant performance improvements, and a great reduction in complexity, storage, and infrastructure costs. Staff now focus on improving the infrastructure, versus spending time frustratingly micro-managing it." For more information To learn more about Neo4j 5, visit the Neo4j 5 web page, read "Scale New Heights with Neo4j 5 Graph Database," or register for the following sessions at the upcoming online developer conference NODES 2022: "What's New in Neo4j 5 and Aura 5 for Developers" and "Introducing Neo4j 5 for Administrators." About Neo4j Neo4j is the world's leading graph data platform. We help organizations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Our customers transform their industries by curbing financial fraud and cybercrime, optimizing global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualization, and more.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Variphy Releases Software Version 13.0

Variphy | September 20, 2022

Variphy, the preferred Cisco Unified Collaboration reporting and analytics software solution for over 1,500 businesses, announced today the release of Variphy 13.0, its latest software version. "Our latest release delivers features allowing for an even faster and more streamlined reporting experience with greatly improved scalability," Derek Falter, director of product development, said. "With the addition of Multi-Database Reporting, Auto-Archiving, and Audible Alerts, we make it easier to monitor and report on CUCM, UCCX, and CUBE activities." Variphy 13.0 core feature enhancements include: CDR Auto Database Archiving & Multi-Database Support Variphy overhauled CUCM and CUBE configuration UIs to isolate CDR-specific settings and activation. Auto Database Archiving allows the archive databases to be automatically created for an even more seamless reporting process. Scheduled Call Analytics Report Queueing The update includes a new application setting to determine the maximum number of reports executed simultaneously. CCX CSQ Widget Threshold Audible Alerts An audible alert will be triggered when a configured threshold is breached. The flexible audio configuration is designed to fit any purpose. Report on Individual CUBE Events Variphy 13.0 features the ability to build CUBE CDR reports, widgets, and searches based on individual events. Pill select input was included to allow clients to focus their CDR output on individual events or sequences. Active Directory Authentication Improvements The Active Directory Server form has been updated to include fields to capture an Active Directory Distinguished Name User and Password. CUBE CDR Monitoring CUBE CDR Monitoring alerts users if Variphy does not receive a minimum number of CDRs from CUBE in the previous hour. The update includes other application settings and improvements for seamless monitoring and reporting. Email alerts, CUBE CDR processing, and widget settings are among the enhancements available. Variphy 13.0 is just the latest version in the company's tradition of consistently delivering software updates and new products. Updates are always free for existing users. About Variphy Variphy creates leading-edge UC tools and analytics software solutions to streamline the service delivery and management of Cisco Unified Communications and Collaboration. Since 2004, it has helped over 1,500 organizations visualize, search, analyze, and report on their Cisco UC environments. Product development, sales and marketing, service delivery, and support teams are based in the United States.

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Spotlight

DataRobot offers a machine learning platform for data scientists of all skill levels to build and deploy accurate predictive models in a fraction of the time it used to take. The technology addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics.

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