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Data Dynamics Launches Insight AnalytiX 1.4

Data Dynamics | March 03, 2022

Data Dynamics
The Unified Unstructured Data Management Leader, Data Dynamics, today announced the launch of Insight AnalytiX 1.4, which is focused on increasing the Data Protection and Security Functionalities of the product. To assist enterprises to assure higher precision in PII/sensitive data discovery and updated remediation capabilities, the upgrade provides flexible and scalable data discovery, deep analytics, and decreased data exposure.

Every year, cybersecurity practitioners confront new problems because of several variables. Attacks by threat actors and the ever-evolving architecture of IT infrastructure, including cloud migration, in addition to external impacts like the pandemic, are all considerations to be considered. As a consequence of the increase and evolution of cyber-attacks, every company is more focused on safeguarding business-critical and personal data.

Insight AnalytiX 1.4 has a number of new features that make it an outstanding tool for identifying and mitigating risks. Users may now create a Data Insight report on a dataset utilizing complex multi-level logical expressions and a mix of logical operators in the newest edition of Insight AnalytiX. It decreases the likelihood of sensitive personal data being overlooked, assuring the most significant level of data discovery accuracy. Deep analytics, both descriptive and diagnostic, are used in the report to enable businesses to gain a clear picture of the risk they face and a simple way to quantify it.

"The latest upgrade in Insight Analytix builds a strong data security shield using scalable and flexible data discovery application and deep analytics. The upgraded risk identification and remediation workflow will certainly help enterprises protect their sensitive data from growing cyber threats."

Helen Johnson, CTO of Data Dynamics

When combined with Data Dynamics' unified data management platform, Insight AnalytiX 1.4 can help businesses unleash end-to-end data management features, including Data Analytics, Data Mobility, Data Security, and Data Compliance.

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Understanding User Entity and Behavior Analytics (UEBA)

User Entity and Behavior Analytics (UEBA) is a cybersecurity technology and approach that focuses on analyzing the behavior of users and entities (such as devices, applications, and systems) within an organization's IT environment. By using advanced data analytics, machine learning algorithms, and artificial intelligence, UEBA aims to detect and prevent cyber threats by identifying anomalies, deviations, or patterns in user and entity activities that might indicate potential security risks.

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Spotlight

Understanding User Entity and Behavior Analytics (UEBA)

User Entity and Behavior Analytics (UEBA) is a cybersecurity technology and approach that focuses on analyzing the behavior of users and entities (such as devices, applications, and systems) within an organization's IT environment. By using advanced data analytics, machine learning algorithms, and artificial intelligence, UEBA aims to detect and prevent cyber threats by identifying anomalies, deviations, or patterns in user and entity activities that might indicate potential security risks.

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Neo4j Adds Vector Search Capability Within Its Native Graph Database for Richer Generative AI Insights

PR Newswire | August 23, 2023

Neo4j®, the world's leading graph database and analytics company, announced that it has integrated native vector search as part of its core database capabilities. The result enables customers to achieve richer insights from semantic search and generative AI applications, and serve as long-term memory for LLMs, all while reducing hallucinations. Neo4j's graph database can be used to create knowledge graphs, which capture and connect explicit relationships between entities, enabling AI systems to reason, infer, and retrieve relevant information effectively. The result ensures more accurate, explainable, and transparent outcomes for LLMs and other generative AI applications. By contrast, vector searches capture implicit patterns and relationships based on items with similar data characteristics, rather than exact matches, which are useful when searching for similar text or documents, making recommendations, and identifying other patterns. "We see value in combining the implicit relationships uncovered by vectors with the explicit and factual relationships and patterns illuminated by graph," said Emil Eifrem, Co-Founder and CEO, Neo4j. "Customers when innovating with generative AI also need to trust that the results of their deployments are accurate, transparent, and explainable. With LLMs evolving so dynamically, Neo4j has become foundational for enterprises seeking to push the envelope on what's possible for their data and their business." Neo4j today powers generative AI deployments for multiple Fortune 500 enterprises that include an Asia-based energy multinational, a US-based pharmaceutical manufacturer, and an EMEA-based information and analytics leader, among others. The June 2023 Gartner® report, AI Design Patterns for Knowledge Graphs and Generative AI, states that Knowledge graphs provide the perfect complement to LLM-based solutions where high thresholds of accuracy and correctness need to be attained." This latest advancement follows from Neo4j's recent product integration with Google Cloud's generative AI features in Vertex AI in June, enabling users to transform unstructured data into knowledge graphs, which users can then query using natural language and ground their LLMs against factual set of patterns and criteria to prevent hallucinations. Neo4j's native graph database became fully integrated with Microsoft Azure in April 2023. In December 2022, the company was recognized for the first time in the Gartner®️ Magic Quadrant™️ for Cloud Database Management Systems, which was the first time that native graph vendors were included. Neo4j created the property graph model and is the leading graph database in the market, used by more than 75 of the Fortune 100. More details on Neo4j's vector index search capability can be found here. GARTNER and MAGIC QUADRANT are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 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. About Neo4j Neo4j, the Graph Database & Analytics leader, helps organizations find hidden relationships and patterns across billions of data connections deeply, easily and quickly. Customers leverage the structure of their connected data to reveal new ways of solving their most pressing business problems, from fraud detection, customer 360, knowledge graphs, supply chain, personalization, IoT, network management, and more – even as their data grows. Neo4j's full graph stack delivers powerful native graph storage, data science, advanced analytics, and visualization, with enterprise-grade security controls, scalable architecture and ACID compliance. Neo4j's community of data leaders comprises a vibrant, open-source community of more than 250,000 developers, data scientists, and architects across hundreds of Fortune 500 companies, government agencies and NGOs. Visit neo4j.com.

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IBM Releases Watsonx AI with Generative AI Models for Data Governance

IBM | September 08, 2023

IBM announces plans to enhance its Watsonx AI and data platform, with a focus on scaling AI impact for enterprises. Key improvements include new generative AI models, integration of foundation models, and features like Tuning Studio and Synthetic Data Generator. IBM emphasizes trust, transparency, and governance in training and plans to incorporate AI into its hybrid cloud solutions, although implementation difficulty and cost may be issues. IBM reveals its plans to introduce new generative AI foundation models and enhancements to its Watsonx AI and data platform. The goal is to provide enterprises with the tools they need to scale and accelerate the impact of AI in their operations. These improvements include a technical preview for watsonx.governance, the addition of new generative AI data services to watsonx.data, and the integration of watsonx.ai foundation models into select software and infrastructure products. Developers will have the opportunity to explore these capabilities and models at the IBM TechXchange Conference, scheduled to take place from September 11 to 14 in Las Vegas. The upcoming AI models and features include: 1. Granite Series Models: IBM plans to launch its Granite series models, utilizing the ‘Decoder’ architecture, is essential for large language models (LLMs). These models will support various enterprise natural language processing (NLP) tasks, including summarization, content generation, and insight extraction, with planned availability in Q3 2023. 2. Third-Party Models: IBM is currently offering Meta's Llama 2-chat 70 billion parameter model and the StarCoder LLM for code generation within watsonx.ai on IBM Cloud. IBM places a strong emphasis on trust and transparency in its training process for foundation models. They follow rigorous data collection procedures and include control points to ensure responsible deployments in terms of governance, risk assessment, privacy, bias mitigation, and compliance. IBM also intends to introduce new features across the watsonx platform: For Watsonx.ai: Tuning Studio: IBM plans to release the Tuning Studio, featuring prompt tuning, allowing clients to adapt foundation models to their specific enterprise data and tasks. This is expected to be available in 3Q23. Synthetic Data Generator: IBM has launched a synthetic data generator, enabling users to create artificial tabular data sets for AI model training, reducing risk and accelerating decision-making. For Watsonx.data: Generative AI: IBM aims to incorporate generative AI capabilities into watsonx.data to help users discover, augment, visualize, and refine data for AI through a self-service, natural language interface. This feature is planned for technical preview in 4Q 2023. Vector Database Capability: IBM plans to integrate vector database capabilities into watsonx.data to support watsonx.ai retrieval and augmented generation use cases, also expected in the technical preview in 4Q 2023. For Watsonx.governance: Model Risk Governance for Generative AI: IBM is launching a tech preview for watsonx.governance, providing automated collection and documentation of foundation model details and model risk governance capabilities. Dinesh Nirmal, Senior Vice President, Products, IBM Software, stated that IBM is dedicated to supporting clients throughout the AI lifecycle, from establishing foundational data strategies to model tuning and governance. Additionally, IBM will offer AI assistants to help clients scale AI's impact across various enterprise use cases, such as application modernization, customer care, and HR and talent management. IBM also intends to integrate watsonx.ai innovations into its hybrid cloud software and infrastructure products, including intelligent IT automation and developer services. IBM's upgrades to the Watsonx AI and data platform offer promise but, come with potential drawbacks. Implementation complexity and the need for additional training may create a steep learning curve. The associated costs of advanced technology could be prohibitive for smaller organizations. The introduction of generative AI and synthetic data raises data privacy and security concerns. Additionally, despite efforts for responsible AI, the risk of bias in models necessitates ongoing vigilance to avoid legal and ethical issues.

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FrogData Launches WarrantyMind AI to Optimize Cash Flow for Dealerships

PR Newswire | August 22, 2023

FrogData, a leading provider of AI & Decision Analytics solutions for the automotive industry, today announced the launch of WarrantyMind AI, an end-to-end remote warranty administration service designed to optimize the warranty claims process for dealerships. As consumer demand rises for extended and electric vehicle warranties, properly filing claims is essential for cash flow. WarrantyMind AI ensures dealerships maximize this vital revenue stream. At the core of WarrantyMind AI is FrogData's cutting-edge AI platform that analyzes every repair order to extract key data points. This allows the remote staff to understand repair details, identify missing information, and determine optimal claims submission approach. The AI tools also automatically flag high-risk claims that require priority handling to avoid delays or rejections. By combining seasoned warranty experts with AI-driven insights and automation, WarrantyMind AI delivers increased accuracy, faster completion times, and higher approval rates for dealership warranty claims. The remote staff act as an extension of the dealership team with FrogData's integrated communications platform. WarrantyMind AI also provides dealerships with ongoing analytics and recommendations to continuously improve the warranty process. Dealers can identify technicians needing more training on repair documentation or claims trends that may require negotiations with OEMs. These insights help dealerships maximize future warranty revenue and margin opportunities. Expert staff add an additional layer of quality assurance, leveraging their experience to ensure accuracy and completeness. Decision Analytics also provide data-driven insights to continuously improve warranty operations. Together, the automation and expertise provided by WarrantyMind AI enables dealerships to realize the full revenue potential of their warranty business. Key benefits include: Increased claim approval rates and faster payment timelines to optimize cash flow Reduced administrative costs through streamlined, automated processing Detailed analytics revealing opportunities to maximize future warranty profitability "WarrantyMind AI utilizes cutting-edge AI and advanced data analytics to revolutionize warranty management," said Tej Soni, FrogData CEO. "Our intelligent automation streamlines the claims process while providing dealerships with data-driven insights to continuously improve. This powerful combination of technology optimizes both the speed and quality of warranty revenue." FrogData currently serves hundreds of dealership clients with its Data Analytics and AI platform. WarrantyMind AI provides an innovative new service to maximize a vital dealership revenue stream. For more information, visit www.frogdata.com About FrogData Headquartered in San Francisco CA, FrogData is the leader in AI and decision analytics solutions for the automotive industry. FrogData develops pioneering data-driven software and services that increase productivity and profitability for dealerships nationwide. FrogData partners with hundreds of dealers of all sizes to drive smart, data-informed business strategies and operations.

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Neo4j Adds Vector Search Capability Within Its Native Graph Database for Richer Generative AI Insights

PR Newswire | August 23, 2023

Neo4j®, the world's leading graph database and analytics company, announced that it has integrated native vector search as part of its core database capabilities. The result enables customers to achieve richer insights from semantic search and generative AI applications, and serve as long-term memory for LLMs, all while reducing hallucinations. Neo4j's graph database can be used to create knowledge graphs, which capture and connect explicit relationships between entities, enabling AI systems to reason, infer, and retrieve relevant information effectively. The result ensures more accurate, explainable, and transparent outcomes for LLMs and other generative AI applications. By contrast, vector searches capture implicit patterns and relationships based on items with similar data characteristics, rather than exact matches, which are useful when searching for similar text or documents, making recommendations, and identifying other patterns. "We see value in combining the implicit relationships uncovered by vectors with the explicit and factual relationships and patterns illuminated by graph," said Emil Eifrem, Co-Founder and CEO, Neo4j. "Customers when innovating with generative AI also need to trust that the results of their deployments are accurate, transparent, and explainable. With LLMs evolving so dynamically, Neo4j has become foundational for enterprises seeking to push the envelope on what's possible for their data and their business." Neo4j today powers generative AI deployments for multiple Fortune 500 enterprises that include an Asia-based energy multinational, a US-based pharmaceutical manufacturer, and an EMEA-based information and analytics leader, among others. The June 2023 Gartner® report, AI Design Patterns for Knowledge Graphs and Generative AI, states that Knowledge graphs provide the perfect complement to LLM-based solutions where high thresholds of accuracy and correctness need to be attained." This latest advancement follows from Neo4j's recent product integration with Google Cloud's generative AI features in Vertex AI in June, enabling users to transform unstructured data into knowledge graphs, which users can then query using natural language and ground their LLMs against factual set of patterns and criteria to prevent hallucinations. Neo4j's native graph database became fully integrated with Microsoft Azure in April 2023. In December 2022, the company was recognized for the first time in the Gartner®️ Magic Quadrant™️ for Cloud Database Management Systems, which was the first time that native graph vendors were included. Neo4j created the property graph model and is the leading graph database in the market, used by more than 75 of the Fortune 100. More details on Neo4j's vector index search capability can be found here. GARTNER and MAGIC QUADRANT are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 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. About Neo4j Neo4j, the Graph Database & Analytics leader, helps organizations find hidden relationships and patterns across billions of data connections deeply, easily and quickly. Customers leverage the structure of their connected data to reveal new ways of solving their most pressing business problems, from fraud detection, customer 360, knowledge graphs, supply chain, personalization, IoT, network management, and more – even as their data grows. Neo4j's full graph stack delivers powerful native graph storage, data science, advanced analytics, and visualization, with enterprise-grade security controls, scalable architecture and ACID compliance. Neo4j's community of data leaders comprises a vibrant, open-source community of more than 250,000 developers, data scientists, and architects across hundreds of Fortune 500 companies, government agencies and NGOs. Visit neo4j.com.

Read More

Big Data Management

IBM Releases Watsonx AI with Generative AI Models for Data Governance

IBM | September 08, 2023

IBM announces plans to enhance its Watsonx AI and data platform, with a focus on scaling AI impact for enterprises. Key improvements include new generative AI models, integration of foundation models, and features like Tuning Studio and Synthetic Data Generator. IBM emphasizes trust, transparency, and governance in training and plans to incorporate AI into its hybrid cloud solutions, although implementation difficulty and cost may be issues. IBM reveals its plans to introduce new generative AI foundation models and enhancements to its Watsonx AI and data platform. The goal is to provide enterprises with the tools they need to scale and accelerate the impact of AI in their operations. These improvements include a technical preview for watsonx.governance, the addition of new generative AI data services to watsonx.data, and the integration of watsonx.ai foundation models into select software and infrastructure products. Developers will have the opportunity to explore these capabilities and models at the IBM TechXchange Conference, scheduled to take place from September 11 to 14 in Las Vegas. The upcoming AI models and features include: 1. Granite Series Models: IBM plans to launch its Granite series models, utilizing the ‘Decoder’ architecture, is essential for large language models (LLMs). These models will support various enterprise natural language processing (NLP) tasks, including summarization, content generation, and insight extraction, with planned availability in Q3 2023. 2. Third-Party Models: IBM is currently offering Meta's Llama 2-chat 70 billion parameter model and the StarCoder LLM for code generation within watsonx.ai on IBM Cloud. IBM places a strong emphasis on trust and transparency in its training process for foundation models. They follow rigorous data collection procedures and include control points to ensure responsible deployments in terms of governance, risk assessment, privacy, bias mitigation, and compliance. IBM also intends to introduce new features across the watsonx platform: For Watsonx.ai: Tuning Studio: IBM plans to release the Tuning Studio, featuring prompt tuning, allowing clients to adapt foundation models to their specific enterprise data and tasks. This is expected to be available in 3Q23. Synthetic Data Generator: IBM has launched a synthetic data generator, enabling users to create artificial tabular data sets for AI model training, reducing risk and accelerating decision-making. For Watsonx.data: Generative AI: IBM aims to incorporate generative AI capabilities into watsonx.data to help users discover, augment, visualize, and refine data for AI through a self-service, natural language interface. This feature is planned for technical preview in 4Q 2023. Vector Database Capability: IBM plans to integrate vector database capabilities into watsonx.data to support watsonx.ai retrieval and augmented generation use cases, also expected in the technical preview in 4Q 2023. For Watsonx.governance: Model Risk Governance for Generative AI: IBM is launching a tech preview for watsonx.governance, providing automated collection and documentation of foundation model details and model risk governance capabilities. Dinesh Nirmal, Senior Vice President, Products, IBM Software, stated that IBM is dedicated to supporting clients throughout the AI lifecycle, from establishing foundational data strategies to model tuning and governance. Additionally, IBM will offer AI assistants to help clients scale AI's impact across various enterprise use cases, such as application modernization, customer care, and HR and talent management. IBM also intends to integrate watsonx.ai innovations into its hybrid cloud software and infrastructure products, including intelligent IT automation and developer services. IBM's upgrades to the Watsonx AI and data platform offer promise but, come with potential drawbacks. Implementation complexity and the need for additional training may create a steep learning curve. The associated costs of advanced technology could be prohibitive for smaller organizations. The introduction of generative AI and synthetic data raises data privacy and security concerns. Additionally, despite efforts for responsible AI, the risk of bias in models necessitates ongoing vigilance to avoid legal and ethical issues.

Read More

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FrogData Launches WarrantyMind AI to Optimize Cash Flow for Dealerships

PR Newswire | August 22, 2023

FrogData, a leading provider of AI & Decision Analytics solutions for the automotive industry, today announced the launch of WarrantyMind AI, an end-to-end remote warranty administration service designed to optimize the warranty claims process for dealerships. As consumer demand rises for extended and electric vehicle warranties, properly filing claims is essential for cash flow. WarrantyMind AI ensures dealerships maximize this vital revenue stream. At the core of WarrantyMind AI is FrogData's cutting-edge AI platform that analyzes every repair order to extract key data points. This allows the remote staff to understand repair details, identify missing information, and determine optimal claims submission approach. The AI tools also automatically flag high-risk claims that require priority handling to avoid delays or rejections. By combining seasoned warranty experts with AI-driven insights and automation, WarrantyMind AI delivers increased accuracy, faster completion times, and higher approval rates for dealership warranty claims. The remote staff act as an extension of the dealership team with FrogData's integrated communications platform. WarrantyMind AI also provides dealerships with ongoing analytics and recommendations to continuously improve the warranty process. Dealers can identify technicians needing more training on repair documentation or claims trends that may require negotiations with OEMs. These insights help dealerships maximize future warranty revenue and margin opportunities. Expert staff add an additional layer of quality assurance, leveraging their experience to ensure accuracy and completeness. Decision Analytics also provide data-driven insights to continuously improve warranty operations. Together, the automation and expertise provided by WarrantyMind AI enables dealerships to realize the full revenue potential of their warranty business. Key benefits include: Increased claim approval rates and faster payment timelines to optimize cash flow Reduced administrative costs through streamlined, automated processing Detailed analytics revealing opportunities to maximize future warranty profitability "WarrantyMind AI utilizes cutting-edge AI and advanced data analytics to revolutionize warranty management," said Tej Soni, FrogData CEO. "Our intelligent automation streamlines the claims process while providing dealerships with data-driven insights to continuously improve. This powerful combination of technology optimizes both the speed and quality of warranty revenue." FrogData currently serves hundreds of dealership clients with its Data Analytics and AI platform. WarrantyMind AI provides an innovative new service to maximize a vital dealership revenue stream. For more information, visit www.frogdata.com About FrogData Headquartered in San Francisco CA, FrogData is the leader in AI and decision analytics solutions for the automotive industry. FrogData develops pioneering data-driven software and services that increase productivity and profitability for dealerships nationwide. FrogData partners with hundreds of dealers of all sizes to drive smart, data-informed business strategies and operations.

Read More

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