DataBuck
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Teradata VantageCloud
Teradata VantageCloud: Open, Scalable Cloud Analytics for AI
VantageCloud is Teradata’s cloud-native analytics and data platform designed for performance and flexibility. It unifies data from multiple sources, supports complex analytics at scale, and makes it easier to deploy AI and machine learning models in production. With built-in support for multi-cloud and hybrid deployments, VantageCloud lets organizations manage data across AWS, Azure, Google Cloud, and on-prem environments without vendor lock-in. Its open architecture integrates with modern data tools and standard formats, giving developers and data teams freedom to innovate while keeping costs predictable.
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Neysa Aegis
Aegis provides robust protection for your AI models, effectively preventing issues like model poisoning and safeguarding data integrity, allowing you to confidently implement your AI/ML initiatives in either the cloud or on-premises while maintaining a strong security posture against a constantly changing threat environment. The lack of security in AI/ML tools can widen attack surfaces and significantly increase the risk of security breaches if security teams do not remain vigilant. An inadequate security strategy for AI/ML can lead to severe consequences, including data breaches, operational downtime, loss of profits, damage to reputation, and theft of credentials. Additionally, weak AI/ML frameworks can endanger data science projects, leaving them susceptible to breaches, theft of intellectual property, supply chain vulnerabilities, and manipulation of data. To combat these risks, Aegis employs a comprehensive suite of specialized tools and AI models to scrutinize data within your AI/ML ecosystem as well as information from external sources, ensuring a proactive approach to security in an increasingly complex landscape. This multifaceted strategy not only enhances protection but also supports the overall integrity of your AI-driven operations.
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Causometrix
A comprehensive cloud-based supply chain planning platform designed specifically for manufacturers, distributors, and retailers, it offers a top-tier application experience without the hefty price tag. Featuring a wide array of prebuilt solutions, it also allows for customization to suit all your planning requirements. The platform boasts a highly precise AI/ML demand forecasting engine coupled with innovative exception-based inventory and demand planning solutions. Alongside revenue planning capabilities, this suite enables an extensive sales and operations planning process. Users can try it out with a no-commitment free trial using their own data! This tool stands out as the most user-friendly option for analyzing and manipulating data. Its analytics-driven interface transforms data viewing and entry into an enjoyable and straightforward experience. Additionally, the AI/ML-powered demand forecasting engine intelligently assesses the best level of data aggregation for accurate predictions and adapts to understand how past events and promotions influence future demand trends. This ensures that businesses can stay ahead of market fluctuations while optimizing their inventory management.
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