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DataBuckEnsuring the integrity of Big Data Quality is crucial for maintaining data that is secure, precise, and comprehensive. As data transitions across various IT infrastructures or is housed within Data Lakes, it faces significant challenges in reliability. The primary Big Data issues include: (i) Unidentified inaccuracies in the incoming data, (ii) the desynchronization of multiple data sources over time, (iii) unanticipated structural changes to data in downstream operations, and (iv) the complications arising from diverse IT platforms like Hadoop, Data Warehouses, and Cloud systems. When data shifts between these systems, such as moving from a Data Warehouse to a Hadoop ecosystem, NoSQL database, or Cloud services, it can encounter unforeseen problems. Additionally, data may fluctuate unexpectedly due to ineffective processes, haphazard data governance, poor storage solutions, and a lack of oversight regarding certain data sources, particularly those from external vendors. To address these challenges, DataBuck serves as an autonomous, self-learning validation and data matching tool specifically designed for Big Data Quality. By utilizing advanced algorithms, DataBuck enhances the verification process, ensuring a higher level of data trustworthiness and reliability throughout its lifecycle.
What is NVISIONx?
The NVISIONx data risk intelligence platform empowers organizations to take control of their enterprise data, effectively reducing risks tied to data management, compliance obligations, and storage costs. As data continues to grow at an unprecedented rate, businesses and security leaders face mounting difficulties in securing information that is often difficult to pinpoint and manage. Merely increasing the number of controls will not address the fundamental challenges at hand. With its broad and limitless analytical capabilities, the platform caters to over 150 distinct business scenarios, enabling data stewards and cybersecurity experts to take a proactive approach in monitoring their data throughout its complete lifecycle. Initially, it is crucial to pinpoint and categorize data that is redundant, outdated, or trivial (ROT), which helps organizations identify elements that can be safely discarded, thus optimizing classification efforts and reducing storage expenses. Following this initial step, the remaining data can be classified in context using various intuitive data analytics techniques, allowing data owners to act as their own analysts. This process not only enhances understanding but also facilitates better decision-making regarding data usage. Lastly, any unnecessary or undesirable data can be subjected to comprehensive legal reviews and records retention evaluations, ensuring that organizations uphold compliance and refine their data management strategies for the future. By leveraging such a structured approach, companies can significantly enhance their operational efficiency and data governance.
What is MINDely?
MIND is an innovative data security platform that revolutionizes the approach to data loss prevention (DLP) and insider risk management (IRM) by automating processes that allow organizations to quickly identify, detect, and prevent data breaches at unprecedented speeds. This solution actively searches for sensitive data within various file types across multiple IT environments, effectively covering scenarios where data is stored, transmitted, or currently accessed. By identifying and addressing vulnerabilities in sensitive information across a wide array of IT ecosystems, including SaaS platforms, AI applications, endpoints, on-premises file systems, and email communications, MIND guarantees extensive protection. The platform consistently monitors and analyzes billions of data security incidents in real time, delivering enriched context surrounding each occurrence while autonomously executing necessary remediation actions. Additionally, MIND has the capability to instantly block sensitive data from being exposed or to collaborate with users to reduce risks, all while reinforcing organizational policies. Its ability to seamlessly integrate with various data sources within IT infrastructures allows MIND to uncover vulnerabilities in sensitive data, thereby strengthening the overall security framework. Not only does MIND safeguard critical information, but it also promotes a culture of compliance and heightened awareness among users, ultimately leading to a more secure organizational environment. This dual focus on protection and education ensures that users are not only shielded from risks but are also equipped to recognize and respond to potential threats.
Integrations Supported
Box
Confluence
Dropbox
GitHub
Gmail
Google Drive
Microsoft Azure
Microsoft Exchange
Microsoft OneDrive
Microsoft Outlook
Integrations Supported
Box
Confluence
Dropbox
GitHub
Gmail
Google Drive
Microsoft Azure
Microsoft Exchange
Microsoft OneDrive
Microsoft Outlook
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
NVISIONx
Company Location
United States
Company Website
www.nvisionx.ai/
Company Facts
Organization Name
MIND
Company Location
United States
Company Website
mind.io
Categories and Features
Data Discovery
Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Categories and Features
Data Discovery
Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics
Data Loss Prevention
Compliance Reporting
Incident Management
Policy Management
Sensitive Data Identification
Web Threat Management
Whitelisting / Blacklisting
Data Security
Alerts / Notifications
Antivirus/Malware Detection
At-Risk Analysis
Audits
Data Center Security
Data Classification
Data Discovery
Data Loss Prevention
Data Masking
Data-Centric Security
Database Security
Encryption
Identity / Access Management
Logging / Reporting
Mobile Data Security
Monitor Abnormalities
Policy Management
Secure Data Transport
Sensitive Data Compliance