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What is Datumize Data Collector?

Data is the cornerstone of all digital transformation initiatives. Many projects face hurdles stemming from the false belief that data quality and availability are inherently assured. However, the reality is that sourcing relevant data can often be a difficult, expensive, and disruptive endeavor. The Datumize Data Collector (DDC) acts as an adaptable and lightweight middleware solution, engineered to extract data from complex, often fleeting, and legacy data sources. This type of data tends to remain underutilized due to a lack of accessible retrieval methods. By empowering organizations to collect data from diverse sources, DDC enhances comprehensive edge computing capabilities and allows for the integration of third-party applications, including AI models, while effortlessly formatting and storing the output as needed. Overall, DDC offers a pragmatic strategy for companies aiming to optimize their digital transformation processes through the effective collection of vital operational and business data. Its ability to connect intricate data landscapes with actionable insights not only assists organizations in making informed decisions but also solidifies its role as an essential asset in today’s increasingly data-centric environment.

What is AutoDiscovery?

AutoDiscovery is a sophisticated software solution crafted for automated exploratory data analysis, enabling biomedical researchers to uncover complex relationships hidden within their experimental and clinical trial data. This innovative tool autonomously identifies the appropriate statistical tests required to assess the connections between different variable combinations in each data subset. AutoDiscovery effectively tackles prevalent challenges in biomedical research, such as discerning cause-effect relationships, managing false discovery rates, addressing small sample sizes, organizing treatment groups, and ensuring result traceability. Specifically designed for Principal Investigators, who often face time constraints and may lack extensive statistical knowledge, it allows them to focus on conducting significant and productive research endeavors. Additionally, this software streamlines research workflows, promoting faster insights and breakthroughs in the biomedical domain while empowering researchers to make data-driven decisions with confidence. By leveraging AutoDiscovery, researchers can enhance the overall quality of their findings and contribute more effectively to advancements in health science.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

€1.795 per year
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

Datumize

Date Founded

2014

Company Location

Spain

Company Website

www.datumize.com

Company Facts

Organization Name

Butler Scientifics

Date Founded

2014

Company Location

Spain

Company Website

www.butlerscientifics.com

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

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