
TeraHelix
Date | Investors | Amount | Round |
---|---|---|---|
* | N/A | Acquisition | |
Total Funding | 000k |
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Terahelix.io is a data management startup that offers a unique solution for businesses to handle their data growth without affecting their dependent processes and systems. The company operates in the data management and analytics market, serving clients from various industries that require efficient data handling and analysis.
Terahelix.io's primary service is integrating different data formats, enabling concept and text mining, and providing access to data-driven analytics. This means that they help businesses to bring together data from different sources, extract useful information from it, and use that information to make data-driven decisions. They also offer a service called Data Model Enablement, which validates defined data policies in real time. This means they help businesses ensure their data policies are being followed correctly and immediately.
Another key aspect of Terahelix.io's business model is their focus on automation. They provide a complete and visible data lineage, along with associated metadata, and incorporate a data catalogue to curate and optimise efficient monitoring and quality data. In simpler terms, they help businesses automate the process of tracking where their data comes from and how it's used, and they provide a catalogue of data for easy access and monitoring.
Terahelix.io also offers services to manage data through the provision of qualifiable and quantifiable metrics, engaging templates and blueprints to accelerate the process without constraint. This means they provide tools and templates to help businesses manage their data more efficiently and effectively.
The company makes money by charging clients for these data management and analytics services. Their business model is likely based on a subscription or usage-based pricing model, although specific pricing details are not publicly available.
Keywords: Data Management, Data Analytics, Data Integration, Text Mining, Data Model Enablement, Automation, Data Lineage, Data Catalogue, Data Quality, Data Metrics.