Best Data Discovery Software for Elastic Cloud

Doolytic is at the forefront of big data discovery, integrating data exploration, advanced analytics, and the vast potential of big data. The company is empowering skilled BI users to participate in a transformative movement toward self-service big data exploration, uncovering the inherent data scientist within everyone. As an enterprise software solution, doolytic offers native discovery capabilities specifically designed for big data environments. Built on cutting-edge, scalable, open-source technologies, doolytic ensures lightning-fast performance, managing billions of records and petabytes of information seamlessly. It handles structured, unstructured, and real-time data from diverse sources, providing sophisticated query capabilities tailored for expert users while integrating with R for advanced analytics and predictive modeling. Users can effortlessly search, analyze, and visualize data from any format and source in real-time, thanks to the flexible architecture of Elastic. By harnessing the capabilities of Hadoop data lakes, doolytic eliminates latency and concurrency challenges, addressing common BI issues and facilitating big data discovery without cumbersome or inefficient alternatives. With doolytic, organizations can truly unlock the full potential of their data assets.

The NVISIONx data risk intelligence platform provides organizations with the ability to take charge of their enterprise data, thereby minimizing risks associated with data, compliance requirements, and storage expenses. The exponential growth of data is becoming increasingly unmanageable, leading to heightened challenges for business and security leaders who struggle to secure information they cannot effectively identify. Simply adding more controls will not resolve the underlying issues. With extensive and unlimited analytical capabilities, the platform supports over 150 specific business use cases, equipping data owners and cybersecurity professionals to proactively oversee their data throughout its entire lifecycle. Initially, it is essential to identify and categorize data that is redundant, outdated, or trivial (ROT), which allows companies to determine what can be safely eliminated, thereby streamlining classification efforts and cutting down on storage costs. Subsequently, all remaining data can be contextually classified through a variety of user-friendly data analytics methods, empowering data owners to assume the role of their own analysts. Finally, any data deemed unnecessary or undesirable can undergo thorough legal evaluations and records retention assessments, ensuring that organizations maintain compliance and optimize their data management strategies.