A global survey of mid to large-sized enterprises conducted by IDC in early 2020 estimated that enterprise data expected to double from 1PB to 2.02PB from 2020 to 2022. Survey respondents estimated that about half of the company data was potentially available through their operations, and only about half of that was actually used by the organization – leaving half of all enterprise data either uncaptured or unutilized.
The survey reported that just 22% of data is stored in cloud repositories, while ~70% is stored in internal, 3rd party or remote data centers. Managing enterprise data across multi-cloud dispersed environments is a top challenge for enterprise DataOps and CIOs.
Enterprise data is contained across approximately 300+ document formats, ranging from presentations, multi-page documents, raster images, Computer-Aided Documents, and more. Not all these formats enable users to easily extract data. As enterprise data volumes continue to grow, document transformation into a structured format becomes essential for further data extraction and processing. It is crucial for document transformation to rely on scalable CPU utilization in order to meet processing timeframes and document delivery service levels.
Enter Hyper V
Microsoft’s Hyper-V, codenamed Viridian, can be used to create virtual machines on X86-64 systems running Windows. A server running Hyper V can be configured to expose individual virtual machines to one or more networks.
For enterprise-grade document transformation needs, Hyper V technology enables users to configure and deploy “transformation engines” across multiple CPUs and manage load balancing to accommodate document rendering of various complexity and length.
With the latest release of the Adlib Transform 6.5 Platform, the following elements improve processing capacity in the most cost-effective manner:
Transform’s Job Processing engine can be configured to set priorities in line with document delivery service levels
The Job Processing engine now leverages the Hyper-V operating environment to distribute the workload across multiple CPUs
In addition, the Job Processing engine captures an audit-trail of every incoming request in the transformed document’s meta data and stores it in the enterprise document repositories
Low Blood Pressure = Adlib
We have 20+ years of experience in implementing some of the most complex document transformation projects across life sciences, energy, insurance, financial services, and government. Contact us to help you surface the specific requirements for your document transformation needs.