Recommind Adds Machine-Learning Intel to Data Governance Suite

 
 
By Chris Preimesberger  |  Posted 2013-06-19 Email Print this article Print
 
 
 
 
 
 
 

The new information governance suite manages and organizes overwhelming amounts of unstructured data using intelligent machine-learning IT.

As data loads continue to pour into IT systems from humans and machines with no slowdown in sight, the control of security, management and discoverability of all that business-related content becomes an increasingly important mandate.

With all of this in mind, unstructured data management specialist Recommind on June 19 came out with its new Information Governance Suite (IGS), which manages and organizes overwhelming amounts of unstructured data using machine-learning IT.

San Francisco-based Recommind, which made its reputation in the legal e-discovery sector, has added to its package something it calls Predictiv Governance, which combines machine-learning software with human expertise to automate tasks such as data identification, retention, migration, management and deletion. By adding intelligence to the governance process, Recommind said, the costs, regulatory risks and organizational strain of information overload are lessened.

IGS runs on Recommind's Context Optimized Relevancy Engine (CORE) and consists of four integrated modules for data management, early case assessment, collection and review and analysis.

IGS uses the single unified index of the CORE platform to address the full range of governance tasks, including data identification, policy-based remediation, migration, deletion and e-discovery. By indexing information once in CORE, IT can proactively manage data and legal can reactively review it, all in one system using a completely defensible process, Recommind said.

Here are some some key business benefits of the new suite, according to Recommind:

Reduces regulatory risk: IGS ensures that organizations keep the right data for legal, regulatory and business purposes. By accurately and consistently classifying huge amounts of information, it enables organizations to comply with regulations while maintaining employee and customer data privacy.

Reduces e-discovery risk: Finding, securing and moving data into a separate e-discovery system introduces serious risks related to metadata changes, spoliation and chain of custody concerns. Using IGS, organizations can accurately process and produce data on one integrated platform, without risky data handoffs, Recommind said.

Reduces volumes of unneeded data: To reduce storage and e-discovery costs and comply with retention policies, organizations are looking for ways to identify and delete unneeded material from terabytes of unstructured information. IGS uses human-driven intelligent categorization to automate this process and make it fully defensible, Recommind said.

Helps migrate data: Many enterprises want to migrate content from legacy IT systems, such as file servers and records management systems such as Microsoft SharePoint, to distributed infrastructures such as Hadoop. Using "train by example" and machine-learning techniques, IGS automates data classification so that organizations can move content from older systems to newer ones with speed and accuracy.

Helps protect sensitive information: Most information stored on enterprise file shares, email systems, SharePoint repositories and employee workstations is unknown to the wider organization. IGS accurately finds, indexes, categorizes and anonymizes all sensitive enterprise information so that proper access controls and security can be applied, Recommind said.

The U.S. Department of Energy is included in Recommind's customer list. Others include AstraZeneca, BMW, Cisco Systems, Clifford Chance, Marathon Oil and Morgan Lewis.

 
 
 
 
 
 
 
 
 
 
 
 
 

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