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ZL Technologies Highlights Untapped Power of Centrally-Managed Big Data With Enterprise Analytics Use Cases

Published by: Marketwired

Information Governance Practices Form the Foundation for Transforming Diverse Data Sets Into Previously Unavailable Strategic Insights for the Enterprise

San Jose, CA – May 07, 2015 ZL Technologies, Inc. (ZL), the leader in comprehensive enterprise information governance and analytics, today announced a focus on integrative “Big Data” use cases within the enterprise, highlighting the ZL Enterprise Analytics™ (ZL EA) capacity to combine and cross-analyze data from diverse sources. With the groundbreaking ability to seamlessly manage People Data, Business Data, and Machine Data all in the same governance and analytics platform, ZL encourages organizations to explore potential use cases for enterprise content that may currently sit unmanaged or unaccounted for.

Comprehensive information governance is the concrete foundation for insightful data analysis, yet many businesses still struggle to gain meaningful control of content. This is because the traditional enterprise attitude towards data management has largely been driven by a “reactive” approach: meeting indisputable legal and regulatory requirements for data as they periodically arise. Although this strategy has historically been well-intentioned and objective-driven, it has prominent flaws: (1) it encourages a piecemeal approach to data management, and (2) it largely neglects the potential value of data that is not specifically addressed by external requirements.

ZL’s flagship Unified Archive® platform and ZL Enterprise Analytics™ combine to provide a long-term, comprehensive approach to information governance that enables global analysis across formerly-disparate data streams. With People Data, Business Data, and Machine Data all centrally governed and accessible for analysis within a single “data lake,” there are countless powerful use cases that can be explored:

  • Threat Detection and Security Cross-analyze server log data with unstructured communications data to detect anomalous or suspicious patterns of activity and flag potential threats
  • Customer Experience Management (CEM) Determine the emotional tone of customer/support interactions, and evaluate how such patterns influence subsequent purchase decisions and transactions; optimize existing CEM processes with deeper insights
  • Business Tool Optimization Centrally ingest and integrate data from existing business applications such as ERP systems and CRM platforms, looking for relevant correlations between content and activities
  • Sales Feedback and Productivity Assess and adjust sales procedures by determining which habits have the highest rates of return, and which customer variables correlate to better long-term engagement
  • Workflow and Effort Optimization Aggregate and analyze data from meetings, calendar entries, email, documents, and even collaborative suites to identify overlap in effort and eliminate duplicative work
  • Knowledge and Expertise Detection Pinpoint existing experts and material resources for any given topic, and match the right people to the right tasks based on aptitude and experience
  • Internal Investigations Conduct proactive monitoring across data types, detecting troublesome patterns of interaction and communication long before they become regulatory or legal problems

The ZL ecosystem provides a revolutionary approach to analytics, emphasizing the long-term value of governance architecture. By providing a singular management repository for all People Data, Business Data, and Machine Data, the ZL ecosystem allows diverse data to be leveraged seamlessly together, without the difficulties associated with data movement or sampling. The result is a massively powerful insight engine: one that can detect subtle patterns and deeply integrative relationships that would be impossible to detect with point solutions.

Leveraging multiple data types in concert provides strategic value that exceeds the sum of its parts; the potential use cases are limited only by the imagination and available data sources.