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Consolidating accessing analyzing unstructured data

Machine data is another category, one that's growing quickly in many organizations.

For example, log files from websites, servers, networks and applications -- particularly mobile ones -- yield a trove of activity and performance data.

In addition, companies increasingly capture and analyze data from sensors on manufacturing equipment and other internet of things (Io T) connected devices.

In some cases, such data may be considered to be semi-structured -- for example, if metadata tags are added to provide information and context about the content of the data.

Information Leaders Must Understand the Gaps in Data Lake Concept and Take Necessary Precautions The growing hype surrounding data lakes is causing substantial confusion in the information management space, according to Gartner, Inc.

Traditional structured data, such as the transaction data in financial systems and other business applications, conforms to a rigid format to ensure consistency in processing and analyzing it.

Sets of unstructured data, on the other hand, can be maintained in formats that aren't uniform, freeing analytics teams to work with all of the available data without necessarily having to consolidate and standardize it first.

Long-term, highly granular activity, vulnerability, entitlement and audit information may be consolidated in a low-cost, data security data lake, providing improved access to information while helping streamline data collection and data management and reducing costs.

Provides authorized users (such as auditors, security analysts, and other users) with secure, direct access and self-service reporting capabilities to speed time to insights and to help Guardium administrators become less involved in data management and access issues, and more focused on data security, data protection and compliance progress.

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