Data Classification : ANALYTICS

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The fourth dimension of the quantification of File Analysis Software, according to Gartner, is the analyst. Many departments are interested in the products of this area – from IT, through Security and Finance to the legal department. Analytics allows you to group files according to the importance of individual places, analyze their life in the organization or deviations in the classification of files by their users. This text case focuses on adding value to the vision and goals of the organization, such as identifying additional business opportunities.

Gartner addresses analytics in data classifiers as extracting the potential business value associated with unstructured data. It does so by identifying and enabling metadata tags that optimize business operations. This allows to:

  • Provide better access to unstructured data.
  • Facilitate the business use of previously deemed ‘dark’ data.

Metadata tags can provide critical insight into the data, which is usually understood as sufficient analytics within an organization. For example:

Formal CRM systems are typically less used by salespeople due to their complexity and remote access issues. On the other hand, common office applications (such as Word, Excel or Google Docs) are the basis of their daily activities while creating offers for clients. The same case is with messaging applications (Gmail, Microsoft Outlook, HCL Technologies-Lotus Notes, Slack and Skype), they are used as the main medium for internal and external communication of ideas, proposals and key topics. The lack of classification makes this data “invisible” to analysts.

Ironically, most organizations use business intelligence (BI) in their data warehouses (powered by structured databases and applications). Meanwhile, ignoring the larger datasets that are used more often, including the ‘stream of consciousness’, which is why the analysis of unstructured datasets would provide a much better context here.

The key functionalities in the field of analytics are:

  • Metadata Analytics – reporting on both standard and customized metadata gives you the true power of file analytics. Attributes such as file size, owner, title, number, access, modification, and locking data enable IT organizations to better understand and repair unstructured data.

  • Content awareness – most file analysis tools focus on identifying sensitive data such as personally identifiable information, PCI, and PHI. The robustness of many products or available APIs to power other applications allows additional use cases that require knowledge of the content to classify or enforce policies, such as identifying documented processes, data trends, and analytics. Trade-offs in content search can include delays in retrieving results and degraded live system performance during the data mining process.

  • Tagging and Classification – ability to tag a file or object with metadata for future action or decision making, for example with an external enforcement tool (such as DLP). Tagging is based on metadata analysis or content awareness. Classification is the ability to sort artifacts into groups, such as simple categories or taxonomies.

  • Visualization and presentation – file analysis includes visualization functions that provide a wide range of graphical manipulation. Being able to drill down to a detailed level and graphically represent relationships is crucial. The most effective products will have more graphics and fewer lists when working with files.

  • Reporting – Classifier reports contain detailed content information such as owner, subject, and age based on metadata and / or full-text indexing.

Taking into account the above points, if the classification is a project carried out by the security department, it is worth considering who else will benefit from its implementation. Precisely because of their increased analytical capabilities – your data scientists and analysts can be unmatched allies!

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