Migrated from eDJGroupInc.com. Author: Greg Buckles. Published: 2015-06-30 20:00:00Format, images and links may no longer function correctly. 

Stephen E. Arnold caught this early (2007) Palantir blog entry attempting to explain what Palantir does (or did from a systems engineer’s perspective) to interview candidates. Although this is incredibly dated, the explanation of the functional components of ‘analysis’ and the human role in them still has merit in light of our eDiscovery relevance challenge. Worth a fast read. Here is my extracted interpretations:

Three core disciplines for large data set analysis:

  1. Data Modeling – ‘ontology’ – understanding what data facets are relevant to the problem
  2. Data Summarization – ‘data mining’ – extraction and summarization of those facets
  3. Data Visualization – ‘navigation’ – dynamic interaction where analyst can iteratively test data

The blogger also lays out five essential features of an analysis platform (somewhat repetitive):

  1. Human-driven queries
  2. Summarization
  3. Visualization
  4. Rapid iteration
  5. Collaboration

As a very sharp statistician commented to me after my first Palantir blog, a lot of the players in the analytic space are still very immature in their approaches to the problem. Although very dated, this blog at least gives me some concept of the types of problems and approaches that Palantir was focused on.

Greg Buckles wants your feedback, questions or project inquiries at Greg@eDJGroupInc.com. Contact him directly for a ‘Good Karma’ call. His active research topics include analytics, mobile device discovery, the discovery impact of the cloud, Microsoft’s 2013 eDiscovery Center and multi-matter discovery. Recent consulting engagements include managing preservation during enterprise migrations, legacy tape eliminations, retention enablement and many more.

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