M12S17 - Big Data Requires Big ERM!


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Richard (Dick) Fisher

Organizations are creating data records at a pace few could have imagined just five years ago - terabytes (1 trillion bytes) now and heading toward petabytes (1,000 terabytes) that may need to be archived or disposed of! This session uses the requirement for archiving and disposition of PeopleSoft records and data elements as one example, plus other real world requirements.

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M12S17 - Big Data Requires Big ERM!

  1. 1. Cohasset Associates, Inc. NOTES Big Data Requires Big ERM Session 17 – Panel Discussion Richard Fisher, Cohasset Associates, Inc. and Panel Members Panelists  EMC  Christopher D. Preston Senior Director, Integrated Technology Strategy  IBM Corporation  Jake Frazier, JD, MBA, Worldwide Information Lifecycle Governance Solutions  Autonomy, an HP Company  Manu Chadha Vice President of Sales, Americas Topics  Where and What is Big Data?  What Does it Mean to ERM  Focus - Case Study y  Challenges  Audience Questions2012 Managing Electronic RecordsConference 17.1
  2. 2. Cohasset Associates, Inc. NOTES BIG DATA - Where is it?  Have you done your “Data Map” yet?  “Buzz word” since 2006 changes to Rule 26(f) of Federal Rules of Civil Procedure  Inventory or Roadmap of Electronically Stored Information (ESI)  “Big” is relative  Gigabytes, terabytes, petabytes, exabytes – Depends on size of organization and velocity/volume of data Big Data – What Is It? Examples  Large scale e-commerce transactions  Many large-volume business operation databases or file-based data records, e.g., HR, accounting, procurement, etc. procurement etc  Social network communications, postings  Internet text & documents  Scientific research  Medical records  Other? What Does it Mean to ERM?  To ERM, Big Data is NOT:  Business analytics/trends – a typical IT focus for Big Data  To ERM, Big Data is:  Gigabytes, terabytes, petabytes, exabytes of data with few or no retention controls  Determining where/how to apply retention: Archive set File or data set Data transaction  Attributes for search and disposition2012 Managing Electronic RecordsConference 17.2
  3. 3. Cohasset Associates, Inc. NOTES Big Data – Case Study  PeopleSoft HRIS - Current Situation  340 Gigabytes growing at 15%/yr.  17,000 tables  20 tables with 10,000,000 rows of data , ,  Over 33,000 data elements  No current destruction for eligible records/rows/transactions.  Archiving is done, but does not solve disposition problem. Big Data – Case Study?  Database Element Retention Type of Employee Data Retention Period Name 25 years Pay Data 25 years Pay Summary (e.g., W-2) 50 years Demographics (address changes, etc.) 10 years Assignments (job class, grade, salary 10 years changes, etc.) Time/Attendance Data 7 years Big Data – Case Study  Requirements:  Retention periods vary by need – from 8 to 25 years or more.  At what level can retention be applied: Data base record Data base row Database transaction  How to index/search archived data for disposition purposes.  What are industry best practices?2012 Managing Electronic RecordsConference 17.3
  4. 4. Cohasset Associates, Inc. NOTES General Requirements & Challenges  Manage retention/disposition at various “record” levels:  Archive set  File or data set  Data transaction  Automation may be mandatory for classification, retention & disposition in order to handle the record volume.  Use “Categorization” or other “Analytics” to classify/apply retention? Big Data Questions?2012 Managing Electronic RecordsConference 17.4