Cohasset Associates, Inc.

NOTES

2012 Managing Electronic Records Conference 17.1

Big DataRequires 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 Studyy

Challenges

Audience Questions

Cohasset Associates, Inc.

NOTES

2012 Managing Electronic Records Conference 17.2

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 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 etcprocurement, 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 ofGigabytes, terabytes, petabytes, exabytes of

data with few or no retention controls Determining where/how to apply retention:Archive setFile or data setData transaction

Attributes for search and disposition

Cohasset Associates, Inc.

NOTES

2012 Managing Electronic Records Conference 17.3

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 PeriodName 25 years

Pay Data 25 yearsPay Data 25 years

Pay Summary (e.g., W-2) 50 years

Demographics (address changes, etc.) 10 years

Assignments (job class, grade, salary changes, etc.)

10 years

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 recordData base rowDatabase transaction

How to index/search archived data for disposition purposes.

What are industry best practices?

Cohasset Associates, Inc.

NOTES

2012 Managing Electronic Records Conference 17.4

General Requirements & Challenges

Manage retention/disposition at various “record” levels: Archive set File or data set Data transaction 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?