Controlling a Data Footprint – How to Build a Data Disposition Framework
summary
Claims a workable, incremental way to stop collecting and hoard data: map data elements to owners, pick three retention buckets (under 1 year, 1–3 years, >3 years), use data discovery tools to tag files and move stale items to quarantine, run a 45-day manual review, then destroy with vetted methods. Uses tokenization/masking/anonymization/encryption, highlights a 100 PB example (50 PB personal) across 20 business units, and reports that with executive buy-in breach response meets 72‑hour notification targets.
AI-written summary · about
chapters (22)
- 0:00Data disposition frameworks intro
- 1:02Drivers for data lifecycle
- 4:13Why retain data long term
- 6:22Regulatory retention complexity
- 8:46Legal holds and litigation risks
- 10:53Operational retention reasons
- 13:01Identifying sensitive data
- 15:07Partnering with data owners
- 18:15Manual effort challenges
- 21:49Disposition workflow samples
- 22:12Data governance foundation
- 25:21Building disposition matrix
- 27:30Sensitive data identification
- 29:21Disposition framework components
- 30:27Personal data disposition example
- 33:38IP protection strategies
- 35:21Finding sensitive data in files
- 38:10Metadata correlation across DBs
- 39:32Automating data destruction
- 43:06Monitoring and maintenance
- 44:09Implementation roadmap
- 44:51Q&A
related talks
24mStop Making Your Data Team the 'Data Police'
43mThe Death of Data: Risk, Retention, and Rot
13mBuilding Privacy Products: Field Notes
24mData’s Rotten Scoundrels – How To Avoid Becoming One
20mThe Journey To Data Stewardship: A Practical Framework To Future Proof Your Privacy Program
01h 59mDeep Dive Workshop – Data Security and Data Protection
47mPanel: Making The Most Of Data Governance
01h 59mDeep Dive Workshop – Data Security and Data Protection