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Managing Shared Content Part 1 - Susan J. Sullivan, CRM; NARA Part 2 - Tim Shinkle, Gimmal 1.

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Presentation on theme: "Managing Shared Content Part 1 - Susan J. Sullivan, CRM; NARA Part 2 - Tim Shinkle, Gimmal 1."— Presentation transcript:

1 Managing Shared Content Part 1 - Susan J. Sullivan, CRM; NARA Part 2 - Tim Shinkle, Gimmal 1

2 Target Outcomes Knowledge of: How NARA combined people and technology to meet the shared drive cleanup challenge, and How cleaning up shared drives can lead to efficient management of all electronic content. How people interact with Indexing and Categorization (I&C) Management Tools to manage content Start thinking how you can do this in your organization 2

3 First meeting with the CIO as Records Officer Clean up those shared drives! ARMA is in Florida this year. 3 *This is a dramatization

4 A bit of luck at ARMA ARMA How to Clean Shared Drives May I have your card? 4 *This is a dramatization

5 Services Contract Award winning RM experts Approach rooted in records management Appliance with crawlers Data gathering period, pre-crawls Pilot project, draft rules – cleanup pilot Information Management Plan Communications Plan (posters wiki, meetings) Met and worked with 36 SMEs, crawled and reported, validated, cleaned content 5

6 Overall Process Business Assessment Content Assessment Information Management Plan Human Validation Human Validation Cleanup Policies Litigation Preservation Organization Migration 6

7 People + Tools = Cleanup 7 PeopleTechnology Where is the content?Pre-crawl for overview What to protect / preserve?Find /lock preservation content Develop and validate auto-cleanup rules (.tmp, backup, ~) Find / report on auto-cleanup content Approve auto-clean rulesAuto-clean (2% moved) Develop rules for content to be reviewed before cleaning or moving (large, old, marked “draft, old, trash”, music, video, audio) Find / report on reviewable content Review and identify for cleanupCleanup or move individual or groups of files Develop data integrity policies, develop and communicate cleanup maintenance rules and policies Cleanup / identify continually

8 What is eTrash Non-business value, non-records – Temporary files – Obsolete applications – Files with zero content – Un-openable/un-readable content Not so important content that is not FOIA, Litigation related, retention scheduled and is: – Duplicates – Large – Voluminous – Obsolete 8

9 Content identified for auto-clean System generated temporary files System generated backup files Zero Content files and folders Abandoned applications Obsolete install files System status reporting files 9

10 Content reviewed for cleanup Old files Large files Large duplicate archive Identified old files Compressed file duplicates Renditions convenience copies 10 Non accessed drafts older than 1 year Human identified backup files Human-identified as delete-able content Non-business images music or audio video or media Office Document Duplicates

11 Content Identified for Risk PII and eDiscovery – Litigation hold files – Credit card numbers – Social security numbers 11

12 A new way of viewing content 12

13 Outcomes Shares cleaned or under review Bulletin issued - NARA Bulletin , December 06, 2011; Guidance on Managing Content on Shared Drives [ ] Inputs to Presidential Memorandum -- Managing Government Records, November 28, 2011, [ office/2011/11/28/presidential-memorandum-managing-government-records ] office/2011/11/28/presidential-memorandum-managing-government-records Policies identified – cleaning shares, – managing content on shared drives Most importantly…..clear path forward 13

14 Data Management Policies Reliability and content – Preserving file attributes Context – Preserve file path, include recordkeeping metadata within user profiles Usability – Align storage with usage, preservation and access needs. Widely accessible storage location for high value content – Use robust search capability eliminate complex hierarchical folder structures. Authenticity - only authorized users can add, change and delete content for a specified data set. 14

15 Benefits Storage space – when cleaned all identified, can provide upwards of 47% in reclaimed storage. Money - To maintain 27 TB clean per year, save $187K per year (shares and ). Time – Staff time culling through volumes of content to find or migrate their information (FOIA – E-discovery) Access efficiencies – Right information, right place right time 15

16 Enterprise Value of Cleaning If users are to migrate their own content to a target system (e.g., new share or ECRM), they decide: 1.Should it migrate (30 seconds) 2.How should it be tagged (30 seconds) If 55% of 10TB should not be migrated: – There are 16.8M documents (at 360KB/DOC) – 140K hours of wasted decision making – $10.4M of staff time (at $75 per hour) 16

17 Next Opportunity: Align management with value Permanent Mid to long term retention Short term retention E-trash 17

18 Beyond cleanup – Auto-class Keyword/Boolean - group Concept Search – Ontology (file plan) based – Analyze word/phrase relationships (Bayesian) Clustering (Bayesian) – Auto-classifier – Near duplicates – Predictive coding Learning by example 18

19 People will still have a role Connect with business owners Develop / enforce file integrity & metadata policies Locate and categorize content – Develop rules Boolean / Bayesian / Train tools by example – Run & review crawls and refine rules Determine and assign value (metadata) – Search legal terms, financial terms, health terms – How often accessed, how often duplicated? Develop / enforce storage / management policies according to value Manage by exception 19

20 Vision of future Workers store content Crawler indexes content nightly Maintain current item-level, categorized index Tweek index crawler, learns I&C Tools 20

21 Retention Bell Curve Granular retention schedules Big bucket retention schedules Granular retention schedules Paper-based file plans Large categories by business function / creator role Indexed and Categorized at item level I&C Tools 21

22 Vision Evolution Get clean and lean – Mature cleanup program – Cleanup and network policies enforced – Comfortable with crawlers and rules Develop rules to index at various value levels – Transitory, 3 year, 7 year, 10 year, etc. – Permanent Group, cluster, train, metadata extraction, migrate, and tag with retention Go granular as needed Crawl nightly, manage by exception 22

23 Automated Policies Run continuously – Cleanup policies for files that are no longer needed – Security risk policies for files meeting PII criteria – Legal policies for files meeting legal hold criteria – IT drive management policies for databases, multimedia, images, applications, web content and other categories – Lifecycle management policies for inactive files draft files active files records reference material – Content integrity policies for abandoned files – Migration policies for files that require migration to target systems 23

24 I&C Tools Indexes & categorizes content according to rules Clusters content around trends (retention) Ingests content samples and learns. More crawling = more learning – Manage by exception = more learning Act upon content (move, lock) 24

25 Part 2 Tim Shinkle

26 Professional Services Approach Phase 0 – Source repository assessment Phase I – Program setup Phase II – Pilot Phase III – Enterprise Transformation

27 Phase 0 – Source Repository Assessment Perform preliminary analysis, e.g., Shares, SMEs, business processes Scan a percentage of enterprise shares that represent a good cross section of the data Produce an assessment report and plan moving forward – Percentage of candidate eTrash, migrate, relocate, review leave – PII analysis – Metadata extraction – Classification and categorization – Strategy and approach – Expected cost savings and benefit analysis

28 Phase I – Program setup Meet with sponsors Identify scope (shares, groups, SMEs) Develop communication plan Stand up program communications portal (e.g., corporate portal, wiki) Identify pilot group(s) (shares and SMEs) Meet with RM and Legal to identify candidate records, holds and PII Approve policies, cleanup and classification rules

29 Phase II – Pilot Interview pilot group Perform analysis and refine rules Crawl and report on pilot group shares Review results with SMEs and move approved files to cleanup location Refine global cleanup and classification rules and update published policies Develop benefit analysis report and enterprise strategy Publish updates to program portal (wiki)

30 Phase III – Enterprise Transformation Outline schedule, project plan and group priority Update communications on portal (e.g., wiki) Repeat for each group – Meet, refine share mapping to SMEs and perform high level analysis, size and scope – Crawl shares, generate reports and publish for review – Meet with SMEs, review results and start the clock – Move approved files to temporary storage location – Perform any additional migration and transformation based on classification, categorization and metadata extraction of results – Repeat until completed Publish final reports, develop and implement strategy for on going policy information governance automation

31 Technology Demonstration Example Data

32 Classification Best Practices Create categories that can be leveraged by machines (e.g., Put like content in like containers, e.g., expense reports) People can provide context that is not found in the content (e.g., the folder name is a project number where anything to do with the project is dumped into the folder, such as proposal, meeting notes, deliverables, reference material, directions, local hotels, expenses, etc...) Folders can only sometimes tell you what can be found in the folder, other times it cannot (e.g., \Invoices vs. \Misc)

33 Classification Best Practices File names are mixed and matched (e.g., \Proposals\Proposal XYZ.doc, \Meeting notes\Proposal XYZ.doc) Don’t confuse how individuals want to organize their content with the classification of the content The more the information is shared, the more it needs to be generally classified (big buckets) Combine algorithm extracted topics and identified classifications with subject matter expert search rules

34 Q&A Susan Sullivan, CRM Director - Corporate Records Management National Archives and Records Administration V: (301) Tim Shinkle Director, Gimmal Group (703)


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