Defensible Disposition Strategies for Disposing of Structured Data - etrash

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1 Defensible Disposition Strategies for Disposing of Structured Data - etrash Presented by John Isaza, Esq., FAI Co-Founder & CEO, Information Governance Solutions, LLC Tom Reding, CRM Executive Consultant, Information Governance Solutions, LLC JI

2 Your Presenters John Isaza, Esq., FAI John is a leading author and speaker on Information Governance and Records Management issues. He is a distinguished ARMA fellow and recipient of the Britt literary award. In addition to being CEO of Information Governance Solutions, John chairs the Records & Information Governance practice at Rimon PC, a global law firm. Tom Reding, CRM Tom is a recognized authority and thought leader on Knowledge, Content, Document & Records Management. Prior to joining IGS, Tom was a Principal in EMC s Information Governance Practice, working with customers on regulatory compliance, litigation support and privacy. Prior to EMC Tom served a similar role with IBM. Thank you to IBM & EMC for their contributions to this presentation. JI

3 Why a presentation on this topic? Cost savings are substantial Ignored Little is understood about the issues IG Professional needs to know It is where many clients want to start addressing their IG challenges Long overdue JI

4 Agenda Part I: Part II: Part III: Part IV: Part V: Part VI: Define the Business Problem Describe Requirements Present Solutions Role of Records Retention Schedule Real World Examples of Successes Address best practices JI

5 Part I: Business Problem No Easy Button to time consuming, labor intensive categorization of information in structured data systems JI

6 Structured Data Challenges Large % of Dark Data and Big Data is Structured Data The record is comparatively easy to all other non-records Difficult to apply business rules to structured data Knowing what conditions must be in play to defensibly dispose of this data is not well understood The Right to be Forgotten is an important driver for Defensible Deletion of Structured Data, specifically web based data JI

7 TYPICAL STRUCTURED DATA ENVIRONMENT LEGACY APPLICATIONS READ ONLY ACTIVE APPLICATIONS READ & WRITE INFRASTRUCTURE & STORAGE FRAGILE HIGH COST BUT LIMITED VALUE ARCHIVE SILO 1 ECM BACKUP = ARCHIVE KEEP EVERYTHING (!?) ARCHIVE SILO 2 COLD DATA GROWTH IT COST DATA ACCESS? USERS COMPLIANCE MANAGEMENT LINE OF BUSINESS, CUSTOMERS, COMPLIANCE, AUDITOR JI/TR

8 Lifecycle Management Challenges Difficulty in applying Work-in-Process rules to structured data, as in SAP At what level should retention be applied to structured data At what level should hold orders be applied to structured data According to Gartner, by 2020, 50% of all current applications in the data center will be retired Difficult to apply RRS (Records Retention Schedule) retention periods to objects JI

9 A Problem Worth Solving Information Governance has come of age Huge-scale volumes of information required for new and unanticipated ways The costs of poor management are staggering: Regulatory enforcement Discovery requests Penalties TR

10 Part II: Requirements What processes and technologies do we need? JI

11 Policies, Business Processes & Technologies to enable Defensible Destruction Improved IG business process elements to include: Access, Control, Security, Privacy, Intellectual Property Protection, Retention, Disposition The RRS Open Archive Information System (OAIS) standard Compliant Archive Repository, IG enabling / executing policy engine JI/TR

12 Funding Value Proposition of Structured Data E-Trash Disposition What should be included in the business case when proposing program for funding: Conservative projection of reduced storage, application and server cost* Better, faster, more accurate and decision making becomes mainstream Better overall governance of data, including: control, security, protection, preservation, retention, disposition, discovery (hold order mgmt.) Measurable results in the routine disposition of large stores of structured data. * = Hard $ Savings JI

13 Part III: Solutions The ILM Approach & the Unified Archive Approach JI

14 THE INFORMATION LIFECYCLE MODEL JI PC LAW FIRM EVOLVED

15 Lifecycle Business Rules Hold Temp Retention 90 Days Location(s) Inbox (semi-structured) Metadata Name Work in Progress Retention 12 Months Location(s) SAP Metadata Name Content Type Final Record Retention Retention Schedule Location(s) Archive Metadata Name Content Type Retention Location Metadata Security Versions Disposition Disposition 19 JI

16 Other ILM Considerations Big Data = Big Management Data stores Disparate needs and retentions International data disposition requirements Historical Archives JI

17 Information Lifecycle for Structured Data Tiered Storage & Tiered Search Access Both high speed synchronous access and slow asynchronous access to information supported. Ability to tier the information storage across its lifecycle within the archive. Typical lifecycle profile requires that most recent information is likely to be the most regularly referenced & should be accessible using a high speed synchronous search. Older information that is accessed less frequently can be supported using slower asynchronous searches. This can be configured in an archive, with information automatically moved between storage tiers. TR

18 The Unified Archive Approach A unified approach to enterprise archiving for structured data & unstructured content Active Archiving Application Decommissioning Reduce IT complexity Optimize infrastructure Ensure regulatory compliance TR TR

19 Benefits of Structured Data Archiving Cost Take-out Optimize Re-envision DECOMMISSION REDUCE COSTS APPLICATION, INFRASTRUCTURE & STORAGE RIGHT SIZE DATA ACCESS HISTORICAL CONTEXT NEW PERSPECTIVES CONFIGURABLE GOVERNANCE TR

20 Part IV: Role of the RRS The first line of defense JI

21 What, When, Where and How to Capture Structured Data as official business records Records are objects in SAP, for instance What conclusions has your organization arrived at regarding What, When, Where, and How Keep it simple Point of authentication vs. a draft Not capturing DB changes Reports Management System - Beware of: BLOBS (Binary Large Objects) Application and Release of Hold Orders Unique disposition step-by-step processes Example: Web Transactions as business records Content, Structure, Context TR

22 Structured Data as Business Records Capturing the Input & Output is key Manage in-place vs. capture and preserve in a secure archival repository In-place: no duplication, use production system when performing discovery Move to a secure repository: allow for use same user interface, helps ensure desired high performance of the transactional system TR

23 Structured Data Records Managed in an Archived Information Unit (AIU) Data is managed as an OASIS Archive Information Unit (AIU s) Reports data now consistent with other organizational records Helps meet compliance objectives and supports litigation requirements Declare and classify records An entire load or individual documents within a load Classify to the corporate records file plan Time, event and event+time retention rules Apply legal holds and manage the records lifecycle Records Jonathan Smith Ronald Smith William Smith TR

24 Managing Legal Holds and Dispositions as part of an Archived Information Unit(AIU) Individual records can be retained while dispositioning the rest of the original data load Unload and extract individual records on legal hold Reload them into a new AIU All their records metadata and properties are maintained The rest of the original load completes disposition Disposed Unload & Reload with records metadata in tact Disposed Jonathan Smith Ronald Smith William Smith TR

25 Part V: Real Successes Two approaches with great results TR

26 Hard cost take-out North American Bank CHALLENGE Lower cost of data retention for legacy applications Ensure compliance with regulatory requirements for retention Enable access by LOB users to legacy data for audit, compliance, legal and regulatory needs SOLUTION Decommissioning of legacy applications while complying with retention requirements and maintaining access to vital information Time to Value Twelve Weeks Innovation $3.8M available annually for innovative IT projects based on eliminating only 1 main application Success 80+ more applications to be consolidated or decommissioned TR

27 Optimize environment EU Bank Active Archiving & Application Decommissioning CHALLENGE Infrastructure Optimization & Cost Reduction: Manage growing data volumes in banking applications to enable infrastructure & storage to be optimized and costs minimized Compliance: Long term retention of financial data, contracts & other documentation Information Availability: Make information more readily available to all authorized internal and external users; Focus on providing retail customers with access to statement information. SOLUTION Implemented Corporate Archiving Backbone to manage long term retention of digital content and data Ongoing archiving of invoices & other documents for long term retention Ongoing archiving of data from Core Banking systems archived daily for retention & data growth management 10 years customer statements available via retail banking online system Information Types Over 90 different archived simultaneously (approx 50% structured data & 50% content) Over 35 Billion records archived Structured Data Daily: 16 million archived; Unstructured Content Total : 670+ Million documents archived Monthly: 10 million archived; Access & Performance >600,000 retrievals/month Supporting tens of thousands of internal users & millions of customers TR

28 Part VI: Best Practices General guidelines JI

29 Measure the success of your Program once operational Measure results in the routine disposition of large stores of structured data Better, faster, more accurate so decision making becomes mainstream Storage costs drop measurably Better overall governance of data has become mainstream: control, security, protection, preservation, retention, disposition, discovery (hold order mgmt.) JI

30 Order of Defensible Disposal Impact Driven & Executed based on the Information Economics Completely delete obsolete structured data sources Completely retire applications to Structured Archive compressed files Archive large portions of historical data to Structured Archive compressed files Test Data Management / Sub-setting Steady state archiving TR

31 Structured Solution High Level ILG Capabilities Needed Initiation, Analysis & Design Assess: Find structured data sources and sensitive data assists with the archive roadmap Discovery: Find sensitive data and relationships Factory: Combine analysis, requirements and design, profile of each database to help prioritize data sources Build, Test & Implementation Structured Archive: Retire, Archive, Subset and Protect Policy Governance: Integrate policy with Structured Archive Privacy/Security: Protect Sensitive Data Factory: Automate Structured Archive Build and Documentation TR

32 Structured Data Challenges: Speed Bumps Organizational Barriers Requirements: Determine the RIGHT Business and Records staff to identify retention and archiving parameters Legal hold analysis Complexity and Intelligence Need to understand business rules Can t apply simple de-dupe rule or easily leave a stub Often COTS applications are customized & Custom not known at all Access to retired and archived data Significant access to data common Finding and building relationships takes time Need to decide how to chunk the data TR

33 ACT NOW BIG DATA COMPLIANCE WE WILL NOT GENERATE LESS DATA IN FUTURE... AND THERE WILL NOT BE FEWER REGULATIONS JI

34 John Isaza, Esq., FAI Co-Founder & CEO, Information Governance Solutions, LLC Tom Reding, CRM Executive Consultant, Information Governance Solutions, LLC JI