EDUCATION. Information Classification: The Cornerstone to Information Management Sheila Childs, EMC

Transcription

1 Information Classification: The Cornerstone to Information Management Sheila Childs, EMC

2 Abstract Information Classification: The Cornerstone to Information Management Anyone who is trying to get a handle on information growth, compliance-related risk mitigation and information management costs realizes that without an understanding of the information under management, these objectives are difficult to achieve. Fundamental to any ILM strategy is the practice of information classification Information classification requires that I.T. administrators work with Line-of-Business and knowledge workers to gain an understanding of the data to be managed. This sessions will explore the different types of classification methodologies and file system metadatabased classification and content/context-based classification will be discussed. Manual versus automated classification procedures will be discussed, along with the pros and cons that each approach has in its implementation. We will discuss the difference between indexing and classification, and discuss where each approach makes sense. The session will include information on various standards under development and focus on the work of the SNIA DMF ILM initiative. It will culminate with a view of the benefits to be derived through classification of information better risk mitigation, lowered management costs and a better understanding of corporate information. 2

3 SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies and individuals may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced without modification The SNIA must be acknowledged as source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. 3

4 The Issues: Management of Information Growth Information growth In 2003, 800 MB of new information was created for each man, woman and child on the earth. 92% of it stored on magnetic media Businesses users are expected to make up nearly half of the 500 million people that will be using Instant Messaging by % of all commerce conducted online ($1500 for every person on the planet) by This drives a tenfold increase in the number of transactions What s the problem? Can t reduce storage purchases (or accelerate storage purchases) Storage solutions not matched to data needs Poor understanding and/or weak definition of data security requirements Can t find what you need when you need it Can t manage what you don t know or understand Sources: IDC; Kahn/Blair 4

5 The Issues: Compliance- Related Risk Management Compliance Is a Huge Issue 2002 SEC fines five broker-dealers a total of $8.25M for failure to preserve communications (SEC Press Release 12/3/02) 2002: Federal agency admits that it s current system for collecting information is antiquated, outdated, inaccurate, and untimely after discovering that it had approved two 9/11 terrorists for student visas six months after 9/11(CNN.com, 3/12/02) Morgan Stanley must pay billionaire financier Ron Perelman more than $1.4B in damages (Forbes 5/18/2005) What s the problem? Urgency with which solutions are deployed Lack of understanding of what is important to be retained (classification) Accountability; business is at risk 5

6 The Goal: Information Management Information Management Disciplines AIIM focuses on Enterprise Content Management: content/document management, business process management, enterprise portals, knowledge management, image management, data warehousing and data mining ARMA focuses on Records Information Management : a specialized field of information management that is concerned with the systematic analysis and control of operating records associated with business activities SNIA focuses on the data center tools and technologies that enable Information Lifecycle Management 6

7 Information Lifecycle Management and Classification Information Lifecycle Management The policies, processes, practices and tools used to align the business value of information with the most appropriate and cost effective IT infrastructure from the time information is created through it s final disposition Information is aligned with business processes through management polices and service levels associated with applications, metadata, information and data Information Classification The process of assigning value to information associated with a business process, in order to produce requirements for the management of this information, within a defined scope 7

8 ILM Implementation Roadmap Instrument & manage services to ILM practices across sites Deploy ILM practices across the enterprise Automate with ILM Management tools Automate ILM-based policies & services Begin operating services to policies Set data & info polices across domains Tier storage and protection into standard Service Levels Pilot ILM-based Solution Stacks Standardize Information, Data, & Storage Services Begin collaborating on requirements Identify value, lifecycle, & classification of information for each business unit Identify information assets & infrastructure resources/ services 8

9 Why Classify? Why Now? Identifying the Value of Information Value: How much this information contributes to the business, Value of Information Plus risk assessment The loss of data or of access (business losses, downtime & productivity losses, credibility losses) The potential legal, business risk, or liability The cost to recreate the data Other measures which might include future value, ownership, value as a business record, competitive advantage, etc. Value of Information $z $y $x Mission Critical Business Vital Business Important Important Not Important Cost of Service Class of Service 9

10 Some Relevant Terms Data Data is what I.T. manages: files, volumes, bits and bytes Information Information is data with context Data Lifecycle supports the Information Lifecycle Record Recorded information, regardless of medium or characteristics, made or received by an organization that is evidence of its operations, and has value requiring its retention for a specific period of time (ARMA) Taxonomy A hierarchical structure used for categorizing a body of information or knowledge, allowing an understanding of how that body of knowledge can be broken down into parts, and how its various parts relate to each other. Taxonomies are used to organize information in systems, therefore helping users to find it. Related terms: ontology, categories, evidence structures Lexicon the vocabulary of a language, an individual speaker or group of speakers, or a subject Example: A dictionary of over 200,000 medical, pharmaceutical, biomedical & healthcare acronyms and abbreviations is a medical lexicon Related terms: thesaurus, vocabulary 10

11 Classification for Information Management Activity Kinds of Information Basic Classification Goal Records Management Business records Making sure business records are properly retained for legal, compliance and business purposes, and then properly disposed of when no longer needed Document / Content management Knowledge management Information security and privacy Disaster recovery Customer / client relationship management Storage Management Documents a wide range of digital information Operational information of all kinds All valuable information, with a focus on sensitive, confidential and proprietary information Information needed to continue business operations Information about an organizations interactions with customers/clients and prospects All stored digital business information Ensuring that there are controls in place for accessibility to business documents so that that they are easily accessible to knowledge workers and others Ensuring that the knowledge of some individuals and groups in an organization is harnessed for use by others in the organization Ensuring that valuable information is accessible only to those authorized to see it, and ensuring its trustworthiness Ensuring that the collection of and access to sensitive information is properly controlled Ensuring that vital information required to operate the business can be recovered in a timely fashion after a disaster Ensuring that the customers experience with a company is satisfactory and consistent; identifying customer patterns that can lead to more revenue Ensuring that storage resources such as disk and tape are used cost-effectively in a manner consistent with the value of the information stored Source: Information Nation, by Randy Kahn and Barclay Blair 11

12 Example: Classification for Security and Privacy Why classify for security and privacy? Ensure that valuable information is accessible only to those authorized to see it, and ensure its trustworthiness Ensure that the collection of and access to sensitive information is properly controlled Formal security classifications have existed in the U.S. for years Security practices: encryption, data retention, data sanitization But expensive and complex (ex: encrypting everything is rarely practical, so classification becomes critical) Check out SNIA Tutorial: Security Track 12

13 Classification is Difficult Stakeholders are numerous Huge amounts of Information do we need to classify it all? Determining what should be retained and what should be thrown away Scope Information is hard to find how much risk are you willing to bear? Lack of buy-in from business and senior executives But compliance is pushing execs to classify 13

14 Getting Started - Information Stakeholders Security Officer Line of Biz Application Sponsor Business Process Analyst I.T. Architect Records Manager DBA Information Management Architect I.T. Admin Legal Counsel Information Management Executive Committee 14

15 The Classification Process: Types of Classification Application-based Metadata (and extended metadata) Content 15

16 Application Classification Focused on Business Applications Drivers for Application Classification Disaster recovery and business continuity Server consolidation Application performance Application Classification is fairly simple Establishes a ranking of applications All information associated with the application is treated the same Works best when applications are segmented by server Application Classification is often good enough 16

17 Metadata-based Classification METADATA classification is largely based on file attributes and access patterns What is file named? What is the file type? Who owns the data? Where is it located? When was it created? 17

18 Metadata-based Classification Requirement Function How Classification Helps Disaster Recovery, Accessibility Information availability Lowered costs (implementation of tiers of storage) ediscovery Data placement and protection Data placement and protection Data migration Search and retrieval Identification of mission critical data required for resumption of business Frequency of access Understanding the lifecycle of applications AND user behavior (ownership, creation, access) Identifies data access patterns (Including frequency of access) Provides ownership information Information Security and Privacy Access control Address privacy concerns Identifies which departments and users have access to data 18

19 Metadata-based Classification File-level metadata offers limited input, therefore limited recognition Still useful but class of solutions limited Generally useful in optimizing HSM or archiving strategies Tends not to meet complex ILM needs (security, retention, etc.) Combined with valid ownership data (not System ), can yield incremental ILM value Ex: Legal dept {Joe, Mary, Betty, Tom, and Matt } Rule: all legal files stored in host_legal, retain = 5 yrs Pros & Cons Fast, lightweight, not invasive Doesn t address changing business value over time 19

20 Beyond Metadata Extended Metadata The Basics Name Size Date Owner Creation Date Modification Date Access Control Security Clearance Access Control List Browse Privileges Read Privileges Write Privileges Sharing Policy etc. Ownership Creator Last Updated By Department Division Application Project ID etc. Compliance Retention Policy Expunge Date Industry Regulation Flag Corp Governance Flag Attorney-Client Priv Flag etc. Identification File Format Version Related Transaction Related Content Objects Parent Object Child Objects Bar Code Tracking ID Radio Frequency ID etc. Process Control Approval Status Lifecycle Phase Workflow Routing Send To Rules Next Approver etc. 20

21 Content Classification Classification based on CONTENT makes use of indexes, lexicons and taxonomies What keywords? How is this data related to other data? How should data be retained/disposed of for compliance or otherwise used by the business? 21

22 Content Classification Requirement Function How Classification Helps Records retention Data placement Ensuring the proper retention and disposal of business records ediscovery Information Security and Privacy Complying with electronic document discovery and preservation Access control Including AND other documents Ensure distinction of relevant records from nonrelevant records Protect the corporation Reduce time required for discovery Address privacy concerns Identifies content to secure Document, content and knowledge management Knowledge use Share information to drive business goals Storage Management Data placement and protection Information protection, access, etc. Of secondary importance (?) 22

23 When Does Content Classification Make Sense? Automated Classification speeds time-to-information effectiveness Automated Content Classification make sense When multiple classification options results in confusion When there is an overwhelming volume of items to classify When some documents require time-consuming review by subject matter experts When there are a large number of non-business documents When you don t want to have idiosyncratic results The highest quality and accuracy occurs when records management is as non-intrusive as possible to the desktop end users and does not interfere with the normal work routines of professional staff in the enterprise ** ** Timothy J. Sprehe and Charles R. McClure, Lifting the Burden. Information Management Journal, Vol.39 Issue 4 (Jul/Aug 2005),

24 Content Classification Algorithms All content-based classification is based on natural language Content classification algorithms Keywords Term frequency Pattern matching Latent semantic analysis (synonymy and polysemy) Neural Networks Bayesian Rules-based 24

25 Example of Content Classification (Rules-based) Objective: Find and classify all documents at a mortgage company into category New Homes Built in Fresno Classification Rules Document has Fresno in title (metadata) Document refers to homeowner/builder Perry or Trendmaker (keyword) Document includes Fresno in the text (keyword) Document uses abbreviations or regular expressions (entity extraction) 4 bdrm, 5/2.5/3 Comprehensive rule: if Fresno is true, Perry Homes is true, the document contains a numeric string such that 2003<x<2007 and/or uses the above regular expressions, then classify as a new home being built by Perry Homes in Fresno Secondary objective: Relate and groups plot plan, land survey, deed based this classification of documents 25

26 Building Taxonomies Kingdom Taxonomies A Taxonomy is a classification scheme that makes it easy for user to find information based on familiar hierarchies Industry specific lexicons and taxonomies are available Taxonomies should maintain policies and rules as industry and business environment changes Taxonomies should leverage exiting thesauri and glossaries Phylem Class Order Family Genus Species Kingdom Kingdom 26

27 Classification: What is Good Enough? Challenges of classification various types Some human intervention always required to review results of classification Automated tools improve efficiency Documents with little text how are these classified? Power point slides, , etc. Varying document types Metadata classification might be better in this case Lack of consistency in naming, structure, format Metadata classification may be best Factors affecting accuracy Document consistency / naming consistency The strength of the taxonomy (content) Applicability of classification algorithms to specific content What is a reasonable cost per document? What is the cost of a document that is incorrectly classified? Does cost outweigh the value to the organization? 27

28 Additional Definitions Indexing = the act of preparing documents to be searched later; specifically, where in the document does a word/ phrase/ string occur Search = the act of looking for something in documents Clustering = the act of grouping related documents and using these groups to generate dynamic categories; statistical/ semantic analysis to generate vector; documents are grouped along similar vectors Classification = the act of organizing/sorting/storing documents into pre-defined categories; includes concept recognition, pattern matching, entity extraction, thesaurus, misspelling, related terms, broad/ narrow terms, etc. 28

29 Indexing versus Content Classification Index When keyword searches only are sufficient When looking to find information quickly within a particular file or document Aspects Search may return too many matches Can be security hole if indexed by system Proprietary formatting issues Not all formats can be indexed Provides objective analysis of textual information found Nothing about misspellings, query expansion, thesauri, etc. 29

30 Indexing versus Classification Classify based on content When you need to group related documents When you need to discover information that is or might be relevant When you need to associate documents with one category or another based on the meaning of a word (or phrase) Aspects Content/documents must be cracked and run through classifier/taxonomy rules engine 30

31 SNIA s Role Business Process Information Lifecycle Information Classification Industry-specific and company-specific considerations may produce unique information and/or requirements. The mapping of business requirements to data Service Level Objectives and policies for applying those SLOs to data during the information s lifecycle. Best practices defined by SNIA Data Management Forum Data Lifecycle Data Classification Common data center-wide definitions of expected behavior. The assignment of service levels to data based on its current state in its lifecycle. Standards defined by SNIA 31

32 To Achieve Alignment, I.T. Classifies Its Resources Into A Service Catalog Mission Critical Business Critical Business Important Development Requirement SLA 1 SLA 2 SLA 3 SLA 4 Availability 99.99% 99.9% 99% 97% Threshold based Automated provisioning Up to 20% of current file System allocation within 1 business Day Up to 20% of current file system allocation within 2 business days Up to 10% or current file system allocation within 4 business days Up to 20%of file system allocation within 1 business week scratch based allocation RTO 15 minutes 1 hour 8 hours 24 hours RPO 1 hour 12 hours 48 hours 96 hours Restore Requests 100 requests/ week 100 requests / week 50 requests / week 50 requests / week Backup success rates 97% 95% 90% 90% Archive Policy No access in 90 days No access in 30 days No access in 90 days No access in 180 days Archive access time Seconds Seconds Up to 4 Hours hours Forecasting Monthly Quarterly Yearly Yearly Incident classification and notification Sev. 1 < 15 minutes Sev. 2 < 30 minutes Sev. 3 < 1 day Sev. 4 < 1 day Sev. 1 < 25 minutes Sev. 2 < 40 minutes Sev. 3 < 1 day Sev. 4 < 1 day Sev. 1 < 25 minutes Sev. 2 < 40 minutes Sev. 3 < 1 day Sev. 4 < 1 day Sev. 1 < 25 minutes Sev. 2 < 40 minutes Sev. 3 < 1 day Sev.4 < 1 day Asset reporting to support chargeback Weekly Bi-weekly Monthly Monthly Compliance HIPPA SEC 17a4 Sarbanes-Oxley None Compliance categories Cost $$$$ $$$ $$ $ Check out SNIA Tutorial: ILM ILM and and Tiers Of Of Storage 32

33 What Are Your Next Steps? 1. Set project objectives it s not too soon to implement information classification Processes can be put in place; some tools are available, technologies have emerged. Use of consulting services 2. Empower a cross-discipline, responsible Information Management team Storage Architect / I.T. Line-of-Business representatives Records Manager Legal Counsel Security Officer 3. Identify Application/Information to be classified and managed Scope Application-based, METADATA-based, Content-based Indexing 4. Create standard configurations with well-defined service classifications 5. Classify Infrastructure resources 6. Correlate Information Classes with SLOs (may not be 1:1) 7. Align information with Infrastructure based on classifications 33

34 Summary Classification - Immediate Benefits Better understanding of your information Better deployment and alignment of your I.T. resources (storage/server consolidation, smart purchases, etc.) Better compliance readiness and ediscovery Classification - Longer term benefits Service Level Management improves I.T. service delivery Information management automation Cost reduction 34

35 Other Related Tutorials and Data Management Activities at SNW Data Management Tutorials Using ILM to Leverage Business Continuity and Disaster Recovery at the Acme Anvil Data Protection for Federated Environments, Application Consistency and Coherency Disk-based Restoration Technologies Disk and Tape Backup Mechanisms ILM and Tiers of Storage Archives, Backups and Disaster Recovery: Lines of Distinction Next Generation Technologies for Tiering and Compliance Maintaining Logical Readability in Long-term Archives Identifying and Eliminating Backup System Bottlenecks Data Management Solutions Center Check out SNIA Tutorial: Data Mgmt Track 35

36 Q&A / Feedback Please send any questions or comments on this presentation to SNIA: track-datamgmt@snia.org Many thanks to the following individuals for their contributions to this tutorial. SNIA Education Committee Edgar St. Pierre Bob Rogers Eric Hibbard Matthew Coblentz Bill Pierce LeRoy Budnick 36