Data Classification. The Foundation for Intelligent Information Management. Infostructure Associates Leveraging Information for Organizational Success

Transcription

1 Data Classification The Foundation for Intelligent Information Management David Hill Principal Wayne Kernochan President Infostructure Associates Leveraging Information for Organizational Success

2 SWC Legal Notice "# $ % & ' () *' + ',

3 Agenda. / 0' -

4 Definition of Data Classification Data classification is the process of separating data into separate piles (i.e., categories) to which different policies apply. 1

5 What Good is Data Classification (The Storage-Centric View) 34 & +."' # + 5, 3 2

6 Data Classification Above the Storage Tier & : Enterprise Applications Cross-database, data-copy, business-process metadata Structured, semi-structured, unstructured metadata Databases Business-Process software Global metadata repository Business Intelligence Content Management Data-level metadata sub-repository } Business } Database Level Level Storage Area Networks Systems Management File, block metadata (age, striping) Hardware-level metadata sub-repository } Hardware Level Disk, Tape Servers

7 Why data classification the ILM View SNIA s Data Management Forum has a powerful vision for ILM. Although infrastructure and services can exist without ILM, the starting step for ILM is data classification. Business Framework Define Business Process Information Network Infrastructure IT Infrastructure Business Requirements Requirements Applications Data Management Services Compute Infrastructure Requirements Goals Management Policies, Instrumentation, Filters Information Management Services Storage Infrastructure ILM Framework ;

8 Why Data Classification the business view 3 = ' 9>,? <

9 Problems Right Here in Data Classification City & A A 3 6' ' 6 +( 38'

10 Data and Metadata Before Data Classification C4 &"D,# & %>D=' ' %' C. ('7 "=# (' 0 5 C C C C9, & C&() This is a start for building a compelling case for data classification. B

11 Some Benefits from Extending Data Classification 3=' 7 &' ' % " # % 7 "$ ' %(E%,66(F# BB

12 Two Problems to Solve 0' 0' = B

13 Data Classification Playbook Definitions A "69# =4 6 A = A A A 9' B-

14 What is data classification? 6 " #' + G A 6 6 +) +)' B1

15 A Few More Basics About Data Classification (>$ "(># +&,'' ) 6 66 B2

16 More Thoughts on Data Classification A ', ' + A + A = A ='' B

17 General Tool Strategies for Helping to Classify Data 5 5) + H) +%,) + B;

18 Application Classification 96 96=96 + (> + ' $"G># ' B<

19 Why not just application classification? 3 + ' + C' ', + 3' +(,' ' B@

20 Data Classification Through Metadata A,6 646'66 + ', + ( A A 6' " #6'=' + & + (

21 Adding in Content Analysis A 4" ='# 9, ,,I = ' A 7, = ' B

22 The Management Lens A 66 A = J A 9= 6

23 Data Classification and the Management Lens &() & & "6 6 # 3 ' ' -

24 The Data Lens " % 3 & % $% 8,: C 96 6 ' )& # " >(G6 ) %G 3 ' G %6 6 9 ' &6 0G 1

25 Distinguishing Between Unstructured and Semi-Structured Data. 5(>5"5 (>$# + C"' # 9' +) + C "'6 6 #

26 Don t reinvent the wheel - 1 ) Enterprise Applications Cross-database, data-copy, business-process metadata Structured, semi-structured, unstructured metadata Databases Business-Process software Global metadata repository Business Intelligence Content Management Data-level metadata sub-repository } Business } Database Level Level Storage Area Networks Systems Management File, block metadata (age, striping) Hardware-level metadata sub-repository } Hardware Level Disk, Tape Servers

27 Don t reinvent the wheel - 2 6,6 +%,K%&&"%&&# EII Front End EII Engine/Server EII Back End Applications BI Reporting Tools & Applications End User SQL, XQuery Query Optimizer Adapter SAP, Siebel Transaction Engine (combine sub-query, sub-read, sub-update) ETL Tool Relational Data Oracle, IBM DB2 Program Program Oracle Database APIs, Web service provider code Cache Database Metadata Repository (contains cross-data-source views, virtual tables) Local EII Server Files, Content Excel, Web graphics ;

28 How Accurate Will The Classification Be? 8 ' 6 '< B6 = '' ' 6 6 : 8) L 6 L C,: 8 D= 6 6, = 6 ' L 6D: 8B26 4 L 4: + 6H626 )3) <

29 Steps to Take 3 + A + A ' + A, = ) ' ' ' + A + A 9 + A 5(>5 ) ' "66#.9,

30 Separating Data into Pools %' A 6' " J'# + 6 = " 9#' %%' A, "# "%' A, %A -

31 Where Should Your Data Be Today? Production Data Active Changeable Pool Active Archive Pool Deep Archive Pool Endpoint Analysis: What characteristics should data have to be in each pool? Data Destruction Availability Online Online Offline Type of Data Changeable data Fixed data Fixed data Purpose OLTP Collaboration Business intelligence Reference Compliance Ad hoc recall, e.g. regulatory requirements -B

32 Applying Data Classification for Intelligent Information Management C' "# ' ' Enterprise Applications Business-Process software Business Intelligence Cross-database, data-copy, business-process metadata Databases Global metadata repository Content Management }Business Level Structured, semi-structured, unstructured metadata File, block metadata (age, striping) Storage Area Networks Data-level metadata sub-repository Systems Management Hardware-level metadata sub-repository }Database Level } Hardware Level Classified Data Disk, Tape Servers -

33 Conclusions 3 = ' + + %', & ' +%' 5 D=6 D='M ")3)# --

34 Thank You Questions please. David Hill (781) Wayne Kernochan (781) Infostructure Associates Leveraging Information for Organizational Success ventures.com -1