BIG DATA GOVERNANCE: BALANCING BIG DATA VELOCITY & INFORMATION GOVERNANCE

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1 BIG DATA GOVERNANCE: BALANCING BIG DATA VELOCITY & INFORMATION GOVERNANCE Size Matters. The success of big data projects requires access to huge sets of high quality information. Compliant data represents the largest set of high quality business information within an enterprise. Attend this session to learn how to drive compliance and governance into your data lake, to deliver the information that your strategic initiatives require. 1

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3 BIG DATA GOVERNANCE PETER SMERALD SENIOR DIRECTOR PRODUCT MARKETING & ENABLEMENT 3

4 ROADMAP INFORMATION DISCLAIMER EMC makes no representation and undertakes no obligations with regard to product planning information, anticipated product characteristics, performance specifications, or anticipated release dates (collectively, Roadmap Information ). Roadmap Information is provided by EMC as an accommodation to the recipient solely for purposes of discussion and without intending to be bound thereby. Roadmap information is EMC Restricted Confidential and is provided under the terms, conditions and restrictions defined in the EMC Non- Disclosure Agreement in place with your organization. 4

5 THE GREAT DISMAL SWAMP 5

6 BACKGROUND ON THIS SESSION Data lakes have begun to resemble a new generation of repositories Documentum is a world leader in repositories. Extensive experience with large scale compliant repositories Extensive experience with combined storage and application level systems (Centera and Documentum) Compliant storage is not an archive 6

7 SESSION OBJECTIVES HYDRO SERE SUCCESSION Build the case that compliance is the key component to accessing enterprise data Propose a pragmatic information architecture to establish data integrity controls Begin the dialog 7

8 SUCCESS = Ƒ(QUANTITY) ACCESS = Ƒ(COMPLIANCE) Size Matters: The success of big data projects requires access to huge sets of high quality information. 8

9 Success = ƒ(quantity) Organizationally, the most comprehensive, most valuable information resides in applications 9

10 GROUPING PACKAGED APP VALUE TRANSACTION APPLICATIONS Rich, highly validated transaction data. PRINT STREAMS CONTENT AND IMAGES INTERACTION APPLICATIONS COLLABORATIVE APPLICATIONS CMOD Valuable customer communications and financial reporting history. Images- comprehensive archive of legal agreements. Content- vast quantities of work products. Enriched and semi-structured contentparticularly important source of communications history. 10

11 PROVIDING DATA FOR THE LAKE Format Considerations Applications Images Print streams Unstructured documents Typical format Structured data- highly normalized table structures Multi-page tiff, with little metadata- and minimal text Large files with proprietary formats (ex. AFP, PCL, Postscript, multiple PDFs) Too many to count Why problematic Very difficult to construct business object Little to no textual information Massive sizes, not easily parsed No structure, little to no way to understand what is documented 11

12 PROVIDING DATA FOR THE LAKE Acquisition Method Duplicate ETL and migration tools into the data lake Pros Represents very distilled and highly valuable information Cons Non-compliant Poor quality Integrate APIs to applications Real-time, elegant Expensive Rigid Aggregate/ archive Archive data Generally pays for itself with infrastructure savings Requires a new mindset 12

13 A blended approach is needed ETL loads for non-compliant information APIs for hugely important systems that require real-time access because the velocity is so high An HDFS capable compliant archive for everything else 13

14 Access = ƒ(compliance) Without proper controls, the compliance, risk, and/or legal teams will block efforts to move data into the lake. 14

15 DATA INTEGRITY- THE GREAT DIVIDE Data Scientists Layers of defensibility: 1. Being able to do the right thing, 2. Doing the right thing, and 3. Proving the right things are being done. Records Managers 15

16 THE TRUTH, THE WHOLE TRUTH, AND NOTHING BUT THE TRUTH Data integrity Retention Legal holds Chain of custody Security Privacy Auditability 16

17 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability REQUIREMENTS: Retention : Based on record type- not format Date based and event based Occasionally one record is controlled by multiple retention policies Legal holds: Crosses content types Jurisdictions dictate disposition Occasionally one record is subject to multiple holds 17

18 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability STRATEGY: Storage based controls: Best place for enforcement of policies Allows most efficient control of content Controls administered by storage team Software based controls: Best place for management of policies Provides proof of proper management Controls administered by records team 18

19 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability REQUIREMENTS: From the moment the item is collected, every transfer must be documented and it must be provable that it has not be changed. If there are discrepancies, then the chain of custody is broken and The information has limited (if any) value Trust in the results of the analysis will not exist. 19

20 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability STRATEGY: Treat chain of custody as a process, not a technology. Enforce at the ingestion point. Mark object metadata with identifier Store chain of custody information as records 20

21 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability REQUIREMENTS: Ensure that content is not accessed by unauthorized parties A hierarchy of information exists Cross border controls Cross content controls exist 21

22 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability STRATEGY: The scale of a data lake changes everything. Abandon hope of managing privacy separately from security. Manage by sets, not by object. Create homogenous pools of anonymous information Mask Metadata Build sets of homogonous information Abstract 22

23 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability REQUIREMENTS: Audit controls at two levels: Management and policy functionsprotect against improper changes to the system Access and use of the information 23

24 Data integrity Retention Legal holds Chain of custody Security Privacy Auditability STRATEGY: Management controls enforced at application layer Access enforced at storage layer 24

25 Bringing it all together 25

26 EMC Business Data Lake SUPPORTING CUSTOMER CHOICE Data & Analytics Catalog Pivotal Cloud Foundry Data Lake Platform Manager BIG DATA SUITE PIVOTAL HD GEMFIRE Supported Third Party Platforms Choice of Hadoop Pivotal Distribution Big Data Suite VMware vcloud Suite GREENPLUM DB HAWQ Data Governor EMC II Storage 26

27 EMC Business Data Lake SUPPORTING CUSTOMER CHOICE Data Lake Platform Manager Compliant data store (cold data EMC lake) II Storage Data Governor 27

28 EMC s next generation platform for compliant application data preservation 28

29 Compliant data store (cold data lake) Structured data Unstructured data Applications File records Data records Compound records Enterprise grade integrity controls: Retention controls Legal holds Audit controls Chain of custody Data lake ready information architecture: Metadata together with content Augmented metadata Business object granularitystructure 29

30 An example Predictive health studies Oncology Patient Record System Compliant data store EMC II Storage (cold data lake) Laboratory Information System Hospital A Patient Record System Hospital B Patient Record System X-Rays Treatment records Progress notes Immunization records Prescriptions Telemetry Attributed with unified patient ID Segmented to discrete record Structured for reuse Patient Centric Application 30

31 HEALTHY GROWTH OF THE DATA LAKE Data Lake Success = ƒ(quantity of rich data) Access to that data= ƒ(compliant data lake) Size Matters The success of big data projects requires access to huge sets of high quality information. Compliant data represents the largest set of high quality business information within an enterprise. 31

32 LEARN MORE ABOUT INFOARCHIVE DATE TIME TITLE LOCATION Everyday Self-Paced Hands On Lab: IT Transformation By Application Decommissioning InfoArchive EMC vlabs in the Village Wednesday 1:30 PM 2:30 PM Hands On Lab EMC InfoArchive: An Applied Technology Review Galileo 906 3:00 PM 4:00 PM Big Data Governance: Balancing Big Data Velocity & Information Governance Venetian Ballroom A 3:00 PM 4:00 PM Real Stories EMC InfoArchive - Set Your Data Free! Galileo 1004 Thursday 9:00 AM 1:00 PM Hackathon: From the Ground Up - Developing an EMC InfoArchive Archiving Solution Galileo 1006 InfoArchive Product Community: //community.emc.com/community/products/infoarchive EMC Store InfoArchive: //store.emc.com/us/product-family/emc-infoarchive-products/emc- InfoArchive/p/EMC-InfoArchive 32

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