1. 11121 ≡ Kon Leong, CEO www.ZLTI.com Turning The RIM Tide: How To Channel The Data Tsunami Into A Data Lake 2015 May 18 th, 2:30 PM

2. 11121 Turning the RIM Tide: How To Channel The Data Tsunami Into A “Data Lake” ≡ ● The 3 Steps To The Data Lake 1. Gather All The Data 2. Enable All The Functions 3. All Together Now – The Data Lake ● Rethinking RIM … Again ● Back To The Future From IG To Information Management ● Case Studies

3. 11121 Unified Collection of Unstructured Big Data All Data Types E-mail Exchange, Lotus Share- Point, Quickr File System XML Black- berry Bloom- berg IM PST/NSF MIME MSG OCS ECM Fax Social Media ≡ All Collection Modes Proactive Archiving (by policy) Index In-Place (without storing a copy) Reactive Archiving (on demand) UNIFIED Collection Data-in-the-Cloud Data-On-Premise All Data Locations Office365, Gmail, Salesforce, etc.

4. 11121 Governance Functions Reduce Storage Overload User Need Corporate / Gov’t Compliance - 100% Capture, Index, Store, Search - Tamper-Proofing - Monitoring & Surveillance Litigation Support, eDiscovery* - Search, Hold, Attorney-Client - Save Discovery Costs - Case Management Stay In Control, Minimize R.O.I. Support Litigation, Slash Costs Records Retention Management* - Enable Electronic Records Retention - Enforce Doc Level Granular Policy Manage Electronic Corporate Records Solution Corporate eMemory Mgmt* - Enable Enterprise Data Mining - “Classified” Secure Access, Audits “Big Data” Analytics for Competitive Advantage “Silos” = duplicates Storage Management* - Dedupe/Offload Large Email/Files - Avoid Storage Quota / Admin O’H - End-User Access, Productivity 10X ≡ 2000 2002 2005 2008 2012 ; inconsistent search; disjointed retention circa ; differing views loss of data control Solution: Unified Archive: One Copy, One Policy Control Point, One Search, One View

5. 11121 Getting It All Together ≡ ● UNIFIED Applications e-Discovery Compliance Records Storage Analytics One central console Single data copy No data moved between apps ● UNIFIED Architecture One Data Copy One Policy Control Point One Search One Data Schema Much Lower Admin O’H Much Lower Storage Costs Much Faster Performance DATA CONTROL ● UNIFIED Data Types Domino  Exchange, Files, ECM, ERP, Twitter, IM, Soc.Media, Salesforce Ingest all data types All stored together, no silos Notes/Exchange – no migration Benefits

6. 11121 The Architecture At 30,000 Feet One Data Copy, One Coordinated Policy, One Consistent Search External Data Hadoop, eDiscovery & Other Ecosystems Analytics* Storage Records* Compliance E-Discovery* Cloud Options Hadoop-compatible Infrastructure Data Management Layer aka “Data Lake” Unified Archive Repository must scale to BILLIONS of items ONE Copy/Policy/Search/Data Schema/System Capture: Single Instance │ Copy │In-Place │ Crawl │ Stub │ Classify │ OCR │ Encrypt │ Compress │ Restore farm Event Logs Sys/Net Logs Structured Data ERP, BI, STRUCTURED ≡ Data Creation Layer RIM e-D Storage Analytics IM Shared Files Share- Point PST NSF ECMSocial Media Email Exchange, Domino UNSTRUCTURED Salesforce, Gmail “lake”

7. 11121 As one of the top broker-dealers in the U.S., this firm manages over 30,000 broker-dealers and employees. The company needed to manage files and email for SEC compliance, e-discovery, records management and storage optimization. Previous solutions were unstable and did not meet minimum requirements. Challenge  Satisfied SEC compliance requirements on document retention, while cutting costs  Provided highly available access  Reduced server counts by 75%, while significantly increasing throughput  Centralized data flow from multiple silos  Solved multiple data coherency issues caused by silos under one unified system  Expected boost in marketing and management performance through analytics Value Deployed Unified Data Management Platform to provide compliance, e-discovery and records management for files, email and many other file types. Currently, evaluating early use cases for Analytics. Solution ≡ Files, Email, IM, Bloomberg, Social Media Compliance, Records, e-Discovery A Top Wealth Management Firm CASE STUDY

8. 11121 eTrash Non-Records Records eTreasure? or “Big Data Analytics” “Data Lake” “Unified Repository” Retention? Access Privileges? Classification? Audit? 0% 100% Need for R I M Scope of New Records Management ≡ The Future Of RIM

9. 11121 Analytics The Power of Unstructured BIG DATA Legal Compliance IT Records ≡ Who Will “Own” It? Acquisition:Committee Ownership:GC, CRO, CCO, IT Operation:IT, RIM, GC, CCO Analytics:CDO, CDS, 3 rd Party

10. 11121 Turning the RIM Tide: How To Channel The Data Tsunami Into A “Data Lake” ≡ ● Avoid Data Silos ● Take A Unified Approach 1. Add Info Governance 2. Add Analytics ● Expand Scope Of RIM ● Prepare For Life Beyond IG SUMMARY

11. 11121 ≡ Kon Leong, CEO www.ZLTI.com Turning The RIM Tide: How To Channel The Data Tsunami Into A Data Lake 2015

12. 11121 ≡ to cost-justify the archive. Analytics for Strategic Advantage with Corporate eMemory® COMPLIANCE E-DISCOVERY ESTORAGE RECORDS “Unified Archive” Unstructured / Structured Big Data There are already 4 ROIs Analytics Use Cases Reduce  Costs and Risks (Defensive) Make  Money, Maximize Value (Offensive)* IP – Prior ArtKM Governance BoD HR*SCM Investigations*R&DSentiment Trending TopicsRetiree* Expert Network Rev Rec Effectiveness*Sales Analysis*

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14. Employee Effectiveness Email Phone Calls Documents Web Logs Calendars Influence Expertise Leadership Efficiency Effectiveness Powerful TechnologyCompetitive Advantage Data SourcesInferences ≡ Dial For Priv Dial For Pria cy Dial For Prva cy Dial For Pria cy Privacy vs. Control

15. 11121 People Analytics – Early Sample Use Cases ≡ HR/Sales ● Who knows whom? ● Who is likely to quit? ● Analyze transactions, networks, roles* ● When were our last visits to our top twenty clients? ● Competitive intelligence on demand ● Avg. 1,100 connections per salesperson. Map them. ● Reduce the probation period 12 months to 1 month HR/Expert Network ● Who knows what? Domain expert database ● Who fixed what when? Replay events, for crisis mgmt., product recalls, product liability ● Retain knowledge of retired/moved/former employees ● Identify experts, offer contracts HR/Human Networking ● Who knows whom (internal ↔ external) ● Strength rankings of client/employee relationships HR/Productivity/Sentiments ● Who are the most productive employees? ● Workforce sentiment ● Top Trending Topics HR/Leadership ● Who are the real “go-to” people, the most respected? ● Who should be promoted? Compliance/SarbOx ● Revenue recognition detection. Identify side-letters ● Detect bribery, collusion, FCPA violations Security/Threat Prevention ● 80% threats come from internal vs. external ● Prevention – Detect anomalies vs. benchmark ● Information Vectors – Detect Ed Snowdens in progress Security/Investigations ● Ad hoc investigations, organization-wide, in seconds ● Replay events, instantaneously Transparency/Org. Politics ● Instantaneous transparency, identify political friction Post-Merger Integration ● Track progress, view bottlenecks, expose politics R&D Resource Pooling ● Coordinate global R&D, leverage past research, eliminate duplication, shorten timeframe ● R&D patent / prior art research Legal ● De-dupe legal advice (reduce outside counsel fees) ● Reuse legal language, check variances Split by ROI, Strategic, etc.

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20. Gartner Says Beware of the Data Lake Fallacy Stamford, Conn., July 28, 2014 Information Leaders Must Understand the Gaps in Data Lake Concept and Take Necessary Precautions The growing hype surrounding data lakes is causing substantial confusion in the information management space, according to Gartner, Inc. Several vendors are marketing data lakes as an essential component to capitalize on Big Data opportunities, but there is little alignment between vendors about what comprises a data lake, or how to get value from it. "In broad terms, data lakes are marketed as enterprise-wide data management platforms for analyzing disparate sources of data in its native format," said Nick Heudecker, research director at Gartner. "The idea is simple: instead of placing data in a purpose-built data store, you move it into a data lake in its original format. This eliminates the upfront costs of data ingestion, like transformation. Once data is placed into the lake, it's available for analysis by everyone in the organization." However, while the marketing hype suggests audiences throughout an enterprise will leverage data lakes, this positioning assumes that all those audiences are highly skilled at data manipulation and analysis, as data lakes lack semantic consistency and governed metadata … ≡

21. 11121 You have to manage your Data Lake – the fallacy of technology being magic Steve Jones, Capgemini August 5, 2014 (excerpts) Gartner published a report calling Data Lakes a fallacy in which they point out many of the issues with an unmanaged Hadoop environment. It’s a great headline but actually the paper itself raises exactly the points that we made in Capgemini back in 2011 about what companies should be doing in this space. Back then we published a paper on Mastering Big Data which talked about how data governance was a core requirement to get value out of Big Data. Gartner raised a very valid point, basically that Hadoop isn’t magic. Just dumping data into a single repository doesn’t mean that it’s now magically easy to use. But dismissing data lakes altogether is “throwing the baby out with the bathwater.” It’s great that Gartner is highlighting the need for governance and the need for considering both structured and unstructured information in a unified manner. At Capgemini, we’ve been championing governance in a Big Data world and it’s great to have Gartner agreeing with us. ≡

22. 11121 ≡ ● Lot of labor / time to collect ● Data may be old, incomplete ● Adds data silos  more risk Working on Dead Grass Living Analytics – Today vs. Tomorrow Use Cases: -Reduce Costs -Reduce Risks -Increase Security -Manage Resources -Make money ● Tap into data, instantaneously ● Data is up to date, complete ● Data is under governance, no silos Beach enables benchmarking and analytics Reactive E-Discovery  Proactive E-Discovery Reactive Analytics  Proactive Analytics