Building a Data Strategy for a Digital World

Transcription

1 Building a Data Strategy for a Digital World Jason Hunter, CTO, APAC

2 Data Challenge: Pushing the Limits of What's Possible

3 The Art of the Possible Multiple Government Agencies Data Hub 100 s of Service Providers Benefits Citizen Identity 36 Jurisdictions IRS SLIDE: 3

4 The Art of the Possible Accessed by millions of users Supporting over 70,000 concurrent users Multiple Government Agencies Data Hub 100 s of Service Providers 36 Jurisdictions SLIDE: 4

5 Health Insurance for Millions of Americans Before MarkLogic Unable to handle complexity Impossible data model Development too slow Limited scalability Inflexible to change The Results Built for Today s Data Schema-agnostic data model that could handle various data sources and adapt to later changes with policies and regulations Agile Development 18-month timeframe from procurement to launch for what has been called the most complex government-it project of all-time Secure and Trusted Did not have to sacrifice any of the enterprise features required, and could rely on a system with government-grade security, ACID transactions, and HA/DR Successful Deployment Over 8 Million people signed up for health insurance in less than 5 months during the first year of open enrollment SLIDE: 5

6 Challenges for Insurance?

7 It s All About Data! Mergers & Acquisitions creating data challenges Personalization customer centric approach Agility Move from reactive to proactive Bring innovations to market quicker (6 months not 3 years) Insight-driven decision-making Ensure regulatory governance and compliance SLIDE: 7

8 THE REALITY Data Is In Silos Data is spread across disconnected databases M&A outpaces the speed of data integration Data needs to be delivered in real time SLIDE: 8

9 OLTP ETL ETL ARCHIVES ETL ETL WAREHOUSE REFERENCE DATA ETL DATA MARTS THE IT CHALLENGE Relational Databases With ETL Sacrifice Agility, Timeliness, and Cost All future data needs must be predictable Siloed database changes require ETL re-writes New SQL queries require database re-indexing Security risk increases with each additional silo SLIDE: 9

10 Building a Data Strategy: The Operational Data Hub

11 THE DESIRED SOLUTION A Database That Integrates Data Better, Faster, With Less Cost SLIDE: 11

12 Focus On The Data Social Media Product Holdings Fraud detection & investigation Customer Data Policy Systems Online Access Transaction records Public Data Sets SLIDE: 12

13 Focus On The Data Social Media Product Holdings Fraud detection & investigation Customer Data Policy Systems Online Access Transaction records Public Data Sets SLIDE: 13

14 Focus On The Data Social Media Product Holdings Fraud detection & investigation Customer Data Policy Systems Online Access Transaction records Public Data Sets SLIDE: 14

15 Fast & Agile Development Ready for Business 360 view of Customer, Operation, Risk Transaction Management systems Analytical analysis?? Content Management SLIDE: 15

16 OPERATIONAL APPLICATIONS BIDIRECTIONAL ANALYSIS OF ALL DATA MULTI- CHANNEL DISTRIBUTION DESIGN PATTERN Operational Data Hub Data-Centric: Integrates at the data level, not just functionally. Data is the longest living asset of a business! XML JSON Convergent: Operational & Analytical. Read and write. Always current. Contextual: Data harmonized with semantic metadata Cost-effective: Minimizes ETL, data copying, business silos, technical silos, and people-centric integration Secure: Provides a platform for rich data governance Complementary: Leverages existing assets and patterns SLIDE: 16

17 Aetna Human Resources Data Hub Integrated HR Data, Real-Time Delivery at Scale 140+ DATA FEEDS HR DATA HUB 50+ SYSTEMS WHY MARKLOGIC? Massive ETL too complex EMPLOYEE DATA PAYROLL DATA REAL-TIME APP REAL-TIME APP Flexibility for future change Cost and timeline constraints Enterprise requirements EVALUATION DATA REAL-TIME APP OTHER SYSTEMS BATCH ANALYTICS SLIDE: 17

18 Aetna Human Resources Data Hub Integrated HR Data, Real-Time Delivery at Scale Flexible Managed complex data ingestion, complex WHY output MARKLOGIC? 140+ DATA FEEDS HR DATA HUB 50+ SYSTEMS EMPLOYEE DATA PAYROLL DATA EVALUATION DATA OTHER SYSTEMS The Results Scalable Daily throughput of 50GB+ Agile Deployed in 1 year versus REAL-TIME a 5 APP year ERP replacement Future-proof Enabled maximum reuse and consistency REAL-TIME APP Success MarkLogic now the data layer for all HR data REAL-TIME APP Massive ETL too complex Flexibility for future change Cost and timeline constraints Enterprise requirements This could not have been done with DB2, or Oracle, or any kind of relational database I think eventually relational will die off. BATCH ANALYTICS - DIRECTOR OF ARCHITECTURE, AETNA SLIDE: 18

19 ALM Customer 360 Integrated Publishing Data to Drive Better Customer Engagement 60+ DATA SOURCES CONVERGE MDM TRANSACTIONAL BEHAVIORAL ALM PROPERTIES DESCRIPTIVE De-duplication Mapping and linking Source prioritization Point-in-time scoring Provenance DOWNSTREAM OUTPUTS PREDICTIVE ANALYTICS WEBSITE RECOMMENDATIONS WEBSITE PERSONALIZATION MARKETING CAMPAIGNS INSIGHTS AND REPORTS PERMISSIONS SYNDICATION WHY MARKLOGIC? Massive ETL too complex Development too slow on relational Structured and unstructured data Data harmonization Personalized content delivery SLIDE: 19

20 ALM Customer 360 Integrated Publishing Data to Drive Better Customer Engagement TRANSACTIONAL BEHAVIORAL ALM PROPERTIES DESCRIPTIVE The Results 60+ DATA SOURCES Timely CONVERGE Deployed MDM into production DOWNSTREAM in 4 OUTPUTS months Great ROR 600% improved response rate on 1 st project PREDICTIVE ANALYTICS Better Insights Personalized content delivery WEBSITE RECOMMENDATIONS MARKETING CAMPAIGNS WHY MARKLOGIC? Massive ETL too complex Development too slow on relational Success We re building products WEBSITE around our customers workflow PERSONALIZATION and making their lives easier, which translates into greater revenue opportunities. MarkLogic s potential is virtually limitless. De-duplication Mapping and linking Source prioritization Point-in-time scoring Provenance Structured and unstructured data Data harmonization INSIGHTS - GENE AND BISHOP, VP OF TECHNOLOGY, ALM REPORTS PERMISSIONS SYNDICATION Personalized content delivery SLIDE: 20

21 The MarkLogic Alternative An Operational and Transactional Enterprise NoSQL Database EASY TO GET DATA IN Flexible Data Model Data ingested as is (no ETL) Structured and unstructured data Data and metadata together Adapts to changing data and changing data structures EASY TO GET DATA OUT Ask Anything Universal Index Index once and query endlessly Real-time and lightning fast Query across JSON, XML, text, geospatial, and semantic triples in one database TRUSTED TO RUN YOUR BUSINESS Enterprise Ready Flexible cloud deployment Enterprise-grade data security Reliable data and transactions (100% ACID compliant) Out-of-the-box failover, replication, and elasticity SLIDE: 21

22 The World s Experts at Integrating Data From Silos MATURITY 1,000+ global customers 10+ years of customer success 8 years of public cloud success 250+ partners 525+ employees worldwide CREDIBILITY Erie Insurance EXPERTISE Consulting Services Data integration experts for government and commercial 24x7 Expert Support Provided by true engineers Online Community Collaborate with tens of thousands of people Free Training Web-based and instructor-led SLIDE: 22

23 Thank you

24 Appendix

25 If You Have a SOA Infrastructure SOA / EAI Function focused Emphasis on data movement SLA dependent on downstream systems Ephemeral information exchange Least-common-denominator data interaction Operational Data Hub Data and function focused Emphasis on data harmonization Can proxy for off-line systems as appropriate Durable information management Throws nothing away; enhances data provenance SLIDE: 25

26 If You Have a Data Lake Data Lake Batch-oriented Analysis only Save everything and process with brute force Simplified security model Limited or no context Multi-layered ecosystem encourages technical silos Operational Data Hub Real-time Two-way analysis & operations Save and index everything for sub-second processing Mature and fine-grained security model Advanced Semantics capability for rich context Multi-model capability eliminates technical silos