1. Using Big Data for Customer Analytics at Transamerica David Beaudoin Vishal Bamba John LoGiudice Enterprise Computing Community Conference Marist College June 12-14, 2016

2. About Transamerica Transamerica is the US-based brand of Aegon, a Dutch financial services firm. Aegon is one of the world's leading providers of life insurance, pensions and asset management and is helping approximately 30 million customers globally to achieve a lifetime of financial security. 2

3. About Transamerica Operating under the Transamerica brand, the Americas is Aegon’s largest market, with two-thirds of the company’s underlying earnings generated here. Transamerica products and services help more than 27 million customers protect against financial risk, build financial security and create successful retirements. 3

4. About Transamerica Transamerica is among the top ten largest providers of variable annuities, individual universal life and individual term life in the US. In 2015, over $6B in benefits were paid. With over $100B in assets under administration, we serve more than 4 million retirement plan participants across the entire spectrum of defined benefit and defined contribution plans. 4

5. As you may have guessed… That is a WHOLE LOT of data to manage! 5

6. Data Architecture Rich data environment across organizational business units, comprised of many source systems across various platforms… A consistent enterprise view of data across business units is required. 6

7. Data Architecture 7 As in many industries, we are focused on leveraging technology to build data-driven customer relationships. How can the current data architecture support this strategic direction?

8. Data Architecture 8 Enterprise Data Management Crossroads! Another traditional warehouse project? OR Enterprise data hub/lake/ocean with new “Big Data” technologies?

9. A New Strategy for Customer Data… EMAP: Enterprise Marketing Analytics Platform Integration of both internal and external data sources to provide a 360-degree view of customer relationships for marketing, planning and operational analytics. Solution must provide strong data governance and security, and facilitate the use of clean, trustable, high quality data by business stakeholders and end users. 9

10. EMAP – Customer 360 What is your current & future value to the business?

11. Initial Use Cases Customer 360 Investment Advisor and Producer Profile Journey Map Customer Lifetime Value Marketing Attribution Asset Retention Foundational Applied

12. Enterprise Data Hub / Data Lake 12

13. Customer Data Lake – Iterative Approach

14. Big Data Management Data Integration HDFS Big Data management Data Quality Hive Hive / Impala MR / Spark Cleansed Files IndividualHousehold Informatica Big Data Management Cloudera Big Data Platform Big Data Analytics Datameer Extract Load & Transform Data Quality –Cleaning, Identity Resolution Admin Extracts Partner Files IVR Enrichmen t Inputs CRM Solicitation History Weblogs BI Tableau H2O – ML Consumption Big Data Relationship Management Identity Resolution Search - Solr EMAP Solution Architecture

15. Technology Stack 15

16. Identity Resolution Data Quality Ingest & Profile Cleanse & Transform Standardize / cleanse phone, email, address DOB BDM Tool: Developer & Analyst Tool Cleanse & Transform Standardize / cleanse phone, email, address DOB BDM Tool: Developer & Analyst Tool Source 1 Source N Masking Cleanse, Standardize, Mask Personal Data BDM Tool: Developer Masking Cleanse, Standardize, Mask Personal Data BDM Tool: Developer BDRM matching/linking (assign cluster ID ) BDRM matching/linking (assign cluster ID ) Pre BDRM Managed Views Pre BDRM Managed Views Pre BDRM Managed Views Pre BDRM Managed Views BDM Managed Views (Current) BDM Managed Views (Current) BDM System Tables (Date) Hive BDM System Tables (Date) Hive BDRM Hbase/Hive BDRM Hbase/Hive Post BDRM Managed Views Post BDRM Managed Views Post BDRM Managed Views Post BDRM Managed Views Post BDRM Managed Views Post BDRM Managed Views Profile Data Discovery Design/Define rules Build/Define Ref Table BDM Tool: Analyst Tool Profile Data Discovery Design/Define rules Build/Define Ref Table BDM Tool: Analyst Tool Address Doctor process Address Doctor process EMAP Data Process Flow

17. Informatica Big Data Developer

18. HDFS Data Management Structure 18 Managed by IT Core set of Informatica developers to create the mappings to ingest data from sources into HDFS (jobs run and monitored by IT) Users can request access via a defined security approval process (Access can be limited at the file level) Access will be tracked and can be included in auditing reports/events SSN will only be allowed in the core data ingested from source systems. Managed by the Team Governed onboarding process: IT will assist the team with setting up directories, reviewing recommended data flow process, reviewing security privileges, education on platform security and compliance issues, and providing basic setup guidance and assistance Access will be tracked and can be included in auditing reports/events MANAGED DATATEAM DATA Physically Separated by Directories

19. Audit Events Audit events/actions Include: Failed login attempts Role changes Data access Denied data access Any many more … Alerts -- Can email an alert when a specific event occurs Data captured about the audit event: Date Command performed Object affected User that performed the action IP Address Audit Events ARE NOT Stored on the Hadoop Cluster

20. Summary of Key Benefits Provides a single platform to house key customer and prospect data sources Establishes persistent keys across previously disparate data sources Provides for rapid intake of new data sources (structured and unstructured) Eliminates today’s data intake and append bottleneck Empowers analysts to explore all data elements Increases processing power for statistical analysis Improved recruiting and retention of Data Engineers and Data Scientists 20

21. Lessons Learned Tie to business use cases to demonstrate value Align with larger enterprise strategy (business and technical) Socialize the platform and vision Technology is changing rapidly Partner with key vendors Partner with business Invest in a PoC Small team with the right skillset - innovative and curious 21

22. Lessons Learned Big Data requires Data Governance Establish tools and processes to support data governance from the start Important to have Data Stewards: Profile, validate, catalog, metadata creation, lineage 22

23. Transamerica is Hiring! bigdatajobs@transamerica.com

24. Questions?