Dansk IT Big Data i de største danske banker

Transcription

1 Dansk IT Big Data i de største danske banker How can we realize the benefits Presentation 7/ Jens Chr. Ipsen, head of Information Management & Data Warehouse

2 The essence of Danske Bank Vision To be recognised as the most trusted financial partner Strategic core We are a modern bank for people and business across the Nordics with deep financial competence and leading, innovative solutions Customer promise We help customers be financially confident and achieve their ambitions by making daily banking and important financial decisions easy Core values Expertise, Integrity, Value creation, Agility, Collaboration 1

3 Overview Danske Bank has a strong Nordic franchise Facts million customers 300 branches 1 15 Countries 19,000 full-time employees Business units Personal Banking Business Banking Corporates & Institutions Wealth Danica Pension Danske Capital For divestment Non-core (Ireland & Conduits) Personal banking activities in the Baltics 1. Excluding agricultrural centres in Denmark 2. Market share by lending 2

4 Agenda How we started the journey Design solution Opportunities & Challenges How can we realize the benefits How to continue the journey 3

5 Analytical landscape Report, BI and advanced analytic capabilities in business units Industrialized analytics and IT capabilities centered in Group IT as solution provider 4

6 How we started the journey Danske Bank has started the journey but there is still a distance to go Before 2014 Empty talk and no action Focus on classic analytics 2014 Consolidation of major Information Management disciplines 2015 Research project and pilots 2016 Mature organisation and technology First business projects 2017 Business driven Big Data is just data and advanced analytics will be the driver Learning Sometimes IT should take leadership 5

7 How we started the journey To become leader in digital financial services Danske Bank must master 5 technology trends We must Mobile Social Analytics Cloud... support mobile as the primary customer platform, and support any device, any place, any time business utilise social technologies as key competitive capabilities for customer collaboration and employee productivity use analytics and Big Data to better understand customers and their needs, and to improve our offerings use cloud as a key element of high performance utility IT 5 Cybersecurity defend our digital offerings against threats, balanced with a convenient and trustworthy customer experience 6

8 How we started the journey The vision effective and efficient! D a t a Q u a l i t y Advanced Analytics Performance Management Enterprise wide dashboards Basic reporting Reporting Operational, Regulatory, Self service Data Foundation (structured ) Enterprise Data Warehouse Analytics & Decisions Business Intelligence Big Data (semi/ unstructured ) Data Management What should individuals or systems do and why What are the alternatives and what are their costs 7

9 How we started the journey Input to the vision is based on industry inputs, benchmarks and internal feedback Data Reservoir BCBS 239 Data Lake Data Hub BI Ecosystem 8

10 Design solution The information management strategy will support Danske Bank in become more data driven Business 1 2 Promote Business Analytics Present and visualize information Platform 3 4 Create Information Management platform Data Governance Organization 5 6 Optimize organization and collaboration Effective and efficient information management 9

11 Decisions & calculation engine Design solution The System covers not only data discovery but should be part of the core IT infrastructure Enterprise Service Bus Customer Account Data Order Mgmt. System New Account Risk Screen Data Sources Server Logs Click stream Data s Word Document Data Hub Legacy Data warehouses Common Integration Layer (Data Warehouse) Analytics Risk and Credit models Customer Insight FRAUD & AML Deposit spread optimization Price optimization Trading Risk Underwriting optimization Debt collection IT management GUI optimization, etc. Advertising Databases Marketing Data Warehouse Trade Confirmations Insurance Claims Social & Chat Data CRM News feeds 10

12 Attractiveness For Internal Use 10 Design solution In 2015 The Big Data pilot addressed 4 key themes 1 Pilot 1 Online/Offline data integration 2 Pilot 2 Text mining data integration in credit risk models Channel optimization Online/offline data Credit/Risk models Text mining data Support existing initiative Integrate online/offline data in existing predictive models Exploration of customer behaviour across online channels and websites Integrate with existing customer data Enhancing early warning with external data has a big potential for improving performance Unconventional data sources and text mining to enhance early warning 3 Big Data platform 4 Executive Board for decision making Hardware Software/tools ExBo recommendation Big Data roadmap Strategic options Retain Keep as is Organic growth A 1 2 High Map individual options x Strategic Horizon build platform for immediate organic growth and longer term expansion x P&L Organic growth B 3 x Strategic option Grow Divest Organic growth C Partnership Acquisition Close down Sell Low x x x x x Strategic fit with group High Continued organic growth Commence implementation of new group and business services Attack competitors Explore Asian opportunities Scope broader palate of group Migrate to Bank platform and business opportunities Sweep in target Nordic clients from group Establish UK set up Investigate and implement hardware and middleware for Big Data platform Investigate and implement software for Big Data advanced analytics ExBo presentation and recommendation Suggested Big data roadmap for decision 11

13 Design solution Advanced analytics is enabled by the Big Data Eco system Data Compliance Exploring process Big Data Eco system Governance Benchmark Technology Competencies 12

14 Design solution A four to six week development process are used for test of ideas 1 POC case Define POC case and align expectations 6 2 Data Fetch Analysis on potential data sources Request for data (manual) Store data for POC usage (Hadoop optional) 5 3 Data cleasing Mini data profiling Tool consideration QA consideration 4 Data ready for application use Data delivery definition Optional re-use of other data Make data delivery 7 Data modelling Data mining Tool consideration (RapidMiner, R, SAS) Test/simulation of hypothesis (parameter selection) for model 8 Data transformation Tool consideration Analyse request for transformation (ex. aggregation, summation, average, filtering etc.) Transformation of data Application test Test of data delivery POC evaluation Evaluate POC criteria Next step other POC or project? 13

15 Opportunities & Challenges We need to change our development process... From Specific business need/requirement Project implementing solution To Search for the business need (Data will tell us) Specify requirement Project implementing solution Outcome Only discovery Solution deployed NEW: This collaboration model need to be established How do we make sure to have progress in this field? Classic projects follows the classic governance processes 14

16 Opportunities & Challenges Considerations on how do get the best progress going forward 1 We must avoid the Netflix case! Netflix never implemented the algorithm that won The Netflix $1 Million Challenge because it could not be implemented IT wise (link) 2 End of day Advanced analytics should be implemented in production in IT systems Discovery and model development is a different discipline than implementing in IT systems. 3 Focus is important to get progress learnings must be shared as long as we are an immature organization Let s not split the people in to many organizational instances. Result: Too many of the few spend time on solving the exact same problems 4 Do not make a split of the Information Management Platform No one can say what kind of data we need to a project 15

17 Opportunities & Challenges Why a data scientist is not just a data scientist Platform Capabilities Data Warehouse IM / Hadoop HW, SW, Tools Operation Computer science skills Capabilities Data Performance, test and implementation Operation Business domain skills Capabilities: Discovery Model development Businescase decisions 16

18 Opportunities & Challenges An integrated team between Business and IT works best Central IT Platform Data Engineering (ML) Machine Learning and other Artificial Intelligence disciplines Business unit (without skills in advanced analytics) Subject matter experts Business unit Subject matter experts Business analysts Modeling 17

19 Opportunities & Challenges Data Governance will be a challenge.we need to solve it. Data Governance Council CDO? Common Integration Layer (CIL) Management reports Data Clusters Data Subjects Principles and Policies Customer Cluster Board Data Specialists Product Cluster Board Data Specialists Business Model for Customer Business Model for Product Master Data Management System Data Standards Operational Data Management Systems Data Quality Responsible Data Quality Responsible Data Quality Responsible Data Quality Responsible Sys #1 Sys #2 Sys #3 Sys #4 18

20 How to continue the journey Project pipeline for 2016 is characterised by business drivers Big Data research We continue the journey to improve the Big Data Ecosystem Platform, Processes, security and toolset is getting improved to allow analysts to quickly gather insight using machine learning etc. Implementation of Hadoop as landing zone for our enterprise data warehouse Online/Offline data integration and life events is part of the proactive customer contact strategic program Use Weblogs to identify customer behaviour and optimize the offer presented Fraud Improve fraud engines with advanced analytics Customer behaviour and Transactions Analyse business customers, customer journeys, transactions to better profile business customers and risk evaluation Risk Analytics Enable advanced analytics in risk and credit models or development of models 19

21 How to continue the journey Roadmap and IT capabilities reflects business needs Analytical areas \ Capabilities Real-time Weblogs Adv. Data Capture Methods Security Self Learning Compliance Discovery Cognitive Analytics on customer behavior based on weblogs, mobile behavior Next best offer Support all channels (mobile, web, call centre etc.) Robotic/Automated loans, advisor guidance etc (Cognitive analytics) Automated help desk (Cognitive analytics) Wealth management (Cognitive analytics) + + Micro segmentation / profiling In progress Planned Not planned Social media analytics / Sentiment analysis on customer responses Text mining on financial news feeds, statements etc Network/Influencers Fraud (+) Location Marketing Risk and customer insight models Total over 5 years; mdkk IT Big Data Core team will navigate based on these capabilities and the changing business requirements and not a classic road-map. 20

22 How we started the journey: A IM strategy and a investment in research Design solution: Some just install and start we took a broader IM approach Opportunities & Challenges: Not so much technology but more organization Learnings How to continue the journey: Business focus, agile governance, just data 21

23 Questions 22

24 Thank you 23

25 Our TO-BE architecture is the foundation and Hadoop plays a major role Big Data/Hadoop capabilities is introduced Common Integration Layers (CIL) is the trusted area Efficient use of BI incl. shared dimensions is in focus Advanced analytical capabilities and decision engines Self-Service BI and other end-user technologies Focus on crossdepartmental disciplines (data governance, architecture, QA) 24

26 Ambari Hadoop End User Statistica Zeppelin SAS VA Hue DataStage Tableau Excel ODBC Application Stack Hive Pig Oozie Spark HBase TEZ YARN HDFS Middleware OS 25