No Data Governance, No Actionable Insights

Transcription

1 DATA SMALL DATA MASSIVE DATA No Data Governance, No Actionable Insights Ram Kumar Chief Information Officer, Asia Insurance Australia Group (IAG) Australia MORE DATA MEDIUM DATA LARGE DATA OBESE DATA June 5, 2013

2 Insurance Australia Group (IAG) Some of the world s most trusted and recognised brands $9 billion Insurance Premiums $1000 billion worth of property insured 14,000+ employees in Aus and NZ 8,000+ employees outside Aus and NZ in Joint Ventures Global 100 sustainable Corp. Presence in AUS, NZ, U.K, India, China, Malaysia, Vietnam and Thailand About $1.5 Billion GWP from Asia Thailand, Malaysia, India, China, Vietnam 2

3 Big Data Big Dreams & Big Expectations The Great Race Big Data Big Dreams & Big Expectations, but with little Strategy and Direction A well thought out enterprise wide strategy Purpose - Goals, objectives, requirements, context, value and outcomes Scope Direction Experimentation Capability Governance Are you using Internal Big Data effectively and efficiently to get the required key insights and value? Do not get me wrong here I am NOT questioning or challenging the value Big Data brings 3

4 Key Characteristics of Big Data VOLUME VELOCITY VARIETY VERACITY VALUE Data at Rest Data in Motion Data in Many Forms Data in Doubt Data that Creates Value Terabytes to Petabytes to Exabytes to Zettabytes to Yottabytes of existing data to process Streaming data, milliseconds to seconds to sub seconds response Structured, unstructured, text, multimedia Uncertainty due to data inconsistency & incompleteness, ambiguity, latency, deception, model approximations Contextual, purposeful, meaningful, relevance, applicable Volume, Variety, and Velocity without Veracity and Value creates Vulnerability Content isn t king; it s context that s king 4 Extended IBM work

5 More data means more information and more/better analytics/insights? Does the size of Data matters? More data = more information? More information = more insights? Big Data = bigger insights? Data + strategic thinking = better insights 5 Ref: Dr.Michael Wu, Lithium, 2013

6 Data and information are the lifeblood of insurance Data is therefore a corporate asset Data/ Information Management 6

7 Organisations are moving from process centricity to product centricity to service centricity to now.customer centricity Consistent & reliable data and information Product Centricity Innovation Customer Centricity across all channels Service Centricity Data Analytics & Insights Centricity O U T C O M E S Information Centric Platform of an Enterprise (INFORMATION CENTRICITY) E N A B L E R S People Process Technology Data Culture 7

8 Information Driven/Centric Organisation Culture Need end to end management of data and information lifecycle An information centric organisation has the flexibility to rapidly deliver information as needed to optimize processes, applications and business decisions for sustained competitive advantage. 8

9 Principles of Data Management remain the same Whether it is just data or morbidly obese data Bigger isn t better, better is better. Although big data may indeed be followed by more data that doesn t necessarily mean we require more data management in order to prevent more data from becoming morbidly obese data. I think that we just need to exercise better data management. Jim Harris Whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bites, the truth is we were consuming way more than our recommended daily allowance of data long before the data management industry took a tip from McDonald s and put the word big in front of its signature sandwich. More Data becomes Morbidly Obese Data only if we don t exercise better data management practices Jim Harris 9

10 Data Governance vs Big Data Governance DATA Governance Metadata Data classification Stewardship Data quality Data Context Data lifecycle management Privacy Policy Principles Standards Data Architecture Metrics Data Integration Culture BIG DATA Governance Metadata Data classification Stewardship Data quality Data Context Data lifecycle management Privacy Policy Principles Standards Data Architecture Metrics Data Integration Culture 10 More data types More metadata More sophisticated tools More data sources Non trustable sources Larger volumes

11 Big Data Governance Answer the what, why, how, who, where and when questions if you want to get sustainable business value Have you identified the key business stakeholders for the big data program Have you quantified the financial benefits from big data program Do you have a defined scope for big data that applies to your organization? How will you address the stewardship of big data? Have you established the linkage between big data and risk management? Have you documented a set of policies for big data governance? Do you have consensus on the quality issues associated with big data where the value of the data may or may not be high or obvious? Do you understand the business requirements that drive the retention of big data Do you understand the terms of use of Facebook, Twitter, and other types of social media data? 11 Ref: Tom Deutsch, IBM Have you determined which applications should move into the big data infrastructure platform? Does your organization-wide business terminology (business glossary) include key business terms relating to big data? Do you have database administrators, contractors, and other third parties who possess unencrypted access to sensitive big data such as geolocation data, telephone call detail records, utility smart meter readings, and health claims?

12 Roadmap to tackle Big Data and More Data in the future IBM,

13 Our Strategy covers Information Lifecycle Management Endorsed Group Business and IT Strategy 13

14 Laying the IM Foundations

15 Our Goal Create a culture of information management Get the Right data to the Right place at the Right time in the Right format with the Right quality in the Right context with the Right security and, with the Right governance* *OASIS Party Information International Standards, 15

16 An Enterprise IM Strategy to drive IM culture requires support from the Top 16

17 Creating a culture does not happen overnight Requires Accountability (Structure => Behaviour => Culture) 17

18 Do we know our Customer well enough with our internal data? Foundation /Fundamental for customer centricity Who is John Doe? What do I know about John Doe? Can you identify/recognise your Customer John Doe? 18

19 Achieving Customer Centricity through Information Centricity Chat (Txt) Chat (Video) Fax Chat (Voice) Mobile Consistent & Comprehensive Customer Interactions and Views ATM Internet Social Media Kiosk Branch (F2F) 19 SMS

20 Federated Domain Centric Source of Truth The Foundation Policy Data Claims Data Customer Interactions Data Enterprise Data Model Enterprise Data Dictionary Party, Location & Reference Data Standard Information Services Data Quality and Integrity Accounts Data Employee Data Partner Data 20

21 So, what are we doing about Big Data? Three Options: Option 1: Do nothing for 2-3 years. Not recommended as we will be left behind by our competitors by a long way and it will not be trivial to catch up Option 2: Do a pilot now. Recommended as it gives us the opportunity to experiment and learn about the sustainable value big data could provide the business that would help us to make strategic investment decisions Option 3: Go Big Bang initiating a Big Data project/program. Not Recommended as the concept of Big Data is still evolving along with the tools and the industry is still learning. 21

22 Big Data Landscape..and nobody has claimed leadership position yet. It is too early. It is getting crowded 22 Ref: David Feinleib, July 2012

23 So, what is our roadmap to implement Option 2? Step 1 A community of practice for information and analytics; Identify business units to be the testing ground; Executive sponsored; Backed by a team of data analysts Step 2 Challenge each key function to identify 2-3 business opportunities based on big data, each of which could be prototyped within five to seven weeks by a small team Step 3 Implement a process for innovation that includes four steps: experimentation, measurement, sharing and replication. Look for success stories. Step 4 Shortlist a set of tools to be used for experimentation. Step 5 23 Study the outcomes of the experiment and develop a comprehensive Big Data implementation strategy that is aligned with the enterprise information management strategy Ref: HBR, Oct 2012

24 Reference: 30 Pages Chapter about our work Book published by Springer Verlag in April 2013 Ram Kumar & Robert Logie, Creating an Information- Centric Organisation Culture 24

25 Thank you 25