Creating a Smarter Bank

Bohdan Szymanik, Kiwibank

http://bohdanszymanik.blogspot.com

Objective: get more productive!

Execution

Decision Making

Intelligence

Information

Communication Challenge

2001 2002 2003 2004 2005 2006 2007 2008 2009

Infrastructure Staff

How About Kiwibank?

Conventional BI Isn’t Enough

“Focus on Data Management”

“Focus on Financials”

“It has to be right!”

“Focus on making it easy”

Where’s the data?!

What do we do?

We look for analogues!

Lessons from Systems Management

•Big data

• Diverse data

• Context

• Health indications

Sampling

Hierarchical ModelsBespoke Analysis

Monitors and Trends

The volume and diversity is so great that conventional data modelling

is just not done.

Kiwibank Experience: System

Center

Scheduler Component

Server AScheduler Component

Server B

System Center = A Data Source

What have we learnt?

• Sample data

• Don’t bother with relational models

• Model context can be very simple

– Graphical

– Descriptive

• Make data available true to source

• Expect end user analysis

Empowering the analyst!

Make data available and provide end user toolsAnalysts create local models and local queries

Data Analyst

+ Model Analyst

+ Query Analyst

= A Tough Combination?

Maybe Not…

Localised model creationIn memory databasesContent management

Empowered Analyst

Customer>1,000,000

records

Investment Accounts

<1,000,000 records

Who do we get our money from!?

HP Mini2 GB RAMIntel Atom

Sharepoint2010

My new marketing campaign!

$ as a function of (Surname first letterSurname length )

A

C

E

G

I

K

M

O

Q

S

U

W

Y

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Web 2 / Enterprise 2 = new

data sources!

Social AnalyticsDS 4.3CB 3.0BM 2.0JC 1.7DO 1.4KT 1.0BS 1.0NC .9JW 0.9AH 0.9CB 0.9SK 0.8TB 0.7DG 0.6NL 0.5BN 0.5

How?• Code – Team System• Documents, Wiki, Blog – Sharepoint

Example:Systems of more importance have had more change over time. Correlate change to individuals and account for shared knowledge to identify where people have worked on many small projects, and therefore present significant key man risk.

I keep saying the sexy job in the next

ten years will be statisticians.

Hal Varian, Google’s Chief Economist, The

McKinsey Quarterly, January 2009

http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?_r=1

http://www.forbes.com/forbes/2010/0524/opinions-software-norman-nie-spss-ideas-opinions.html

Summary

1. Don’t push non-financial data through conventional BI stacks

2. Empower analysts

– Make information available

– Use minimal models

– Don’t be scared of desktop query

– Don’t be scared of code