Business Analytics og Optimization, BI på superoktan (IBM Global Business Services)

Business Analytics &
Optimization: BI på superoktan
Ivar Juul
Partner, IBM GBS, Nordic BAO Leader

Agenda

What is BAO
Having data simply is not enough
Advanced Analytics have many forms
– Checkout Analytics Surveillance Engine
– Fraud and Abuse Management System
Develop a business case for Advanced Analytics
What problems are our customers facing
Consolidation of the data warehousing platform

© 2010 IBM Corporation

How IBM’s Business Analytics & Optimization practice helps clients
Business
Intelligence & Enterprise Enterprise
Performance Advanced Analytics Information Content
BAO Strategy Management and Optimization Management Management

• Identify and • Report outcomes • Apply advanced • Ensure robust • Manage document
prioritize of business statistical and and trusted data & records,
opportunities for processes and regression is available including archives
improvement programs analysis upon when needed • Manage structured
• Automate historical data for and is easy to
• Change business and unstructured
management predictive consume
processes and content
dashboards and decision-making
operations to • Provide a • Manage digital
exploit analytics scorecards • Integrate consolidated assets & rights
• Create planning, optimization and efficient
• Implement algorithms and • Provide efficiency
budgeting, & information
management technology into and transparency
forecasting tools platform to
systems to operations to complicated
support
maintain control workflows
optimization
and achieve
initiatives
goals


When having data simply is not
enough:
A Post September 11th
Relationship Analysis of the Hijackers


2 Known Terrorists in US

WATCH LIST: CIA/INS/FBI POSSIBLE TERRORISTS IN THE US:
On or before August 23rd, 2001, Nawaq Alhamzi and Khalid Al-Midhar added to INS watch list

MAKE PLANE RESERVATIONS USING SAME NAMES:
On or about August 25, 2001, Khalid Al-Midhar purchases cash ticket for American Airlines
flight #77 scheduled for September 11, 2001
On or about August 27, 2001, Nawaq Alhamzi books a flight on American Airlines flight #77
scheduled for September 11, 2001

9 Transforming the Business of Government © 2010 IBM Corporation

Two of the Nineteen Terrorists Known

American Airlines
Flight 77
Target:
Pentagon
Nawaq Alhamzi Salem Al-Hazmi Hani Hanjour Khalid Al-Midhar Majed Moqed

American Airlines
Flight 11
Target:
World Trade Center
North Tower
Mohamed Atta Wail Alshehri Abdulaziz Alomari Waleed Alshehri Satam Al Suqami

United Airlines
Flight 175
Target:
World Trade Center
South Tower
Ahmed Alghamdi Marwan Al-Shehhi Fayez Ahmed Mohand Alshehri Hamza Alghamdi

United Airlines
Flight 93
Target:
Unknown

Saeed Alghamdi Ahmed Al Haznawi Ahmed Alnami Ziad Jarrah


Address Connections

RESERVATIONS MADE WITH ADDRESS #1 AND ADDRESS #2
On or about August 25, 2001, Khalid Al-Midhar makes a reservation on American Airlines flight #77
scheduled for September 11, 2001 using Common Address #1
On or about August 27, 2001, Nawaq Alhamzi books flight on American Airlines flight #77 scheduled for
September 11, 2001 using Common Address #2

ADDRESSES ARE USED BY THREE (3) ADDITIONAL PASSENGERS
Mohamed Atta has reservation on American Airlines flight #11 scheduled for September 11, 2001 using
Common Address #1 as a contact address
Marwan al-Shehhi has reservation on United Airlines flight #175 scheduled for September 11, 2001 using
Salem Alhamzi has reservation on American Airlines flight #77 scheduled for September 11, 2001 using


Five of the Nineteen Terrorists Linked

American Airlines
Flight 77
Target:
Pentagon

American Airlines
Flight 11
Target:
World Trade Center
North Tower

United Airlines
Flight 175
Target:
World Trade Center
South Tower

United Airlines
Flight 93
Target:
Unknown



Phone Number Connections
ONE (1) ALERTED PASSENGER MAKES RESERVATION USING COMMON
TELEPHONE NUMBER
On or about August 28, 2001, Mohamed Atta uses
Florida Telephone #1 as a contact number when making reservations on American Airlines flight #11
scheduled for September 11, 2001

NUMBER IS USED BY FIVE (5) ADDITIONAL PASSENGERS
On or about August 26, 2001, Waleed Alshehri and Wail Alshehri make reservations on American
Airlines flight #77 scheduled for September 11, 2001 using Florida Telephone #1 as a contact number
On or about August 27, 2001, reservations for electronic, one-way tickets were made for Fayez Ahmed
and Mohand Alshehri for United Airlines flight #175 using Florida Telephone #1 as a contact number
On or about August 28, 2001, Abdulaziz Alomari reserves a seat on American Airlines flight #11 using
Florida Telephone #1 as a contact number


Ten of the Nineteen Terrorists Linked

American Airlines
Flight 77
Target:
Pentagon

American Airlines
Flight 11
Target:
World Trade Center
North Tower

United Airlines
Flight 175
Target:
World Trade Center
South Tower

United Airlines
Flight 93
Target:
Unknown



Frequent Flyer Connections

ONE (1) ALERTED PASSENGER MAKES RESERVATION USING A
FREQUENT FLYER NUMBER
On or about August 25, 2001, Khalid Al-Midhar makes a reservation on American Airlines
flight #77 scheduled for September 11, 2001 using Frequent Flyer #1

FREQUENT FLYER NUMBER IS USED BY ONE (1) ADDITIONAL
PASSENGER
On or about August 25, 2001, Majed Moqed makes a reservation on American Airlines
flight #77 scheduled for September 11, 2001 using Frequent Flyer #1


Eleven of the Nineteen Terrorists Linked

American Airlines
Flight 77
Target:
Pentagon

American Airlines
Flight 11
Target:
World Trade Center
North Tower

United Airlines
Flight 175
Target:
World Trade Center
South Tower

United Airlines
Flight 93
Target:
Unknown



Remaining Connections
PUBLIC RECORDS
Alerted subjects Nawaq Alhamzi and Khalid Al-Midhar lived with Hani Hanjour
Alerted subject Wail Ashehri was roommates and shares PO Box with Satan Al Suqami

WATCH LIST: INS ILLEGAL/EXPIRED VISAS
On or about August 29, 2001, Ahmed Alghamdi reserves an electronic one-way ticket on United Airlines
flight #175 scheduled for September 11, 2001

FIVE (5) ADDITIONAL PASSENGERS
Alerted subject Ahmed Alghamdi and Hamza Alghamdi both use same address on their airline reservations
Alerted subject Hamza Alghamdi has/does live with Saeed Alghamdi, Ahmed Alhaznawi, and Ahmed Alnami

Alerted subject Ahmed Alhaznawi has/does live with Ziad Jarrah


All Nineteen Terrorists Linked

American Airlines
Flight 77
Target:
Pentagon

American Airlines
Flight 11
Target:
World Trade Center
North Tower

United Airlines
Flight 175
Target:
World Trade Center
South Tower

United Airlines
Flight 93
Target:
Unknown



Only Three Degrees of Separation Links All Nineteen 9/11 Hijackers

Khalid Al-Midhar Nawaq Alhamzi Ahmed Alghamdi

Majed Moqed Mohamed Atta Marwan Al-Shehhi Salem Al-Hazmi Hani Hanjour Hamza Alghamdi

Waleed Alshehri Wail Alshehri Fayez Ahmed Mohand Alshehri Abdulaziz Alomari Saeed Alghamdi Ahmed Alhaznawi Ahmed Alnami

Satam Al Suqami Ziad Jarrah


The world is becoming smaller and flatter,
but also needs to be smarter.


All Scan Avoidance Transactions Displayed

Video Frame linked to each item


Checkout Analytics Surveillance Engine

POS Data, Video, Analytics – Bringing it all Trial results
together – 15 cases of passing
– 10 cases of Void or Over charging error
– Detect scan actions visually
– 2 operational issues (1 potential staff
– Match up visuals scans with barcode reads discount fraud)
– 3 cashiers with more than 1 type of error
– Identify visual scans which have no barcode
read = “Noscan” Simple ROI
– £75 in 3 hours across 10 lanes
– Insert Noscans into data mining database – £75 * 3 hour loss total store = £225
– Assume 12 hours of operation = £900 per
store per day
– 364 day year = £327,600


Fraud and Abuse Management System
FAMS is a full function retrospective analysis solution
FAMS Solution
Investigators Profile Analysts Why Base FAMS?

Detection of anomalous behavior

Developed with innovative industry leaders
FAMS System FAMS System

•Provider Rosters Proven technology
•Profile Values & Scores
•Profile & Claims Reports

Significant levels of success achieved by
clients
Analysis Results Profile Analysis

Designed for ease of use for broad range of
Downstream
FAMS users
Business Provider
Processes Profile Scoring
Profiles

Powerful Spectrum of Integrated Analytical and
Reporting Components
Transaction
History


Predict Behavior & Preferences
IBM SPSS Modeler for Modeling Future Behavior

Maximize Analyst Output
– Easy to learn, no programming approach to data
mining
Create Practical Solutions Faster
– Automatically create accurate, deployable predictive
models
– Choose the best solution with multi- model evaluation
Open & Efficient Architecture
– Data mining within standard databases
– Multithreading, clustering and use of embedded
algorithms

Building a business case:
• Is there a pattern in the data and is it usable ?
• Before investing in SPSS we can analyse the data for patterns
• Develop a business case
• Fast way of getting business insight


What pain points do our customers have

Missing loading predictability No roadmap to identify integration points across
Long response times organizations, project types, or logically
Too many errors promoted to production sequence re-engineering and/or transformation
Funding only for per project, not for architecture initiatives in the company
Lack of confidence in realizing benefits from BI No center of competency to execute/support BI
investments initiatives
Inconsistent measurement/data definition Confusion over lack of common language
No mechanism to correct errors in source around needs, solutions, and approaches
systems No BI technology standards
Too many “standard” BI tools Inability to recognize and manage cross initiative
Many uncoordinated and redundant data dependencies
mart/data warehouse efforts Inability to leverage past investments
Data strategy and data management initiatives Inability to anticipate BI needs
not linked to business priorities and needs Multiple versions of the truth
No common framework to address Lack of improvement in cycle time to meet new
integration/communication needs across BI needs
organization boundaries Not effective leverage skills and resources
across BI initiatives


Loading issues

Variations in number of loaded records Loading predictability per month
Number of loaded COPA records (2006)
100%

800000
90%

700000 80%

600000 70%

500000 60%
High
Records

50% Red
400000 Low A mber
Green
Average 40%
300000
30%

200000
20%

100000
10%

0
0%
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10
M onth

Month

High in month 6 & 11 has been removed
Many data warehouse installations have
problems with
Records loaded by day in January (3 years) – Loading predictability
– Keeping the batch window open
Records per Day

600.000.000
No procedures for follow up on errors in the
500.000.000

400.000.000
source systems effecting the confidence
No of Records

2005-01
300.000.000 2006-01
2007-01 Loading is a project related issue
200.000.000

100.000.000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Date


Annual cost saving of around $100 million in an American Bank

Supply side Consumption side

Sources Data Landing Data Semantic BI Analytics
Integration Zone Repository Layer
DLZ
Reference Data
Views
Original Data CUSTOMER
Certified Translated / Re-formatted Data
Business PRODUCT
Data Soft Exceptions

Hard Exceptions INDUSTRY
Process Logging
Monitor & Control ASSOCIATE

Private Staging Area
ORGANIZATION

1 6
$30MM - 1 8
4 $70MM - $85MM $45MM - $55MM
3 $35MM 2 7

10 Data Governance / Data Quality
6
11 Metadata
Security and Data Privacy $12MM - $25MM
5 4 9 Systems Management & Administration
Hardware & Software Platforms

Supply: Consumption: Cross:
Transformation

1. Data integration hub 6. Design semantic layer 10. Design
Initiatives

2. Consolidate warehouse model 7. Migrate SAS functions to the governance
3. Consolidate external data database Frameworks
4. Sunset existing IM environment 8. Rationalize BI software 11. Metadata layer
5. Design new IM data integration COE 9. Design new IM org model –
reporting tools (BI COE)

Legend: Estimated Direct Annual Cost Savings


Cost Take Out

Core
American Bank 1 American Bank 2
Initiatives
Description Tangible cost reductions
ETL Consolidate the ETL and batch
processes via a methodical 6% $7.750.000 22% $15.575.000
– Disk requirements
engineering approach – MIPS consumption and CPUs requirements
Data Quality Formalize a data quality effort to
minimize the risk of errors
9% $11.900.000 4% $2.775.000 – License cost
Master and Enable a metadata strategy that – Operations cost
reference data includes a repository, user
interface and creation of
9% $11.700.000 5% $3.760.000 • Reduction of operations cost in monitoring
metadata artefacts
Data model Rationalize the current data
and managing loading
objects to a single logical 3% $3.300.000 12% $8.775.000 • Reduction of hardware requirements
conformed data model
Data marts Sunset data marts in the analytic
layer
36% $46.400.000 22% $15.225.000 Intangible cost reductions
Reporting/anal Consolidate analytics tools and
8% $10.800.000 5% $3.775.000
– Development time and resource requirements
ytics tools rationalize external data
SAS Re-platform, consolidate and • Easier access to data via the semantic layer
6% $7.600.000 0% $0
governor the SAS environment • Documented meta data
Analytics Deploy a consolidated analytics
layer to remove the complexity
15% $18.900.000 20% $14.445.000
• Increased reuse
and reduce redundancies
– Maintenance costs
Governance Initiate a governance program to – Higher loading predictability
manage changes, data
definitions and processes 9% $11.700.000 9% $6.375.000 – Fewer errors due to better governance
associated with the analytics
solution
Total 100% $130.050.000 100% $70.705.000

The projects had a positive cash flow after 9 and 12 months and IBM worked
on a risk sharing contract with IBM Global finance involved


Example: Business case for re-implementation of DW

140
Business case
120
– Cost is implementation cost excl. internal
100
hours
80 – Benefits does only include tangible benefits
60 like
40
• Reduction of MIPS and CPU capacity
• Disk capacity
20
• Reduction of license cost
0
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6
• Reduction of operation costs
-20 • Easy of solvency II implementation
-40 • Reduction of man power due to easier
-60
access to data and economy of scale
benefits
– The business case does not include
• Better reporting
The client is running 6 data warehouses across
• Single point of truth
the Scandinavian countries
Business case for data warehouse transformation
project
– One unified data warehouse based on IIW
– Migration of all historical data


© Copyright IBM Corporation 2010 All rights reserved. The information contained in these materials is provided for informational purposes only, and is provided AS IS without warranty
of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, these materials. Nothing contained in these materials is
intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license
agreement governing the use of IBM software. References in these materials to IBM products, programs, or services do not imply that they will be available in all countries in which IBM
operates. Product release dates and/or capabilities referenced in these materials may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are
not intended to be a commitment to future product or feature availability in any way. IBM, the IBM logo, Cognos, the Cognos logo, and other IBM products and services are trademarks of
the International Business Machines Corporation, in the United States, other countries or both. Other company, product, or service names may be trademarks or service marks of others.

35

Business Analytics og Optimization, BI på superoktan (IBM Global Business Services)

Recommended

Recommended

More Related Content

More from IBM Danmark

More from IBM Danmark (20)

Recently uploaded

Recently uploaded (20)

Business Analytics og Optimization, BI på superoktan (IBM Global Business Services)