Outline - Buckeye DAMA · • Donor Centers paid for each registered donor and marrow donation 22 ....

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Case Study Workshop

Buckeye DAMA

Presented by Lewis Broome

February 25, 2016

Copyright 2014 by Data Blueprint

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Outline

• Case Study 1: Deutsche Bank

– Background

– Data-Driven EA Design

– The Results

• Case Study 2: C.W. Bill Young Marrow Donor Program

– Background


– The Results

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Background

• About the Company

• Market Positioning

• Operating Model

• Functional Decomposition

• Business Needs and KPI’s

• Current State Assessment


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About the Company (1)

• A German Bank

• Founded in 1870

• Their Big & Global

– #6 in the world based on assets ($2.6 trillion!)

– Top 5 Wall St. Firm

• Multiple Divisions / Products

– Retail Banking

– Private Wealth

– Investment Banking

– Corporate Banking

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About Investment Banking

Business Unit

• Broad range of products and services

• Focused on serving

– Public and private institutional traders (pension funds, mutual

fund companies like Fidelity Investment)

– Private equity companies

– Hedge funds

• Three sub-divisions: Fixed Income; Equities & IBD

• Organized (and culturally divided) into “The Front Office”

and “The Back Office”


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Hubs for Investment

Banking Locations

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Market Positioning (1)

Source: Business Insider: http://www.businessinsider.com/the-definitive-

ranking-of-wall-street-investment-banks-in-every-business-line-2015-9


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Market Positioning (2)

Lower Cost Differentiation

Broad

Range of

Buyers

Narrow

Buyer

Segment

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Operating Model

Coordination

• Shared customers, products and suppliers

• Impact on other business unit transaction

• Operationally unique business units

• Autonomous business mgmt. & process design

• IT application align to business units

Unification

• Customers & suppliers may be local or global

• Globally integrated business processes

• Use enterprise systems

• BU’s with similar or overlapping operations

• Centralized management often matrixed

• Centrally mandated databases and IT

Diversification

• Few, if any, shared customers or suppliers

• Independent transactions

• Operationally unique business units

• Few data standards across business units

• Most IT decisions made within business units

Replication

• Few, if any, shared customers (per transaction)

• Independent transactions & BU Leaders

• Operationally similar business units

• Standardized data definitions but locally owned

• Centrally mandated IT services

Business Process Standardization

Low High

Hig

h

Lo

w

Bu

sin

ess P

roce

ss In

teg

rati

on

*Source: MIT


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Current State Assessment

Organization &

Readiness

Data Assets

Technology Assets

& Practices

Data Mgmt. Practices

Business Processes

Business

Goals and

Objectives

Enables

Informs

Enables

Enables

Measures

Delivers

Enables

Provides

Context

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Current State: Cultural Readiness


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Organizational Structures

• Understand roles, responsibilities, authority & accountability

– CIO and COO at the same level

– IT decisions managed at the C-Level

• Assess Skills Across Business, Data & Technology

– Lots of Cobol Programmers

– A few good data modelers

– Experienced program managers

– Serious application & technical architects

– A few NASA-level technical engineers

– Experienced (and supportive) SME’s

Current State: Organization

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Current State: Technology Assets

Global Booking Engine

Trade Processing

Settlement

Position Services

Sub-ledger

Products Accounts

Legal Entities

Traders

ReferenceData

Repo / Stock-

Borrow-Loan

StockRecord

Controller / Batch Processing

Gateway

MAINFRAME - dbTrader

32 Front Office Trading Systems (London)

Sysbase

Java

Confirms(Telex)

Electronic Trade

Allocations(Terminal)

Flat File (FTP)

Flat File (FTP)

Flat File (FTP)

Cash Mgmt. & Treasury

Oracle

PL/SQL

Flat File (FTP)

Mostly Client-Server applications A variety of technical platforms Mixture of homegrown & 3rd party

Reporting

Ops

Bloc

k Tr

ades

Settlement AgentsExchanges

CounterpartiesBanks

External Interfaces


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Current State: Business Process

– Run reports to see what and how many trades have been received

– Run reports for each sub-system to see what happen to the trades

– Find trade exceptions from looking at reports of ALL trades

Report-driven

Silo’ed

– Processes aligned to sub-systems

– Process integration points not understood or proactively managed

– Integration required extensive manual intervention & reconciliation

Constrained

– System would shut down at 100,000 trades

– Fragile & difficult to change because impacts can’t be anticipated

– Key-man dependencies defined the career path

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Current State: Data

Management Practices

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Data Management Maturity (DMM)℠ Model

Categories & Relationships

Data Governance

Data Management Strategy

Platform & Architecture

Data Operations

Data Quality

MetadataOversight

Business ITAlignment

Direction & Compliance

StakeholderAlignment

QualitySolutions

Data Infrastructure

Quality NeedsQuality Strategy

Business Process Data Requirements

Quality Rules/Criteria

Implementation Oversight:Collaboration

InfrastructureOversight

Supporting Processes – All Categories

On a scale of 1 to 5,

let’s just say they

were a ‘1’.


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Current State: Data Assets

• Transactional, Master, Reference and Accounting Data

• Structured and flat

• Typical of Legacy VSAM files

– Over-loaded fields were the norm

– Intelligence built into identifiers

– Non-atomic values

– Relationship tables widely used

• Attempted to maintain extensive technical metadata for change control

• 95% ROT (Redundant, Obsolete or Trivial)

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Data-driven Unification EA Model

Core

Business

Component

Core

Business

Component

Master

Data

Lifecycle

Mgmt.

Exception

Workflows

Decision

Support

Shared

Utilities

etc…

Application architecture for enabling functionality

Application architecture for core business functionality

Middleware

passing data

objects

Data Objects


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Data-Driven EA Design - Guiding Principles

• Straight-through processing

• Data correct the first time using VESt’ing (Validation, enrichment

and standardization)

• Exception-based workflows

• Loosely coupled architecture; develop functional systems linked

through a common data model

• Treat data exchanges as a data model

• Identify the key data elements

• Put controls on key data elements

• Create and use business-event metadata to create absolute

workflow transparency and control – end-to-end

• Identify system of Record and Authoritative Data sources

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The Results

By the Numbers:

• Reduced cost per trade by 54%

• Increased number of trades per operational staff by 37%

• Increased trading capacity by 2000%

• Improved ranking (from #11 to #2) in client trading performance

• Increased to over 1.5mn trades per day (from 500K per day)

New Capabilities:

• Work proactively

• Enable continuous improvement

• Transparency and control

• Business agility


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Outline

• Case Study 1: Deutsche Bank

– Background


– The Results

• Case Study 2: C.W. Bill Young Marrow Donor Program

– Background


– The Results

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NMDP

Guidelines

DNA Matching

Patient Advocacy

National Donor Registry

Setting Context

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Donor Center

Recruit Donors

Donor DNA Typing

Marrow Collection

Donor CRM

Transplant Center

Recipient Intake

Recipient Typing

Surgery / Outcomes


About Marrow Donor Centers

• 120+ Donor Centers in the U.S.

• 9+ million registered donors with NMDP

• Approx. 4,800 marrow donations annually (increasing

14% per year)

• Probability of an HLA (DNA) match is approx. 1 in

20,000 donors

• C.W. Bill Young Department of Defense Marrow Donor

Center (MDC) has 854,000 registered donors (adds

~75,000 donors every year)

• Donor Centers paid for each registered donor and

marrow donation

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Donor Centers Process

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Stem cell

harvest

Donor

engagement HLA match

Recipient

intake

Donor

registration

Donor

Recruitment

=Potential Donors

=Registered Donors


Data Valuation

• A unique valuation model – measure impact on

mission as well as business operations

• Donor Center Mission

– Provide high-quality HLA typings for a better match

– Highly efficient match request processing to meet

patient critical timing needs

– On-going donor engagement and education to

increase donor participation

• Donor Center Business Operations

– Increase revenue opportunities

– Cost takeout

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Organizational Needs

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Objectives Benefits / KPI’s

Productivity

Streamline & automate processes

• Increase # of stem cell collections per coordinator

• Increase #of DNA Samples typed per lab tech.

Transparency & Control

Create the capability to monitor,

measure, control & improve

processes in real time

• Reduce the number of missed SLA milestones

• Measure and improve process & staff performance

• Proactively & efficiently address workflow exceptions

• Full audit capability gives proof of FDA compliance

Quality of Services

Improve quality of services for

Donors, NMDP, Transplant

Centers & Recipients

• Min. process errors & max. reporting analytics

• Staff focused on quality of results by minimizing

manual steps

• Improved stakeholder experience

Donor Mgmt and Retention

Maximize participation and quality

of donors

• >99% accuracy for critical donor information

• Identify donors with highest probability of a match

• >75% donor participation rate


EA Solution

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Donor Center

Recruit Donors

Donor DNA Typing

Marrow Collectio

n

Donor CRM

Donor 360° View Data Services Layer Data Services Layer

Third Parties

Fedex, Labs, NMDP, BMDP

Donor Typing Lab

System

Donor Registry and CRM

Donor Center Workflow

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Results (1)

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0

50

100

150

Feb-08 Jul-09 Nov-10 Apr-12 Aug-13

Lag to Register Donor (in Days)

10

30

50

70

90

Apr-12 Jul-12 Oct-12 Jan-13 May-13 Aug-13

Number of Request Processed per Donor Coordinator

0

0.2

0.4

0.6


Percent of SLA's Hit for Donor Requests Processed

0

10

20

30


Average Days to Complete Donation Request

Mission Metrics

Business Metrics


Results (2)

• Business Operations Goals – Higher throughput with same resources = decreased cost per

request

• Impact: 100% increase in requests processed per coordinator =

50% reduction in cost to process a request

– More SLA payment targets hit

• Impact: 32% increase in revenue from SLA targets

• Mission Goals – Fewer days to register a donor with NMDP

• Impact: Increase in donor growth population (velocity);

greater chance of finding a match

– Donation requests processed more quickly

• Impact: Increase in actual marrow donations (still to be

measured)

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2/27/2016

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10124 W. Broad Street, Suite C

Glen Allen, Virginia 23060

804.521.4056

Outline - Buckeye DAMA · • Donor Centers paid for each registered donor and marrow donation 22 ....

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