teradata case

Professor Robert J. Sweeney, Wright State University

Robert J. Davis, Teradata, a division of NCR

Professor Mark Jeffery, Kellogg School of Management

Case StudyTeradata Data Mart Consolidation Return on Investment at GST

Professor Robert J.

Sweeney of Wright

State University and

Robert J. Davis of

Teradata, a division

of NCR prepared

this case study in

collaboration with

Professor Mark Jeffery

from Northwestern

University's

Kellogg School of

Management as

the basis for class

discussion rather

than to illustrate

effectiveness of

management. Some

facts within the case

have been altered

for reasons of

confidentiality.

OverviewRobert Davis had just finished a meeting with

Mark Johnson and Jeff Richards the CFO and

CIO of GST Inc. The telecommunications com-

pany was having a tough year with the stock

price down 35% and Johnson was looking for

ways to significantly reduce costs. Davis

worked for Teradata and Richards had request-

ed he come in to talk with the CFO about

streamlining their investment in technology.

Davis had suggested data mart consolidation

as a potential solution.

The idea of consolidating systems seemed like

an easy win, but Johnson was not impressed.

He wanted to see hard numbers “before he

invested a dime.” Richards was

not as skeptical but he was concerned about

the move to a non-standard infrastructure,

what he would do with the technical resources

potentially displaced by this

new system, user training, and related

organizational change issues.

Davis walked out of the GST corporate

headquarters towards his car. Johnson had

really harped on the need for a realistic ROI

analysis before he committed any upfront capi-

tal to the project. Davis needed his team

to put together an ROI analysis that would

clearly demonstrate how the Teradata

solution could help GST and impact their

bottom line. He wondered how much capital

would be required to fund the consolidation

and if Johnson and Richards could be

persuaded? He also wondered how best to quell

Richards’ concerns about organizational

change and moving to a Teradata architecture?

Fortunately, Johnson and Richards had provid-

ed a detailed breakdown of their costs for the

existing systems.

GST INC.Located in the southeast, GST operates

in the highly competitive telecommunications

industry. With 13 million customers in

11 states, 28,000 employees and annual sales

exceeding $5 billion for the most recent

year, GST was positioning itself to become an

industry leader through its commitment

to product innovation and personalized

customer service.

GST began in 1903 as Greater Southern

Telephone, the region’s third largest incumbent

local exchange carrier (ILEC). Over the years,

Greater Southern has changed its name to GST,

extended its reach as a competitive local

exchange carrier (CLEC), and now

provides a complete menu of state-of-the-art

telecommunications services to its ever-

expanding array of business and residential

customers; each customer has a unique

need for which GST has cultivated a unique

relationship. The service menu includes data

and voice transmission capabilities such as

broadband data services and Internet access

delivered over a digital network.

Case StudyTeradata Data Mart Consolidation Return onInvestment at GST

EB-3105 PAGE 2 OF 13

The telecommunications company was having atough year with the stock price down 35%.

As the business evolved technologically

and geographically, GST adopted a

decentralized model by region. The

corporate level leadership team includes

the President and CEO, the COO, and

fifteen vice presidents; seven are regional

VPs while the other eight include the Chief

Financial Officer, Chief Accounting Officer,

Chief Information Officer, Senior VP

for Investor Relations, VP for Human

Resources, VP for Marketing, VP for Industry

Relations, and the General Counsel. The high-

level corporate organization chart is provided

in Exhibit 1a.

The organization of each GST geographic

region includes a regional vice president serv-

ing as the CEO of the business unit, a regional

CFO, a regional CIO who also reports to the

corporate CIO, a CAO and several product

managers. Exhibit 1b represents the organiza-

tion chart for GST Region 4.

Mary Gros, CEO, had requested a set of income

statements reporting MIS expenses separate

from Cost of Goods Sold. She noted the

increase in IT expense each year, both in

dollars terms and as a percentage of revenue,

and charged Johnson with finding ways to

cut costs. Exhibit 2 contains the comparative

income statements for the past three years

for Region 4.

TERADATATeradata is a division of NCR Corporation, and

is a leading provider of enterprise data ware-

housing technology and solutions.

NCR has a storied history dating back

to its inception in 1884. In that year, John

H. Patterson purchased the National

Manufacturing Company, maker of the first

mechanical cash registers, and renamed it

National Cash Register Company.

Extending from mechanical cash registers,

NCR evolved into an innovative supplier

of advanced Point of Sale Solutions, the world-

wide leader in sales and shipment of

Automated Teller Machines (ATMs), and data

Case StudyROI for a Customer Relationship ManagementInitiative at GST


Jeff Shoemacher,CEO,

VP Region #4

Joe Castellano,Customer Relations

Michael Edwards,Data Services

Cathy Kempf,Internet Services

Paula Saunders,CLEC

Bud Baker,ILEC

Susan Lightle,CAO

Rebecca Koop,CIO

Fall Ainina,CFO

B. Organization Chart for GST Inc. Region #4

Mary GrosCEO

Tom Webster,COO

Karine Hatti,Human Resources

Nichole Knell,Industry Relations

Erica Kolks,Marketing

Cheik Daddah,Investor Relations

Barb Young,General Counsel

Daniel Wymer,CAO

Jeff Richards,CIO

Jean Secrist,VP Region #7

Raveen Rajavama,VP Region #6

Meghan McCormick,VP Region #5

Jeff Shoemacher,VP Region #4

Dominique Arnold,VP Region #3,

Jill Newburg,VP Region #2

Stacy Hoyle,VP Region #1

Mark Johnson,CFO

A. Organization Chart for GST Inc.

1b

1a

warehousing solutions. In 1974, the company

officially changed it name to NCR Corporation.

Today, NCR has a global reach with annual

revenues of $6 billion and approximately

32,000 employees.

In 1991, AT&T invested $7.4 Billion to

acquire NCR and effectively established the

unit as their computer systems division.

That same year, NCR purchased Teradata

Corporation for their advanced enterprise

data warehousing technology. NCR became

an independent company again in 1997 as

a result of the restructuring of AT&T into

three distinct companies: AT&T, Lucent

Technologies and NCR.

Teradata, founded in 1984, was based upon

the mission of providing high-performance

commercially viable data warehouse technology

and solutions. Data warehouse technology

enables large corporations to analyze and act

upon customer information previously locked

in isolated data silos. Exhibit 3 is a schematic

view of isolated data silos in a typical large

corporation such as GST.

The data warehouse systems con-

nect with customer mainframes

and operational systems to “siphon

off” pertinent detailed data from

silos into a large database, where

the data can be queried for effective

and timely analysis and action.

This integrated decision support

system is called an Enterprise-wide

Data Warehouse (EDW).

The primary elements of

Teradata’s value proposition are:

Proven Performance -

Customer References

Teradata customers include

many successful global compa-

nies such as: Wal-Mart, Bank of

America, 3M, SBC, Delta Airlines, Whirlpool,

Belgacom, Harrah’s Entertainment, Royal

Bank of Canada, Procter & Gamble, AT&T,

Travelocity, and Merck Medco.



Comparative Income Statements GST Inc.—Region #4

2

Enterprise

Margins

Growth

ProfitsInventory

Resources

Partners

Payment

Growth

QualityDelivery

Availability

Customers

Purchase History

Attitudes

Demographics

Behaviors

Preferences

Competitors

Channels

Marketing

Products & Services.com's

New Entrants

Multiple Views and Silos of Data in a Large Corporation such as GST Inc.

3

2001 2000 1999

Revenue* $319,904 $280,289 $252,437

Costs and expenses, excluding MIS and depreciation $107,406 $106,539 $108,037

MIS 95,971 75,678 58,536

Depreciation and amortization 55,824 45,605 39,832

Operating Income (Loss) $60,703 $52,467 $46,032

Interest and dividend income 3,733 2,524 2,973

Interest expense (21,790) (15,939) (13,417)

Other income, net 698 531 326

Income (loss) before income taxes $43,344 $39,583 $32,914

Provision (benefit) for income taxes 20,911 18,833 15,333

Net income (loss) $22,433 $20,750 $17,581

* All numbers are in units of thousands.

Scalability

Scalability is the ability to support more

users over time. For an EDW, scalability has

multiple dimensions: hardware, support

of user connectivity, and from a database

perspective the ability to support ever

increasing expectations for complex as well

as ad hoc query performance. Demands

on a data warehouse increase exponentially

as data and user volumes grow, update

frequencies increase, and the operational

feeder systems multiply. The Teradata

solution has demonstrated scalability.

Support for High User Concurrency

One of the sure signs of a successful data

warehouse is when more and more business

users want access to it. In some environments,

this demand presents a dilemma: Do you

accept all users and suffer performance

degradation that leads to diminished ware-

house effectiveness and user attrition? Or do

you restrict data warehouse access to a limit-

ed number of users, resulting in sufficient

warehouse performance but reduced overall

business value? The Teradata solution uses

massively parallel processing so that many

users can access the system simultaneously

without loss of performance.

BACKGROUND ON DATA WAREHOUSE TECHNOLOGY

A data warehouse is not a product but rather

a process. Data warehouses are environments

that allow business users to transform vast

amounts of data into useful information

efficiently and accurately, enabling companies

to “get to know the customer.”

A schematic diagram of a typical data ware-

house is shown in Exhibit 4a. The typical flow

of data to information is as follows: operational

data is generated through customer transac-

tions. Data is then transformed into a consis-

tent format into storage for later use. The

appropriate information is extracted and

imported for summarization. The summariza-

tion might involve comparing sales across

time, across products, and by profit margins.

Similarly, data can be summarized by cus-

tomer, across time, and across products by

profit margin. Finally, the summarized data is

presented as information for use in future

business decisions.

The storage component of the data flow is

the subject of data warehousing. In most

decentralized business environments,

data warehouses have been considered

too costly and as a result, data marts

have proliferated. Data marts are smaller

repositories of information that are for a

specific business unit or process. Exhibit 4b

is a schematic of a company similar to

GST that does not have a centralized data

warehouse, but instead has a series of

isolated data marts.

As independent systems, data marts are

often considered less expensive to operate.

This is only true if one ignores many of the

hidden costs associated with data marts. In a

2001 Gartner report, it was determined that

data marts were 70% more expensive to

operative per subject area than a comparable

data warehouse.

Data marts are usually constructed for an

individual user/business unit because of the



IT Users

Operational,Data

Data Transformation

Enterprise,Warehouse &,Management

Data Mart and Data Warehouse Architectures

Business Users

Data,Marts

Business users accessing disparate data marts

Data Transformation

Schematic of a typical data warehouse architecture

4aAlex Payne, Marketing Specialist, Teradata, a division of NCR and Chiek Daddah, Senior Business Analyst, Teradata, a division of NCR

difficulty of obtaining data consensus across the

organization. Data marts often become isolated

data silos. This is primarily because business

users tend to want to tinker with the system and

customize it to their specific business division

needs. As the number of users (tinkerers) grows,

the effectiveness of the mart deteriorates.

Different users with differing information needs

might customize the mart to their unique needs.

This customization makes it virtually impossible

to share information across the organization.

Finally, changing the data mart is often slow –

programmers often wait until a large number

of changes are received before they alter the

data mart code.

The data warehouse architecture Exhibit 4a is an

improvement over the data mart environment

Exhibit 4b because it allows business users across

the organization access to a single set of data. The

data warehouse is more readily adaptable to

change as user needs change, and is generally free

from the tinkering that tends to be endemic to

data marts. Furthermore, data warehouses are

cost effective because they eliminate redundancy

in staffing as well as information.

Data integration is essential to the development

of a single view of the enterprise. However,

even with integrated data, companies achieve

maximum success if the integrated data is

available to all business units in a useful form

that is both cost-effective and accurate.

Enterprise data warehouses can be seen as an

important step in this direction.




Data,Sources

Information,Users

Hybrid data mart/data warehouse architecture

Warehouse

Type 1,Data Mart

Type 2,Data Mart

Type 3,Data Mart

External ,Data

ODS

Independent,Data Source

4c

IT Users

Operational,Data


Business Users

Data,Marts

Business users accessing disparate data marts

Data Transformation

Architecture comprising of isolated data marts and no centralized data warehouse.

4bAlex Payne, Marketing Specialist, Teradata, a division of NCR and Chiek Daddah, Senior Business Analyst, Teradata, a division of NCR

Alex Payne, Marketing Specialist, Teradata, a division of NCR and Chiek Daddah, Senior Business Analyst, Teradata, a division of NCR

Like most companies, GST organizes data by

function: customer data, partner data, com-

petitor data, and finally enterprise data. A

schematic of this configuration is given in

Exhibit 3. Partitioning data along these lines

obscures many business

relationships that could be more cost

effective and more profitable.

BACKGROUND ON DATA MART SYSTEMS

The typical data mart environment usually

includes independent data marts, dependent

data marts and/or hybrid data marts. In an

independent mart (Exhibit 4b), transactional

data is collected, transformed and then

stored in data marts. These data are then

shared with the business users. Eliminating

data redundancy, guaranteeing data

synchronization and capturing data latency

are difficult to achieve let alone manage

in a data mart environment.

Dependent data marts (Exhibit 4a) receive

data from a data warehouse before the data is

shared with the business users. Transactional

data is again collected and transformed and

the information is stored in a data warehouse.

From here the information flows to data marts.

Similar to an independent data mart environ-

ment, redundancy, synchronization and latency

are problems in a dependent environment.

The third environment is the hybrid data mart

system, shown schematically in Exhibit 4c.

Hybrid systems incorporate features of

both independent and dependent data mart

environments. In addition, the hybrid

environment incorporates the data problems

associated with data marts.

Data marts operated separate from the

business users can create data management

problems down stream. For example,

business users obtaining data will create

internal systems to consolidate the data and

to analyze the data. A simple change in the

way the data is reported from the mart, say for

example, from weekly information to daily

information will obviously alter the way the

data is interpreted. Unless the business users

are vigilant about keeping pace with the changes,

decisions could be made using faulty data.

ENTERPRISE DATA WARE-HOUSE ARCHITECTURE

The architecture for an enterprise data

warehouse (EDW) is shown schematically

in Exhibit 4d. The Teradata EDW database

incorporates massive parallel processing

(MPP) to process many user queries

simultaneously. The database at the core

of the Teradata EDW system has much higher

performance than competitors such as IBM or

Oracle, and this high performance means that

individual data marts can be eliminated.

With the new architecture, shown in Exhibit

4d, all data is housed in a single place giving

business users access to a single view of the

enterprise and more specifically, the customer.

Data synchronization is assured since any

changes in the way the data is collected at

the transactional level flows directly and

immediately to the business users.

Unlocking the information content of the data

(data latency) is facilitated since the data is

accessible at a more granular level. Finally, data

redundancy is eliminated since the business

users have access to a single source for data.



IT Users

Operational,Data


Business Users

Data Transformation

Architecture of an Enterprise Data Warehouse (EDW). The system consolidates all data marts into a single enterprise-wide database. Users then query the database directly,instead of querying disparate data marts.

Enterprise,Warehouse &,Management

4dAlex Payne, Marketing Specialist, Teradata, a division of NCR and Chiek Daddah, Senior Business Analyst, Teradata, a division of NCR

2 Note that marketing research studies may provide additional insights into what constitutes a reasonable percentage increase in value.

3 This is where some thought will have to be given as to what marketing actions will be taken.

Potential costs that are either eliminated or

reduced from Exhibit 4b include administration

costs, systems maintenance costs, data movement

costs and data synchronization costs. Simply stated,

data redundancy leads to staff redundancy, and

eliminating disparate data marts can reduce

the staff count.

The actual Teradata system configuration is

shown schematically in Exhibit 5a. The bottom

cabinets in the exhibit represent disk arrays.

The disk array can be comprised of either 18GB

drives (1.4 terabytes of data) or 36 GB drives

(2.8 terabytes of data.): Disk arrays can be clustered

together to support 100s of terabytes of data.

The middle section of Exhibit 5a contains node

cabinets. Each cabinet has two nodes comprised

of 4-Intel processors. In addition, nodes are

interconnected via Teradata’s BYNET. The processing

cabinets are designed for resiliency with uninter-

rupted power supply units in each cabinet. Up to

256 cabinets (equaling 512 nodes) can be configured

as a single massively parallel processing (MPP)

system. As of January 2002, a total of 2,048 Intel CPUs

could be configured in a complete Teradata EDW.

The top portion of Exhibit 5a presents the adminis-

tration work station (AWS). This is a standalone

UNIX or Windows based workstation that is the

primary operations interface for MPP systems.

The AWS provides a single, graphical view of the

system. Not shown are the thousands of end users

with access to the system. Finally, the dotted line

containing three cabinets (or six nodes) is the

footprint for the proposed GST pilot program.

Exhibit 5b demonstrates the proprietary competitive

advantage of the Teradata EDW architecture. Up to

512 nodes, each node contains four CPU’s, can be



The Teradata BYNET

The BYNET is a high-speed interconnect that is optimized for parallel processing with the Teradata Relational Database Management System. More specifically, two BYNETs are configured with every Teradata MPP (Massively Parallel Processing) System for redundancy, high performance and scalability. These BYNETs are uniquely designed to provide simultaneous, bi-directional traffic (messages) between the :¥ Processing Nodes¥ Parsing Engine (PE-checks the SQL statement, access rights and invokes action),¥ and the Access Module Processors (AMPs) for effective data retrieval and disk management.The BYNET is the key design feature that enables support for many concurrent users and maximum system throughput.

BYNET

NODE,(4) CPUs in a Node,Up to 512 Nodes

Cliques,Grouping of 4 Nodes,

Redundancy in case of,Node failure

RAID,Redundant Array of Indepent Disks,

Terabytes of Data

5b

The Teradata Enterprise Data Warehouse (EDW) Physical Architecture

SMC

SMC

BYNET

UPS

UPS

UPS

BYNET

SMP

SMP

SMC

BYNET

UPS

UPS

UPS

BYNET

SMP

Disk Array,(40 Disks)


SMP

SMC

SMC

BYNET

UPS

UPS

UPS

BYNET

SMP



SMP

SMC

BYNET

UPS

UPS

UPS

BYNET

SMP

SMP

AWS

Height–77",Width–22",per Disk Array

Up to ,256,Cabinets,,Up to,2,048,Intel CPUs

(2) SMP Nodes,per cabinet,,(4) Intel CPUs,per Node

Up to,100's,Terabytes

Disk ,Options,,18GB Drives,(1.4TB),,or,,36GB Drives,(2.8TB)

Pilot Footprint

The Teradata EDW architecture consists of 2–512 processing nodes (each node consists of four high performing Intel based CPUs—this iscalled a symmetric multi processor (SMP) node with disk scalability up to 100s of terabytes via highly available, hot-pluggable, Redundant Array of Independent Disks (RAID) for data storage. Nodes can be aded in pairs to map to the processing requirements of each configuration.Disk options exist with Teradata sourcing RAID configurations from EMC2 and LSI Logic. The GST data mart consolidation pilot system would be approximately 20% of the complete EDW, and is shown schematically in the dashed box.

5a

connect across the message passing layer – this

layer is also known as the system bus or as the

BYNET. Exhibit 5b also shows nodes sharing a

common set of disk arrays grouped into what are

known as cliques. The clique grouping provides

for data redundancy in case of node failure.

DATA MART CONSOLIDATIONPROJECT

GST is operating fifty disparate data marts. The

manufacturers of the data marts include Oracle,

IBM, Informix, and Sybase. Davis suggested the

consolidation of the data marts into an enter-

prise data warehouse (EDW) for two reasons.

First, the EDW is more efficient to operate

thereby reducing the amount of money spent

on information management. Second, the EDW

will provide access to “better” data. Although

cost savings associated with the consolidation

are more readily quantified, the value of the

“better” data is more difficult to quantify.

Rather than proceeding with a wholesale

consolidation of all existing data marts, Davis

proposed a pilot study: consolidate a subset

of the existing data marts to evaluate if the

benefits are obtained. Five fully depreciated

data marts have been identified as candidates

for consolidation: four Oracle 8i systems and

one IBM DB2 system.

To pitch his idea for data mart consolidation,

Davis created Exhibit 6a – an organization

chart for Region 4 in the current data mart

environment. The exhibit shows how the

organization sits “on top” of the data marts.

Each system has its own channel to acquire

data, clean the data and store the data.

Davis pointed out that with just the three

systems and four access points represented,

there are 14 redundant processes. For example,

the “Acquire” step is the bridge between an

access point (customer or supplier) and the

firm. In Exhibit 6a, the “Acquire” step in System

C is completely redundant. That is, the four

access points have been completely sampled

by systems A and B by the time System C is

building its database.

Company wide, with 50 disparate data marts,

GST had a massive amount of redundancy.

This redundancy was expensive, unnecessary

and could be eliminated through data mart

consolidation potentially saving millions

in IT expenses. In addition to the expense

of redundant systems, there are expenses asso-

ciated with the loss of accuracy from any

inconsistencies in the way the data is stored

and reported across the systems. A centralized

data warehouse eliminates these expenses as well.

To support his position, Davis also created

a revised organization chart for the same

region in a data warehouse environment -

Exhibit 6b. In Exhibit 6b, the data sits on top

of the organization giving everyone immediate




VP Region #4




Paula Saunders,CLEC

Bud Baker,ILEC

Susan Lightle,CAO

Rebecca Koop,CIO

Fall Ainina,CFO

A. Region 4 in the Data Mart Environment

Acquire Acquire Acquire Acquire Acquire Acquire Acquire AcquireAcquire

CleanClean Clean Clean Clean Clean Clean CleanClean

Store Store Store

Select Select Select

Summarize Summarize Summarize

Present Present Present

System A System B System C


VP Region #4




Paula Saunders,CLEC

Bud Baker,ILEC

Susan Lightle,CAO

Rebecca Koop,CIO

Fall Ainina,CFO

B. Region 4 in the Data Warehouse Environment

Acquire Acquire Acquire

Clean Clean

Store

Clean

System A System B System C

6a

6b

Data Warehousing & Data Marts Terminology Simplified by Doug Ebel, Teradata, a division of NCR

access to the same data – this

structure is both less costly and

more consistent. The improvements

in efficiency and consistency are

value-added by the data mart

consolidation.

COSTS OF THE GST DATA MART ENVIRONMENT

Susan Lightle, CAO of Region 4 was

asked to identify the costs associated

with the data marts. She offered

the following information – Each

Oracle data mart requires one

system administrator, two data base

analysts, two ETL programmers,

three query programmers, one

network administrator, and two

people working as support staff. In

addition, non-personnel support

costs for each Oracle system was

approximately $1,000,000 for the

next year. This did not include

$80,000 per year per mart for

maintenance and upgrades.

An IBM data mart required one

system administrator, three data

base analysts, two ETL programmers,

three query programmers, one network

administrator, and two people working as

support staff. Non-personnel support costs

for the IBM system was $1,800,000 per year.

Maintenance and upgrades for the IBM mart

total $110,000 per year.

Lightle gave Davis GST employee salary and

benefits information, see Exhibit 7. She also

gave Davis a summary breakdown of the

number and type of GST employees required for

each Oracle and IBM data mart, see Exhibit 8.

COSTS FOR THE TERADATA SOLUTION

The staffing requirement for the Teradata

system depends, in part, on how GST

management decides to handle the personnel

reductions. The most likely scenario for

staffing the proposed enterprise data

warehouse is one system administrator, eight

data base analysts, four ETL programmers,

ten query programmers, and three individuals

serving as support staff. Exhibit 8 also

summarizes the best, worst, and expected

case scenarios for staffing the new Teradata

system. The exact probabilities for the

GST staffing changes were not known,


EB-3105 PAGE 10 OF 13

GST Average Annual Salary Data

GST System Staffing Requirements, Maintenance, and Support Costs

System Administrator $130,000

Data Base Analyst $110,000

ETL Programmer $80,000

Query Programmer $70,000

Network Administrator $80,000

Support Staff $40,000

Benefits 40% of salary

Expected Inflation Rate: Salary and Benefits 4%

GST Individual Data Marts Teradata EDW

Staff / System Oracle 8I IBM DB2 Best Case Most Likely Worst Case

System Administrator 1 1 1 1 1

Data Base Analyst 2 3 6 8 9

ETL Programmer 2 2 3 4 8

Query Programmer 3 3 8 10 15

Network Administrator 1 1 0 0 0

Support Staff 2 2 2 3 4

Maintenance per node $80,000/yr $110,000/yr 10% of HW and software list price per yr

Non-personnel support costs

$1,000,000/yr $1,800,000/yr $125,000/month after the data martsare decommissioned

7

8

however the GST team urged Bob to use

20%-60%-20% as the probabilities for the

staffing scenarios best case, most likely

case and worst cases, respectively.

The list prices associated with the acquisition

of the data warehouse are included in Exhibit

9. The consolidation of the five data marts will

require five nodes. Although the first four

nodes are sold as individual units, nodes

beyond the fourth are only sold in pairs. The

prices quoted in Exhibit 9 are per node. The

proposed system (nodes, software, and disks)

would be depreciated using the MACRS 5-year

class life schedule assuming the mid-year

convention. The total cost for disks is estimated

as $650,000. Maintenance/upgrades for the

nodes and software is 10% of the list price.

Finally, the first year non-personnel support

costs for the Teradata warehouse, once the

system is operational, is projected to be

$1,500,000 (paid in monthly installments.)

On behalf of Teradata, Davis can offer an

installed price for nodes and software at 30%

off the list price. In addition, Teradata is willing

to provide a $400,000 equipment credit against

the purchase price if GST commits to the

consolidation pilot study. However, the disks

for the data storage would not be eligible for

the 30% discount.

Professional services costs (business

consulting) for the three years of the pilot

study are quoted at $125,000 per month

once the implementation project is complete.

Exhibit 11 gives the detailed break down of the

professional service costs during the estimated

12-month implementation schedule.

Consulting costs decline dramatically after

the first year because GST was being urged to

purchase the hardware and re-architect the

data structure at the beginning of the process,

which front-loads the consulting fees. This was

an alternative to acquiring a node, migrating

the data, and re-architecting the system

sequentially. Davis was convinced the

former was in the best long-term interest

of GST. Davis was also encouraging GST

to engage Teradata’s team of consultants

to commence work on the development

of a logical data model to address a

holistic look at the information require-

ments of the total enterprise (including the

requirements associated with the remaining

45 data marts). In addition, Davis was

suggesting GST begin work on the development

of customer relationship management programs

that would be possible with the more complete

view of the customer. The professional services

costs in years 2 and 3 were associated with

the design of the data warehouse under a full-

consolidation EDW scenario and for the

development of CRM programs.

Training costs, separate from business

consulting, would be $15,000 per month for

the first two years – see Exhibit 11 for the start

date of the training. Some of these costs were

related to training the existing employees on

the new system as well as training dislocated

existing employees for other internal positions.

Training would commence once the data marts

are loaded into the warehouse. For this ROI

analysis, the training costs would be expensed

as incurred.


EB-3105 PAGE 11 OF 13

Teradata Cost Sheet

Hardware and Software

Item 1st Node 2nd Node 3rd Node 4th Node 5th Node

Hardware $175,000 $225,000 $200,000 $200,000 $720,000

Software $90,000 $190,000 $190,000 $190,000 $500,000

Training and Professional Services Costs for the Teradata Solution

Expense Year 1 Year 2 Year 3

Training See Exhibit 11: $15,000 per month -$0-$15,000 per month

starting in May

Consulting See Exhibit 11: $125,000 per month $125,000 per month$125,000 per month after implementation

Data Storage Disk Costs

$650,000 (For 2.8TBytes of data)

Adapted from Steven Weber, Pricing Director, Teradata, a division of NCR

9

Summary GST financial assumptions

Required return for project investments 14%

Corporate Tax Rate 38%

Inflation Rate: Non-personnel costs 5%

Inflation Rate: Personnel costs 4%

10

IMPLEMENTATION PROJECT

Exhibit 11 is a high-level schematic

of the proposed data mart consolida-

tion implementation project. Phase 1

– data capture and planning should

take approximately 2 weeks. Although

much of this work is done as part of

the proposal, many details of the

existing system must be understood

prior to data migration. Phase 2 –

moving data to the Teradata system

will involve between 3 and 4 weeks

per data mart (15 to 20 weeks for 5

data marts.) This represents the

physical migration of the data, tables,

and processes highlighted in Phase 1.

After the fifth data mart had been

migrated, all the original data and

many applications would be again

available to the end-users and the

data marts could be retired. However,

once all the data and applications

were copied to the warehouse, GST

required a 6-week test and validation

process be conducted to guarantee

that, from the user’s perspective,

the warehouse was identical to the

original data mart. Bob believed the

first test phase would be complete,

and the data marts could be retired,

as soon as May 1 or it could take as

long as September 1. However, it was most likely

that the data marts will be decommissioned

on July 1.

Phase 3 – model design, re-architect model,

and update will take 3 to 4 months. Although

the end-users have access to the data

and tables, it was during Phase 3 that the

enterprise re-architecture will eliminate the

redundant systems producing significant

performance improvements. Testing

represents the final phase, Phase 4, before

the warehouse was fully operational.

Bob was rather certain that Phases 3

and 4 will take a total of six months

to complete.

The complete transition from data marts to

an enterprise data warehouse was expected

to take twelve months to achieve. Phases 1

and 2 could be accomplished more efficiently

or take longer that expected. In total, the

transition could take as few as ten months

or as long as 14 months. For each month the

project goes over or under the 12 month


EB-3105 PAGE 12 OF 13

Data Mart Consolidation Project Budgeted Cost of Work of Schedule

Jan Feb MarExpenses

Professional,Services

Training

Non-personnel,Support

Apr May Jun Jul Aug Sep Oct Nov Dec

All dollar amounts are in thousands.,Professional services costs include: Data capture and planning data migration, scope of complete CEDW–consolidating the remaining 45 data marts, and scope of future CRM applicaions.CTraining costs include: Training existing employees on Teradata system, and training dislocated Cemployees on other internal systems.CNon-personnel support costs include: Travel, subscriptions, overhead allocation, etc.CC

$220 $255 $270 $290 $290 $290 $270 $270 $270 $270 $270 $270

$15 $15 $15 $15 $15 $15

$125 $125 $125 $125 $125 $125

Data Mart Consolidation Project Baseline

1st Quarter 2nd Quarter 3rd Quarter 4th Quarter 1st Quarter

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb

3 wks

6 wks

4 wks

4 wks

4 wks

6 wks

16 wks

8 wks

Data Capture and Planning

Migrate Datamart 2

Migrate Datamart 3

Migrate Datamart 4

Migrate Datamart 5

Datamart Testing

Engineer EDW

Test EDW

Phase 1: Data Capture and Planning,•Understand the data structureC in each martC•Identify ETL processesC•Specify amount and frequency of C updatesC•Scope amount of data

Phase 2: Data Migration,•Forklift data from marts intoC data warehouseC•Transfer scripts, C Programs C and PL/SQLC•Migrate 3rd party Applications C•Test data marts

Phase 3 & 4: Enterprise Data ,Warehouse Architecture Pilot Study,•Develop logical modelC•Testing

11Source: Alex Payne, Marketing Specialist, Teradata, division of NCR and Cheik Daddah, Senior Business Analyst, Teradata, a division of NCR

© 2002 by Mark Jeffery. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any for by means -electronic, mechanical, photocopying, or otherwise - without the permission of Mark Jeffery. Teradata is a registered trademark and WorldMark is a trademark ofNCR Corporation. All other brand and product names appearing in this release are registered trademarks or trademarks of their respective holders. NCR continuallyimproves products as new technologies and components become available. NCR therefore, reserves the right to change specifications without prior notice. All fea-tures, functions and operations described herein may not be marketed in all parts of the world. Consult your NCR representative for further information.

© 2002 NCR Corporation Dayton, OH U.S.A. Produced in U.S.A. All rights reserved.

www.teradata.com www.kellogg.nwu.edu

base-line the professional service implemen-

tation cost would increase or decrease by

approximately $270,000. Davis had experi-

enced 9 similar data mart consolidation

projects. Of these, 2 had come in under time

at 10 months, 3 had taken 12 months, and 4

projects had run over to the full 14 months.

The existing data marts and the enterprise

data warehouse would be operated simulta-

neously until the fifth data mart has been

successfully moved. Hence, following the

base-line plan, by early June the original

data marts could be decommissioned.

However, GST required the data marts would

continue to operate until July 1 during the

data mart test phase (see Exhibit 11) to

ensure the data and application validation

were completed.

ADDITIONAL DATA

GST used a weighted average cost of

capital (WACC) of 14%, had a tax rate

of 38%, expected an inflation rate for non-

personnel support costs of 5% annually, and

expects salaries to increase 4% per year

across-the-board. In addition, GST was

considering retaining one Oracle mart for

an internal training program. These data

are summarized in Exhibit 10.

Davis was in contact with Johnson, and they

concurred that the analysis of the pilot study

should be conducted utilizing a three-year

investment horizon. The three-year horizon

begins with the start of Phase 1 and runs for

36 months. Phase 1 would commence on the

first day of January 2002.

BUSINESS IMPACT MODELING TEAM

Davis planned to give this ROI problem to the

Business Impact Modeling Group at Teradata.

He wanted to make sure they would be thorough

enough to calculate best, worse, and a most-

likely case for the project ROI, and be realistic

in their numbers. As members of the team,

help Davis make a recommendation to GST.

ANALYSIS

Following are some questions to consider with

your analysis:

• What is the project ROI and the pay

back period?

• Of the best, worst, and expected case

which should you present to GST?

• How much upfront capital is needed

for this project, and what financing

options would you recommend?

• How would you recommend dealing

with Richards personnel concerns?

• If you were Johnson and Richards,

would you move forward with the

consolidation project?


EB-3105 PAGE 13 OF 13

Davis wanted to make sure his team calculatedbest, worst, and most-likely cases for the projectROI, and were realistic in their numbers.

teradata case

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