teradata case
TRANSCRIPT
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
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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.
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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
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EB-3105 PAGE 5 OF 13
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.
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EB-3105 PAGE 6 OF 13
Data Mart and Data Warehouse Architectures
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
Data Mart and Data Warehouse Architectures
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.
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EB-3105 PAGE 7 OF 13
IT Users
Operational,Data
Data Mart and Data Warehouse Architectures
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
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EB-3105 PAGE 8 OF 13
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)
Disk Array,(40 Disks)
SMP
SMC
SMC
BYNET
UPS
UPS
UPS
BYNET
SMP
Disk Array,(40 Disks)
Disk Array,(40 Disks)
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
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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
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
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. 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,
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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.
Case StudyROI for a Customer Relationship ManagementInitiative at GST
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
Case StudyROI for a Customer Relationship ManagementInitiative at GST
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?
Case StudyROI for a Customer Relationship ManagementInitiative at GST
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.