Teradata Case

Case StudyTeradata Data Mart Consolidation Return on Investment at GST Professor Robert J. Sweeney, Wright State University Robert J. Davis, Teradata, a division of NCR Professor Mark Jeffery, Kellogg School of Management With 13 million customers in 11 states.Case Study Teradata Data Mart Consolidation Return on Investment at GST The telecommunications company was having a tough year with the stock price down 35%. Located in the southeast. Greater Southern has changed its name to GST. Some facts within the case have been altered for reasons of confidentiality. Sweeney of Wright State University and Robert J. 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. persuaded? He also wondered how best to quell Richards’ concerns about organizational change and moving to a Teradata architecture? Fortunately. The service menu includes data and voice transmission capabilities such as broadband data services and Internet access delivered over a digital network. He wondered how much capital would be required to fund the consolidation and if Johnson and Richards could be Overview Robert Davis had just finished a meeting with Mark Johnson and Jeff Richards the CFO and CIO of GST Inc. Over the years. He wanted to see hard numbers “before he invested a dime. GST was positioning itself to become an industry leader through its commitment to product innovation and personalized customer service. Johnson had really harped on the need for a realistic ROI analysis before he committed any upfront capital to the project. but Johnson was not impressed. GST began in 1903 as Greater Southern Telephone.000 employees and annual sales exceeding $5 billion for the most recent year. 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. Davis worked for Teradata and Richards had requested he come in to talk with the CFO about streamlining their investment in technology. Johnson and Richards had provided a detailed breakdown of their costs for the existing systems. Professor Robert J. each customer has a unique need for which GST has cultivated a unique relationship. and now provides a complete menu of state-of-the-art telecommunications services to its everexpanding array of business and residential customers. EB-3105 PAGE 2 OF 13 . the region’s third largest incumbent local exchange carrier (ILEC). user training. and related organizational change issues. GST operates in the highly competitive telecommunications industry. extended its reach as a competitive local exchange carrier (CLEC). The idea of consolidating systems seemed like an easy win. what he would do with the technical resources potentially displaced by this new system. The telecommunications company was having a tough year with the stock price down 35% and Johnson was looking for ways to significantly reduce costs. Davis had suggested data mart consolidation as a potential solution. Davis walked out of the GST corporate headquarters towards his car. Davis of Teradata.” Richards was not as skeptical but he was concerned about the move to a non-standard infrastructure. 28. GST INC. and is a leading provider of enterprise data warehousing technology and solutions. CEO. and the General Counsel. She noted the increase in IT expense each year. seven are regional VPs while the other eight include the Chief Financial Officer. NCR has a storied history dating back to its inception in 1884. Chief Information Officer. VP for Human Resources. both in dollars terms and as a percentage of revenue. a CAO and several product managers. a regional CFO. The highlevel corporate organization chart is provided in Exhibit 1a. Mary Gros. the worldwide leader in sales and shipment of Automated Teller Machines (ATMs). a regional CIO who also reports to the corporate CIO. In that year. Region #4 Jeff Shoemacher CEO VP Region #4 Fall Ainina CFO Rebecca Koop CIO Susan Lightle CAO Bud Baker ILEC Paula Saunders CLEC Cathy Kempf Internet Services Michael Edwards Data Services Joe Castellano Customer Relations 1b As the business evolved technologically and geographically. had requested a set of income statements reporting MIS expenses separate from Cost of Goods Sold. GST adopted a decentralized model by region. and data EB-3105 PAGE 3 OF 13 . Organization Chart for GST Inc. and fifteen vice presidents. Extending from mechanical cash registers. maker of the first mechanical cash registers. and renamed it National Cash Register Company. NCR evolved into an innovative supplier of advanced Point of Sale Solutions. Exhibit 1b represents the organization chart for GST Region 4. The organization of each GST geographic region includes a regional vice president serving as the CEO of the business unit. and charged Johnson with finding ways to cut costs. Chief Accounting Officer. Organization Chart for GST Inc. The corporate level leadership team includes the President and CEO. Patterson purchased the National Manufacturing Company. Senior VP for Investor Relations. the COO.Case Study ROI for a Customer Relationship Management Initiative at GST A. Mary Gros CEO Tom Webster COO Mark Johnson CFO Jeff Richards CIO Daniel Wymer CAO Barb Young General Counsel Cheik Daddah Investor Relations Erica Kolks Marketing Nichole Knell Industry Relations Karine Hatti Human Resources Stacy Hoyle VP Region #1 Jill Newburg VP Region #2 Dominique Arnold VP Region #3 Jeff Shoemacher VP Region #4 Meghan McCormick VP Region #5 Raveen Rajavama VP Region #6 Jean Secrist VP Region #7 1a B. VP for Industry Relations. Exhibit 2 contains the comparative income statements for the past three years for Region 4. John H. TERADATA Teradata is a division of NCR Corporation. VP for Marketing. 524 (15. corporation such as GST.605 $52. Lucent Technologies and NCR.333 2000 $280.417) 326 $32.790) 698 $43. Data warehouse technology enables large corporations to analyze and act Proven Performance Customer References $22. in isolated data silos.539 75. was based upon the mission of providing high-performance commercially viable data warehouse technology and solutions.911 warehousing solutions. In 1991.939) 531 $39.037 58. excluding MIS and depreciation MIS Depreciation and amortization Operating Income (Loss) Interest and dividend income Interest expense Other income.Case Study ROI for a Customer Relationship Management Initiative at GST The data warehouse systems conComparative Income Statements GST Inc. 3M.914 15. The primary elements of Teradata’s value proposition are: $319. Royal view of isolated data silos in a typical large Bank of Canada. NCR has a global reach with annual revenues of $6 billion and approximately 32.733 (21. founded in 1984.581 2 Teradata customers include many successful global companies such as: Wal-Mart.344 20.583 18. Harrah’s Entertainment. SBC.—Region #4 2001 Revenue* Costs and expenses. Travelocity. Delta Airlines. Today. NCR became an independent company again in 1997 as a result of the restructuring of AT&T into three distinct companies: AT&T.4 Billion to acquire NCR and effectively established the unit as their computer systems division.703 3. NCR purchased Teradata Corporation for their advanced enterprise data warehousing technology.289 $106. AT&T.832 $46.000 employees.433 $20. and Merck Medco. Multiple Views and Silos of Data in a Large Corporation such as GST Inc. Exhibit 3 is a schematic Belgacom. where the data can be queried for effective and timely analysis and action. Teradata. Behaviors Purchase History Margins Resources Enterprise Profits Inventory Growth Customers Demographics Preferences Payment Attitudes Availability Channels New Entrants Partners Quality Delivery Growth Competitors . That same year.536 39. net Income (loss) before income taxes Provision (benefit) for income taxes Net income (loss) * All numbers are in units of thousands. Bank of upon customer information previously locked America.750 $17.971 55. the company officially changed it name to NCR Corporation.com's Products & Services Marketing 3 EB-3105 PAGE 4 OF 13 . nect with customer mainframes 1999 $252.678 45.032 2.833 and operational systems to “siphon off” pertinent detailed data from silos into a large database. Whirlpool. AT&T invested $7.467 2.973 (13. In 1974.406 95. This integrated decision support system is called an Enterprise-wide Data Warehouse (EDW).437 $108.824 $60.904 $107. Procter & Gamble. Senior Business Analyst. Marketing Specialist. The typical flow of data to information is as follows: operational data is generated through customer transactions. Finally. data can be summarized by customer. The storage component of the data flow is the subject of data warehousing. As independent systems. The summarization might involve comparing sales across time.Case Study ROI for a Customer Relationship Management Initiative at GST Scalability Scalability is the ability to support more users over time. 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.” Data Transformation Data Marts Business users accessing disparate data marts Business Users Schematic of a typical data warehouse architecture 4a Alex Payne. a division of NCR and Chiek Daddah. Teradata. 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. Data marts are smaller repositories of information that are for a specific business unit or process. This is only true if one ignores many of the hidden costs associated with data marts. and from a database perspective the ability to support ever increasing expectations for complex as well as ad hoc query performance. across products. In most decentralized business environments. this demand presents a dilemma: Do you accept all users and suffer performance degradation that leads to diminished warehouse effectiveness and user attrition? Or do you restrict data warehouse access to a limited number of users. Demands on a data warehouse increase exponentially as data and user volumes grow. Data marts are usually constructed for an individual user/business unit because of the Data Mart and Data Warehouse Architectures IT Users Operational Data Data Transformation Enterprise Warehouse & Management 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. support of user connectivity. data warehouses have been considered too costly and as a result. and across products by profit margin. In a 2001 Gartner report. data marts are often considered less expensive to operate. In some environments. The appropriate information is extracted and imported for summarization. update frequencies increase. data marts have proliferated. Exhibit 4b is a schematic of a company similar to GST that does not have a centralized data warehouse. A schematic diagram of a typical data warehouse is shown in Exhibit 4a. For an EDW. The Teradata solution has demonstrated scalability. it was determined that data marts were 70% more expensive to operative per subject area than a comparable data warehouse. and the operational feeder systems multiply. but instead has a series of isolated data marts. Similarly. Teradata. a division of NCR EB-3105 PAGE 5 OF 13 . the summarized data is presented as information for use in future business decisions. scalability has multiple dimensions: hardware. enabling companies to “get to know the customer. Data is then transformed into a consistent format into storage for later use. and by profit margins. across time. 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. data warehouses are cost effective because they eliminate redundancy in staffing as well as information. a division of NCR and Chiek Daddah. a division of NCR EB-3105 PAGE 6 OF 13 . Senior Business Analyst. Furthermore. a division of NCR and Chiek Daddah. This is primarily because business users tend to want to tinker with the system and customize it to their specific business division needs. The data warehouse is more readily adaptable to change as user needs change. As the number of users (tinkerers) grows. even with integrated data. the effectiveness of the mart deteriorates. This customization makes it virtually impossible to share information across the organization. Enterprise data warehouses can be seen as an important step in this direction. Data integration is essential to the development of a single view of the enterprise. Teradata. Finally. 4b Alex Payne. Teradata. 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. Different users with differing information needs might customize the mart to their unique needs. a division of NCR Data Mart and Data Warehouse Architectures External Data Type 1 Data Mart ODS Warehouse Type 2 Data Mart Independent Data Source Type 3 Data Mart Data Sources Information Users Hybrid data mart/data warehouse architecture 4c Alex Payne. Marketing Specialist. Data marts often become isolated data silos.Case Study ROI for a Customer Relationship Management Initiative at GST difficulty of obtaining data consensus across the organization. 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. Data Mart and Data Warehouse Architectures IT Users Operational Data Data Transformation Data Marts Business users accessing disparate data marts Business Users Architecture comprising of isolated data marts and no centralized data warehouse. However. Marketing Specialist. Senior Business Analyst. and is generally free from the tinkering that tends to be endemic to data marts. Teradata. Teradata. Unless the business users are vigilant about keeping pace with the changes.Case Study ROI for a Customer Relationship Management Initiative at GST Like most companies. synchronization and latency are problems in a dependent environment. transformed and then stored in data marts. Finally. 4d Alex Payne. EB-3105 PAGE 7 OF 13 3 . from weekly information to daily information will obviously alter the way the data is interpreted. The system consolidates all data marts into a single enterprise-wide database. the customer. and this high performance means that individual data marts can be eliminated. From here the information flows to data marts. partner data. Teradata. Dependent data marts (Exhibit 4a) receive data from a data warehouse before the data is shared with the business users. The database at the core of the Teradata EDW system has much higher performance than competitors such as IBM or Oracle. competitor data. Eliminating data redundancy. dependent data marts and/or hybrid data marts. the hybrid environment incorporates the data problems associated with data marts. a division of NCR Data marts operated separate from the business users can create data management problems down stream. all data is housed in a single place giving business users access to a single view of the enterprise and more specifically. (MPP) to process many user queries simultaneously. With the new architecture. Hybrid systems incorporate features of both independent and dependent data mart environments. The third environment is the hybrid data mart system. and finally enterprise data. redundancy. GST organizes data by function: customer data. Senior Business Analyst. transactional data is collected. ENTERPRISE DATA WAREHOUSE ARCHITECTURE The architecture for an enterprise data warehouse (EDW) is shown schematically in Exhibit 4d. say for example. decisions could be made using faulty data. In an independent mart (Exhibit 4b). These data are then shared with the business users. guaranteeing data synchronization and capturing data latency are difficult to achieve let alone manage in a data mart environment. instead of querying disparate data marts. Similar to an independent data mart environment. data redundancy is eliminated since the business users have access to a single source for data. Unlocking the information content of the data (data latency) is facilitated since the data is accessible at a more granular level. Partitioning data along these lines obscures many business relationships that could be more cost effective and more profitable. The Teradata EDW database incorporates massive parallel processing 2 Note that marketing research studies may provide additional insights into what constitutes a reasonable percentage increase in value. This is where some thought will have to be given as to what marketing actions will be taken. a division of NCR and Chiek Daddah. A simple change in the way the data is reported from the mart. shown schematically in Exhibit 4c. Data Mart and Data Warehouse Architectures IT Users Operational Data Data Transformation BACKGROUND ON DATA MART SYSTEMS Enterprise Warehouse & Management The typical data mart environment usually includes independent data marts. A schematic of this configuration is given in Exhibit 3. Transactional data is again collected and transformed and the information is stored in a data warehouse. Business Users Architecture of an Enterprise Data Warehouse (EDW). Teradata. business users obtaining data will create internal systems to consolidate the data and to analyze the data. For example. 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. shown in Exhibit 4d. In addition. Marketing Specialist. Users then query the database directly. ): Disk arrays can be clustered together to support 100s of terabytes of data. and is shown schematically in the dashed box. The disk array can be comprised of either 18GB drives (1. The processing cabinets are designed for resiliency with uninterrupted power supply units in each cabinet. data redundancy leads to staff redundancy. Up to 256 cabinets (equaling 512 nodes) can be configured as a single massively parallel processing (MPP) system. Nodes can be aded in pairs to map to the processing requirements of each configuration. two BYNETs are configured with every Teradata MPP (Massively Parallel Processing) System for redundancy. The AWS provides a single. Exhibit 5b demonstrates the proprietary competitive advantage of the Teradata EDW architecture. Redundant Array of Independent Disks (RAID) for data storage.048 Intel CPUs SMP SMP SMP SMP SMP SMP SMP SMP UPS UPS UPS UPS UPS UPS UPS UPS UPS UPS UPS UPS (2) SMP Nodes per cabinet (4) Intel CPUs per Node SMC SMC Disk Options 18GB Drives (1. 5b EB-3105 PAGE 8 OF 13 .4 terabytes of data) or 36 GB drives (2. ¥ and the Access Module Processors (AMPs) for effective data retrieval and disk management. hot-pluggable.As of January 2002. systems maintenance costs. The top portion of Exhibit 5a presents the administration work station (AWS). Each cabinet has two nodes comprised of 4-Intel processors. Finally. data movement costs and data synchronization costs. bi-directional traffic (messages) between the : ¥ Processing Nodes ¥ Parsing Engine (PE-checks the SQL statement.048 Intel CPUs could be configured in a complete Teradata EDW. The bottom cabinets in the exhibit represent disk arrays. More specifically. These BYNETs are uniquely designed to provide simultaneous. In addition. Disk options exist with Teradata sourcing RAID configurations from EMC2 and LSI Logic. The actual Teradata system configuration is shown schematically in Exhibit 5a.Case Study ROI for a Customer Relationship Management Initiative at GST Potential costs that are either eliminated or reduced from Exhibit 4b include administration costs.8TB) Disk Array (40 Disks) Up to 100's Terabytes Disk Array (40 Disks) Disk Array (40 Disks) Disk Array (40 Disks) Height–77" Width–22" per Disk Array The Teradata EDW architecture consists of 2–512 processing nodes (each node consists of four high performing Intel based CPUs—this is called a symmetric multi processor (SMP) node with disk scalability up to 100s of terabytes via highly available. 5a The Teradata BYNET 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 The BYNET is a high-speed interconnect that is optimized for parallel processing with the Teradata Relational Database Management System. can be The Teradata Enterprise Data Warehouse (EDW) Physical Architecture Pilot Footprint SMC SMC SMC SMC AWS BYNET BYNET BYNET BYNET BYNET BYNET BYNET BYNET Up to 256 Cabinets Up to 2. graphical view of the system.4TB) or 36GB Drives (2. Up to 512 nodes. high performance and scalability. the dotted line containing three cabinets (or six nodes) is the footprint for the proposed GST pilot program. Simply stated. and eliminating disparate data marts can reduce the staff count. a total of 2. This is a standalone UNIX or Windows based workstation that is the primary operations interface for MPP systems. access rights and invokes action). each node contains four CPU’s. Not shown are the thousands of end users with access to the system.8 terabytes of data. nodes are interconnected via Teradata’s BYNET. The middle section of Exhibit 5a contains node cabinets. The GST data mart consolidation pilot system would be approximately 20% of the complete EDW. The BYNET is the key design feature that enables support for many concurrent users and maximum system throughput. and Sybase. Informix. For example. First. To support his position. Rather than proceeding with a wholesale consolidation of all existing data marts. the EDW is more efficient to operate thereby reducing the amount of money spent on information management. In addition to the expense of redundant systems.Case Study ROI for a Customer Relationship Management Initiative at GST connect across the message passing layer – this layer is also known as the system bus or as the BYNET. In Exhibit 6b. Second. The exhibit shows how the organization sits “on top” of the data marts. Each system has its own channel to acquire data. The manufacturers of the data marts include Oracle. Region 4 in the Data Mart Environment Jeff Shoemacher CEO VP Region #4 Fall Ainina CFO Rebecca Koop CIO Susan Lightle CAO Bud Baker ILEC Paula Saunders CLEC Cathy Kempf Internet Services Michael Edwards Data Services Joe Castellano Customer Relations Acquire Clean Acquire Clean Store Acquire Clean Acquire Clean Acquire Clean Store Acquire Clean Acquire Clean Acquire Clean Store Acquire Clean Select Select Select Summarize Summarize Summarize Present Present Present System A System B System C 6a Data Warehousing & Data Marts Terminology Simplified by Doug Ebel. The clique grouping provides for data redundancy in case of node failure. In Exhibit 6a. Five fully depreciated data marts have been identified as candidates for consolidation: four Oracle 8i systems and one IBM DB2 system. the EDW will provide access to “better” data. This redundancy was expensive. Region 4 in the Data Warehouse Environment Acquire Acquire Acquire Clean Clean Clean Store Jeff Shoemacher CEO VP Region #4 Fall Ainina CFO Rebecca Koop CIO Susan Lightle CAO Bud Baker ILEC Paula Saunders CLEC Cathy Kempf Internet Services Michael Edwards Data Services Joe Castellano Customer Relations 6b EB-3105 PAGE 9 OF 13 . with 50 disparate data marts. Davis created Exhibit 6a – an organization chart for Region 4 in the current data mart environment. To pitch his idea for data mart consolidation. That is. Davis suggested the consolidation of the data marts into an enterprise data warehouse (EDW) for two reasons. Davis also created a revised organization chart for the same region in a data warehouse environment Exhibit 6b. Company wide. Teradata. clean the data and store the data. there are 14 redundant processes. there are expenses associated with the loss of accuracy from any inconsistencies in the way the data is stored and reported across the systems. a division of NCR System A System B System C B. A centralized data warehouse eliminates these expenses as well. GST had a massive amount of redundancy. the data sits on top of the organization giving everyone immediate DATA MART CONSOLIDATION PROJECT GST is operating fifty disparate data marts. the “Acquire” step in System C is completely redundant. Exhibit 5b also shows nodes sharing a common set of disk arrays grouped into what are known as cliques. Davis pointed out that with just the three systems and four access points represented. IBM. the value of the “better” data is more difficult to quantify. Davis proposed a pilot study: consolidate a subset of the existing data marts to evaluate if the benefits are obtained. unnecessary and could be eliminated through data mart consolidation potentially saving millions in IT expenses. Although cost savings associated with the consolidation are more readily quantified. the four access points have been completely sampled by systems A and B by the time System C is building its database. the “Acquire” step is the bridge between an access point (customer or supplier) and the firm. A. non-personnel support costs for each Oracle system was approximately $1.000/month after the data marts are decommissioned An IBM data mart required one Non-personnel support $1.000 $110. The improvements in efficiency and consistency are value-added by the data mart consolidation. EB-3105 PAGE 10 OF 13 . Maintenance and upgrades for the IBM mart COSTS FOR THE total $110. TERADATA SOLUTION Lightle gave Davis GST employee salary and benefits information.000/yr IBM DB2 1 3 2 3 1 2 $110. GST Average Annual Salary Data System Administrator Data Base Analyst ETL Programmer Query Programmer $130. support staff. four ETL programmers.000. one network number and type of GST employees required for administrator.000 per year. three query programmers. She also gave Davis a summary breakdown of the The staffing requirement for the Teradata system depends.000 per year. This did not include $80. Network Administrator Support Staff Benefits Expected Inflation Rate: Salary and Benefits 7 GST System Staffing Requirements. ten query programmers. one network administrator.000 $80. The most likely scenario for staffing the proposed enterprise data warehouse is one system administrator. CAO of Region 4 was asked to identify the costs associated with the data marts. and two people working as each Oracle and IBM data mart.000 per year per mart for maintenance and upgrades. see Exhibit 8.800.000 for the next year. two data base analysts. two ETL programmers. two ETL programmers. see Exhibit 7. Maintenance.000/yr $1.000/yr costs system administrator.000 40% of salary 4% COSTS OF THE GST DATA MART ENVIRONMENT Susan Lightle. Non-personnel support costs for the IBM system was $1.000 $70. Exhibit 8 also summarizes the best. and Support Costs GST Individual Data Marts Staff / System System Administrator Data Base Analyst ETL Programmer Query Programmer Network Administrator Support Staff Maintenance per node Oracle 8I 1 2 2 3 1 2 $80.000 $80.Case Study ROI for a Customer Relationship Management Initiative at GST access to the same data – this structure is both less costly and more consistent. The exact probabilities for the GST staffing changes were not known. on how GST management decides to handle the personnel 8 reductions.000/yr Best Case 1 6 3 8 0 2 Teradata EDW Most Likely 1 8 4 10 0 3 Worst Case 1 9 8 15 0 4 10% of HW and software list price per yr $125. in part. and two people working as support staff. and expected case scenarios for staffing the new Teradata system. In addition.000.800. three query programmers.000 $40. She offered the following information – Each Oracle data mart requires one system administrator. three data base analysts. worst. eight data base analysts. and three individuals serving as support staff. 000 per month Year 3 -$0- Consulting See Exhibit 11: $125.000 per month once the implementation project is complete.000 per month $125. Although the first four nodes are sold as individual units.000. However. The list prices associated with the acquisition of the data warehouse are included in Exhibit 9. once the system is operational. and disks) would be depreciated using the MACRS 5-year class life schedule assuming the mid-year convention. the training costs would be expensed as incurred. a division of NCR 9 Professional services costs (business consulting) for the three years of the pilot study are quoted at $125. Training would commence once the data marts are loaded into the warehouse.000 Training and Professional Services Costs for the Teradata Solution Expense Training Year 1 See Exhibit 11: $15. The total cost for disks is estimated as $650.000 $190.000 $190. would be $15. Maintenance/upgrades for the nodes and software is 10% of the list price. 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. the disks for the data storage would not be eligible for the 30% discount.8TBytes of data) Adapted from Steven Weber. In addition. EB-3105 PAGE 11 OF 13 . Teradata.000 3rd Node $200. Pricing Director.000 (paid in monthly installments.000 per month $125.000 5th Node $720.000 per month after implementation Data Storage Disk Costs $650.000 per month starting in May Year 2 $15. The consolidation of the five data marts will require five nodes.000 $500. which front-loads the consulting fees. Teradata is willing to provide a $400. Teradata Cost Sheet Hardware and Software Item Hardware Software 1st Node $175.000 $90.000 equipment credit against the purchase price if GST commits to the consolidation pilot study. migrating the data. software. is projected to be $1.) On behalf of Teradata.000 2nd Node $225. most likely case and worst cases. The professional services costs in years 2 and 3 were associated with the design of the data warehouse under a fullconsolidation EDW scenario and for the development of CRM programs.000 4th Node $200. Finally. Davis was convinced the Summary GST financial assumptions former was in the best long-term interest Required return for project investments 14% of GST.000 (For 2. This was an alternative to acquiring a node. Training costs. 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. respectively.Case Study ROI for a Customer Relationship Management Initiative at GST however the GST team urged Bob to use 20%-60%-20% as the probabilities for the staffing scenarios best case. 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. In addition. nodes beyond the fourth are only sold in pairs. Exhibit 11 gives the detailed break down of the professional service costs during the estimated 12-month implementation schedule.000 $190.500. For this ROI analysis. Davis can offer an installed price for nodes and software at 30% off the list price. Davis was also encouraging GST Corporate Tax Rate 38% to engage Teradata’s team of consultants Inflation Rate: Non-personnel costs 5% to commence work on the development of a logical data model to address a 4% Inflation Rate: Personnel costs 10 holistic look at the information require- ments of the total enterprise (including the requirements associated with the remaining 45 data marts).000 per month for the first two years – see Exhibit 11 for the start date of the training. The prices quoted in Exhibit 9 are per node. the first year non-personnel support costs for the Teradata warehouse. and re-architecting the system sequentially. The proposed system (nodes. separate from business consulting. and processes highlighted in Phase 1. Teradata. and update will take 3 to 4 months. Training $15 $15 $15 $15 $15 $15 once all the data and applications were copied to the warehouse. re-architect model. subscriptions. Phase 4. overhead allocation. before the warehouse was fully operational. However. and training dislocated employees on other internal systems. the transition could take as few as ten months or as long as 14 months. Although much of this work is done as part of the proposal. However. Although the end-users have access to the data and tables. 1st Quarter Jan 3 wks Feb Mar 2nd Quarter Apr May Jun 3rd Quarter Jul Aug Sep 4th Quarter Oct Nov Dec 1st Quarter Jan Feb Data Capture and Planning 6 wks Migrate Datamart 2 4 wks Migrate Datamart 3 Phase 1: Data Capture and Planning •Understand the data structure in each mart •Identify ETL processes •Specify amount and frequency of updates •Scope amount of data 4 wks Migrate Datamart 4 4 wks Migrate Datamart 5 6 wks Phase 2: Data Migration •Forklift data from marts into data warehouse •Transfer scripts. Phase 3 – model design. For each month the project goes over or under the 12 month EB-3105 PAGE 12 OF 13 . as soon as May 1 or it could take as The complete transition from data marts to long as September 1. original data mart. it was during Phase 3 that the represents the final phase. Phases 1 on July 1. many details of the existing system must be understood prior to data migration. Professional services costs include: Data capture and planning data migration. tables. Bob believed the 11 first test phase would be complete. and 2 could be accomplished more efficiently or take longer that expected. GST Non-personnel $125 $125 $125 $125 $125 $125 Support required a 6-week test and validation process be conducted to guarantee All dollar amounts are in thousands.Case Study ROI for a Customer Relationship Management Initiative at GST IMPLEMENTATION PROJECT Data Mart Consolidation Project Baseline Exhibit 11 is a high-level schematic of the proposed data mart consolidation implementation project. scope of complete EDW–consolidating the remaining 45 data marts. it was most likely enterprise re-architecture will eliminate the redundant systems producing significant an enterprise data warehouse was expected that the data marts will be decommissioned performance improvements. a division of NCR and the data marts could be retired. Source: Alex Payne. Training costs include: Training existing employees on Teradata system. Senior Business Analyst. C Programs and PL/SQL •Migrate 3rd party Applications •Test data marts Datamart Testing 16 wks Engineer EDW 8 wks Test EDW Phase 3 & 4: Enterprise Data Warehouse Architecture Pilot Study •Develop logical model •Testing After the fifth data mart had been migrated. Testing to take twelve months to achieve. Teradata.) This represents the physical migration of the data. all the original data and Data Mart Consolidation Project Budgeted Cost of Work of Schedule many applications would be again Expenses Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec available to the end-users and the Professional $220 $255 $270 $290 $290 $290 $270 $270 $270 $270 $270 $270 Services data marts could be retired. Marketing Specialist. Bob was rather certain that Phases 3 and 4 will take a total of six months to complete. from the user’s perspective. the warehouse was identical to the Non-personnel support costs include: Travel. Phase 1 – data capture and planning should take approximately 2 weeks. and scope of future CRM applicaions. division of NCR and Cheik Daddah. 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. In total. etc. that. mechanical. All other brand and product names appearing in this release are registered trademarks or trademarks of their respective holders.A. used in a spreadsheet. and a mostlikely case for the project ROI. 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. and expects salaries to increase 4% per year across-the-board. These data are summarized in Exhibit 10. NCR continually improves products as new technologies and components become available. by early June the original data marts could be decommissioned. No part of this publication may be reproduced. 2 had come in under time at 10 months. 3 had taken 12 months.000. and most-likely cases for the project ROI. © 2002 NCR Corporation www. 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.without the permission of Mark Jeffery. which should you present to GST? • How much upfront capital is needed for this project. worst. ADDITIONAL DATA GST used a weighted average cost of capital (WACC) of 14%. In addition. Phase 1 would commence on the first day of January 2002. worse. As members of the team. The three-year horizon begins with the start of Phase 1 and runs for 36 months. Davis had experienced 9 similar data mart consolidation projects. and were realistic in their numbers. He wanted to make sure they would be thorough enough to calculate best. All rights reserved. Of these.Case Study ROI for a Customer Relationship Management Initiative at GST base-line the professional service implementation cost would increase or decrease by approximately $270. or transmitted in any for by means electronic. www. NCR therefore. GST was considering retaining one Oracle mart for an internal training program. would you move forward with the consolidation project? BUSINESS IMPACT MODELING TEAM Davis planned to give this ROI problem to the Business Impact Modeling Group at Teradata. OH U. photocopying. reserves the right to change specifications without prior notice. and 4 projects had run over to the full 14 months. and expected case Davis wanted to make sure his team calculated best. ANALYSIS Following are some questions to consider with your analysis: • What is the project ROI and the pay back period? • Of the best.S. Consult your NCR representative for further information.teradata. or otherwise . following the base-line plan. However. worst. had a tax rate © 2002 by Mark Jeffery. Produced in U. Teradata is a registered trademark and WorldMark is a trademark of NCR Corporation.S.A. stored in a retrieval system.com Dayton.kellogg. and be realistic in their numbers. All features. of 38%. expected an inflation rate for nonpersonnel support costs of 5% annually. help Davis make a recommendation to GST. functions and operations described herein may not be marketed in all parts of the world.nwu. The existing data marts and the enterprise data warehouse would be operated simultaneously until the fifth data mart has been successfully moved. and what financing options would you recommend? • How would you recommend dealing with Richards personnel concerns? • If you were Johnson and Richards.edu EB-3105 PAGE 13 OF 13 . Hence.