Business Intelligence Solution Evolution: Adoption and Use

The global enterprise resource planning (ERP) systems industry blossomed in the 1990s automating back office operations. These systems have now become essential for many companies. Businesses are
now extending their transaction-based systems to support more strategic and complex decisions. Accordingly, ERP vendors have developed a range of solutions focusing on business intelligence (BI) in
a variety of functional areas. However, the question must be asked: Is there an evolutionary nature to the adoption and use of these various BI solutions similar to that which occurred with ERP
systems?

This article uses a two-pronged research approach to investigate the adoption of BI solutions in Australian companies. The first phase of the approach involved a Web-based survey to identify BI
implementation patterns. This was then expanded to include a case study approach. The research indicates an evolutionary maturity in the adoption and use of BI solutions.

Introduction

Much attention has been paid to optimizing business transactions and the associated processing of data. However, top-level management is disappointed in the role information technology plays in
supporting decision making in organizations. (Drucker, 1998) Being able to use information systems to support decision making has been a goal since the introduction of computer technology to
business. One type of information system with this specific goal was termed a decision support system (DSS).

DSSs promised to provide managers with timely and relevant information, in addition to analytical capabilities to enhance decision making. Alter (1980) identified three major characteristics of
DSSs:

  • Designed specifically to facilitate decision processes
  • Support rather than automate decision making
  • Respond quickly to the changing needs of decision makers

Holsapple and Whinston (1996) more recently identified five characteristics that should be common across DSSs:

  • The inclusion of a body of knowledge that encompasses a component of the decision-makers’ domain; this includes how to achieve various tasks and the possible valid conclusions for various
    situations
  • The ability to acquire and maintain descriptive knowledge
  • The flexibility to present knowledge on an ad hoc basis in a variety of customizable formats
  • The ability to derive subsets of stored knowledge to facilitate decision making
  • The flexibility to allow users to choose the sequence of knowledge-management activities

As the demand for information systems to support effective decision making has increased, so have the terms used to describe them: data warehousing, knowledge management, data mining, collaborative
systems, online analytical processing-with business intelligence tending to encompass all.

Figure 1. Business intelligence evolution model (McDonald, 2004)

The market for business intelligence solutions has grown quickly, with revenues reaching $12.8 billion in 2002/03 (Knights, 2004). Figure 1 presents a model that demonstrates the evolution of
business intelligence tools and usage. (McDonald, 2004)

The lowest level of the hierarchy is the business intelligence infrastructure. This represents the data warehouse which extracts data from operational systems and then transforms, consolidates, and
aggregates this data in readiness for reporting to assist in decision making. The next level, business performance management, refers to the use of the data from the data warehouse to provide
feedback to management on key performance indicators (KPI). Decision enablement refers to the automation of decisions (including KPI’s) based on historical decisions stored in a knowledge
repository.

The highest level of the hierarchy is business activity monitoring (BAM). This term, first coined by Gartner, refers to a process whereby key business events are monitored for changes or trends
indicating opportunities or problems, enabling business managers to take corrective action. These BI systems are event-driven, real-time, and rule-based. (Nesamoney, 2004)

The systems also give rise to the concept of analytics and dashboards. A dashboard in the corporate world provides a visual summary to the performance of the company; and this is displayed in a
similar fashion to an automobile dashboard, which provides a visual summary of a car’s performance. The measures are usually KPIs with added knowledge based on previous performance criteria. These
dashboards allow users to drill down, providing greater detail about the performance.

McDonald (2004) further attempts to classify many of the BI solutions as shown in Figure 2.

More recently there has been a consolidation of vendor BI solutions through take overs and mergers. The effectiveness of a business intelligence solution relies largely on the underlying data
infrastructure. (Gartner, 2003) The importance of the underlying data infrastructure is reinforced by McDonald’s model. Accordingly, the major ERP vendors with their data warehouse solutions have
become major players in the business intelligence market. (META Group, 2004)

Although ERP systems have traditionally been concerned with managing the processing of business transactions rather than business intelligence, ERP system vendors are transforming their solutions
to fit into the BI arena. This article discusses this evolution with specific reference to the leading ERP vendor, SAP.

ERP Systems

ERP systems are information systems that are integrated and modular, have broad business functional scope, and are responsible for transaction processing in a real-time environment. The potential
benefits of ERP systems make them essential for companies to be competitive and provide a foundation for future growth. A recent survey of 800 top U.S. companies showed that ERP systems accounted
for 43 percent of these companies’ application budgets. (Somer and Nelson, 2001)


BI Infrastructure


Business Performance Management


Decision Enablement


Business Activity Monitoring

  • Informatica
  • Siebel
  • Oracle
  • Cognos
  • PeopleSoft
  • SAP
  • Business Objects
  • Ascential
  • SAS
  • MicroStrategy
  • Hyperion
  • Crystal Decisions
  • Microsoft
  • Actuate
  • Siebel Analytics
  • Cognos
  • Business Objects
  • SAS
  • PeopleSoft
  • SAP
  • MetaStorm
  • OutlookSoft
  • Hyperion
  • Staffware
  • Brio
  • Informatica
  • FileNet
  • Savvion
  • Siebel Analytics
  • Cognos
  • Business Objects
  • SAS
  • MetaStorm
  • SPSS
  • Microsoft
  • Informatica
  • Staffware
  • Synthean
  • Elity
  • Tibco
  • FirstRain
  • Arzoon (Vigilance)
  • Business Objects

Figure 2. BI Solutions (McDonald, 2004)

The market penetration of ERP systems varies considerably from industry to industry. A report by Computer Economics Inc. stated that 76 percent of manufacturers, 35 percent of insurance and
healthcare companies, and 24 percent of federal government agencies already have an ERP system or are in the process of installing one. (Stedman, 1999)

The major vendor of ERP systems, SAP, has approximately 56 percent of the ERP market worldwide and 75 percent of the Australian market. In Australia, a recent report identified the top 100
companies by IT usage. (BRW, 2002) This list was compared with SAP’s customer list to determine the market penetration of this vendor’s ERP system: 9 out of the top 12 IT users were SAP customers
and 45 percent of the total list were also SAP users.

Researchers believe the growth in the uptake of ERP systems is due to several business needs:

  • Streamline and improve business processes
  • Better manage information systems expenditure
  • Meet competitive pressures to reduce costs
  • Increase responsiveness to customers and their needs
  • Integrate business processes
  • Provide a common platform and better data visibility
  • Use a strategic tool in moving toward electronic business (Davenport et al., 2003; Hammer, 1999; Iggulden, 1999; Somer et al., 2001; Markus et al., 2001)

The benefits expected from the implementation of ERP systems vary from company to company and depend upon the company’s level of ERP maturity. Early research indicated the main benefits companies
were expecting were related to technical issues. A landmark study by Deloitte (1998) identified the main benefits of implementing an ERP system were associated with addressing the Y2K problem and
overcoming issues associated with poor and disparate systems. This research also identified an evolutionary nature to ERP usage post-implementation.

Deloitte identified three phases: stabilize, synthesize, and synergize. In the stabilize phase companies familiarize themselves with the implementation and master the changes that have impacted
their organization. During the synthesize phase, companies seek improvements by implementing better business processes, adding complementary solutions, and motivating people to support and adopt
the changes. In the final phase (synergize), process optimization is achieved, resulting in business transformation.

Evolution of ERP Systems

Holland and Light (2001) developed a maturity model of ERP usage and then considered how cost, entropy (level of disorder), complexity, flexibility, and competitiveness would be impacted at each
stage. They identified three stages.

  • In stage one, companies commence their ERP implementation while simultaneously managing their existing legacy systems.
  • In stage two, the implementation is complete across the organization and the functionality is adopted.
  • In the third stage, the ERP system has been accepted and companies investigate avenues for achieving strategic value from the additional functionality available in the ERP system.

In the model, proposed by Cap Gemini and Ernst & Young (2002), maximum shareholder value can be gained when an organization efficiently and effectively adapts to its environment, whether as
mergers, acquisitions, spin offs, new markets, or improved collaboration with customers and suppliers. They believe ERP systems can assist in the goal of an adaptive enterprise through what they
term the “adaptive ERP value trajectory.” The model focuses on companies moving from core ERP transactions to enterprise application integration (EAI) to integrate and collaborate with business
partners. This implies increased reliance on BI solutions.

Davenport et al. (2004) supported this evolution of ERP systems toward BI through three value drivers they identified for ERP usage:

  • Integrate: where a company is able to integrate its data and processes internally and externally with customers and suppliers
  • Optimize: where a company standardizes strategic processes based on best business practices offered by the ERP system
  • Inform: where a company can provide context-rich information to support effective decision making

This study identified benefits companies were expecting from their systems. The top benefits identified are related to effective decision making and business intelligence.

All the ERP usage models identify the evolutionary nature of how companies use these types of systems to gain greater business value. Accordingly, to satisfy customer demands, ERP systems have
evolved from a transactional focus to a more analytical, strategic focus and incorporate BI functionality.

The Davenport study identified benefits companies were expecting from their systems. The top benefits identified are related to effective decision making and business intelligence.

ERP and Business Intelligence

Although an ERP system’s strength is in the integration of data across functional areas to support particular business processes, the reporting capability has been limited. This was the case for
SAP’s ERP system (R/3) which primarily focused on transaction processing and the associated reports. In an attempt to solve this problem, SAP developed its Logistic Information System (LIS) and
incorporated it into its ERP system (R/3 Ver. 2). This was SAP’s first foray into a data warehouse solution and it had a number of shortcomings. (McDonald et al., 2002) One of the major issues was
that transaction-processing systems (OLTP) are finely tuned for performance, with much of the processing required for analytical processing (OLAP) affecting the performance of the OLTP system.
(Rose et al., 2002) Therefore, it is often recommended that these two systems be separated to optimize performance. In addition, the LIS interacted with only certain modules of SAP, and this
necessitated the use of a separate system for human resource reporting.

Another significant disadvantage of the LIS was that it assumed all the data required for effective decision making was contained in the ERP system. Rarely is an ERP system responsible for all of a
company’s transaction processing needs; numerous legacy systems are often still operating either due to budgetary constraints or because the ERP system lacks necessary functionality.

These performance issues encouraged SAP to develop a separate data warehouse to facilitate business intelligence. This is known as Business Information Warehouse (BW). Stein and Hawking (2002)
performed an analysis of SAP’s Australian customers and identified BW as the most common “second wave” solution implemented postcore ERP. The META Group’s research found that 56 percent of SAP
customers who had implemented three or more modules planned to implement BW in the next two to three years. This group increased to 63 percent when customers had implemented five or more modules.
(Schlegel, 2004)

The evolution of ERP has resulted in the development of a broad range of “bolt-on” solutions. These solutions built upon the underlying data contained within the ERP system and provided extended
functionality to assist with more strategic decision making. In addition to BW, the other solutions included: Customer Relationship Management (CRM), Strategic Enterprise Management (SEM), Advanced
Planner Optimizer (APO), and Workplace (later to become Enterprise Portal). SAP originally referred to these solutions collectively as “New Dimension” solutions and later re-branded them as part
of a marketing and licensing exercise to be included with the ERP system as part of mySAP.com.

As mentioned previously, a company’s ERP system is considered a necessary infrastructure. Maturing companies are placing similar importance on their data warehouse solution. One of the key
characteristics of any decision support system including a data warehouse is, “…the inclusion of a body of knowledge that encompasses a component of the decision-maker’s domain. This
includes how to achieve various tasks and the possible valid conclusions for various situations.” (Holsapple and Whinston, 1996)

In accordance with this characteristic, SAP introduced “Business Content” to enable companies to fully exploit their BW solution. This was comprised of pre-defined reports including the
underlying infrastructure to support specific business situations.

The more strategic solutions of mySAP.com relied heavily on the data contained in BW and provided domain-specific information to assist in decision making. For example, APO facilitates planning,
pricing, scheduling, and product shipping across the supply chain using real-time information from retailers and suppliers. This solution uses various models to assist decision makers in satisfying
customer demands and requires data from internal systems, suppliers, and retailers to be transformed and analyzed and presented in a format that allows easy interpretation. Obviously, BW plays an
important role in this solution, as it acts as the extractor, integrator, and repository for this data.

Similarly, CRM supports the decisions associated with customers in terms of marketing, sales, service, and interactions. CRM requires information to be captured and applied to a pre-stored
scenario, and much of the required information is supplied via the BW solution. (McDonald et al., 2002) While the SEM solution facilitates corporate performance management, it has a number of tools
that assist with strategy formulation and monitoring. As the data required for this decision making can come from numerous systems, SEM depends on a data warehouse solution. (McDonald et al., 2002)
The analytical solutions of mySAP.com rely increasingly on a data warehouse to supply the necessary information. This is consistent with the model shown in Figure 1 (McDonald, 2004) which
identifies a data warehouse (BW) as the infrastructure to underpin other BI solutions.

The SAP “bolt-on” solutions satisfy many of the characteristics of DSS as identified by Holsapple and Whinston. (1996) The BW solution collects and transforms the data from a variety of systems
and then provides it to the other solutions for analytics in specific business domains. An important component of BI solutions is the presentation of knowledge to assist with decision making. The
BW solution has several interfaces that allow end users to create ad hoc queries and drill down as far as the individual transaction documents.

The final component of mySAP.com is the Enterprise Portal (EP). This solution recognizes that users often require more than one solution to perform their daily tasks. The portal provides single
sign-on whereby a user logs on to the portal, which in turn automatically logs the user on to all other specified systems. Similarly, all the required reports, queries, and transactions from the
different systems are accessed through a standardized portal interface. This means that information required for decision making is quickly accessible on a single screen rather than forcing a user
to move between systems with different interfaces.

Adoption of BI by Australian Companies

The remainder of this article describes the adoption of BI solutions by Australian companies. This data was collected as part of a two-phase research project. Research for the first phase was
conducted via a Web-based survey; phase two involved a follow-up interview with a major adopter of BI solutions.

Phase 1 Survey
In 2000, KPMG (now BearingPoint) in conjunction with the Nolan and Norton Institute, conducted research on benchmarking ERP system implementation, usage, and support. In August 2004, a research
project building upon the original survey was undertaken by Victoria University’s ERP Research Group in conjunction with BearingPoint and supported by SAP and the SAP Australian Users Group
(SAUG). The study determined benchmarks related to the use, maintenance, and support of SAP solutions by Australian companies, and evaluated total cost of ownership (TCO). As part of this research,
the intended use of various business intelligence solutions was determined.

A Web-based survey instrument was developed to collect information in seven major areas: organization profile, resource allocation, SAP solutions implemented, upgrades, return on investment,
outsourcing, and future technologies. In support of the study, SAP supplied contact details of the key IT executives for each of their customers.

Company Demographics
The respondent companies came from a variety of industries, predominantly energy and natural resources (21 percent), manufacturing/distribution (21 percent), retail/ wholesale trade (18 percent),
and public sector (18 percent). These results are expected; the sectors have traditionally shown the greatest adoption of SAP solutions in Australia, and, conversely, SAP represents a high
proportion of the ERP systems implemented in these sectors. In terms of revenue, companies were spread equally in the under-$500 million, $500 million to $1 billion, and over-$1 billion brackets.

Half the respondents had a workforce of greater than 1,000 full-time employees (FTE). Employee numbers ranged from 40 to 6,800 FTE. The average number of SAP users across the sample was 655 FTE.
Over half the sample indicated that more than 50 percent of their workforce are SAP users.

SAP Business Intelligence
Respondents were provided a list of SAP BI solutions and asked to indicate which they had implemented or intended to implement. (See Table 1)


BI Solution


Currently Implemented


Implemented in One Year


Implemented in Three Years

Business information Warehouse (BW) 60% 20% 10%
Advanced Planner and Optimiser (APO) 10% 10% 15%
Customer Relationship Analytics (CRM AN) 0% 0% 15%
Strategic Enterprise Management (SEM) 5% 25% 15%

Table 1. Business intelligence solution adoption

Phase 2 Case Study
In phase 2 of this research, data collection was comprised of interviews and access to relevant documents to support the case study.

The company known as KP employs approximately 4,000 people and operates in the service sector across Australia. It originally implemented SAP in late 1999 to address Y2K issues. Initially, KP used
SAP to support its financial and billing processes and limited human resource functionality. Additionally, as part of the implementation, KP consolidated its five disparate practices into one
system. The initial implementation was mainly concerned with back-office processes, where forms would be completed manually by employees and sent to a centralized processing area to be entered into
the SAP system. Accordingly, the system was used by relatively few employees.

Since the initial implementation, more extensive HR functionality has been implemented, including payroll, training and events, travel management, and employee self-service. Later, additional
modules were implemented, including CRM, enterprise portals, and BW. These more recent implementations provided the required functionality and have enabled KP to create a customized user
environment of the standard SAP interfaces. This has been successful, as the initial SAP implementation was poorly received by employees, partly due to organizational changes that occurred
concurrently.

Prior to the implementation of SAP BI solutions, all reports were hand delivered on hard copy and did not provide company-wide information because of the variety of information systems supporting
the five disparate practices. After the initial SAP (R/3) implementation, customized reports were created using SAP’s proprietary programming language (ABAP) and were run overnight in batch mode,
printed centrally, and distributed in hard copy. This usually resulted in a three- to four-day turnaround before employees received final reports. In some cases, staff were downloading soft copies
of reports and manipulating data to suit themselves.

In light of these data-integrity issues, KP realized they needed to improve the speed of report availability and the consistency of data across the organization. Also, there was a requirement to
perform complex calculations in relation to KPI’s and store the results to support future decisions. To incorporate this functionality KP implemented SAP’s data warehouse solution (BW) in March
2003. BW was selected for the additional functionality it offered-pre-delivered extractors which sourced data from a number of systems.

The initial BW implementation took five months and focused on profit-center-management reporting for approximately 500 users. Subsequent work added specific reporting for partners and basic CRM
reporting. The next major implementation of BW involved engagement and time-management reporting, which was fundamental to KP’s business and involved all employees.

In March 2005, KP implemented SAP’s SEM solution to support profit center budget reporting.

Key features of KP’s BI solution landscape:

  • All reports delivered via the enterprise portal and the Web. The level and type of reporting is determined by the portal design, which depends on the user’s role within KP.
  • Related reports are connected via hyperlinks, and reports are tied back to SAP’s ERP for further detail.
  • Most reports can be downloaded to Microsoft Excel for additional calculations and manipulation.
  • Default report views are created to address common employee questions. These reports also incorporate context menus to assist with navigation.
  • Some reports are available to senior management via smart phone. For example, a user can access the CRM system for details about a customer and then click on the phone number to call them.

The next evolution of KP’s BI landscape will involve extending HR salary reporting, improved CRM reporting, and the incorporation of data from non-SAP systems into BW. KP wants to implement a
forecasting model which is directly linked to budgeting, using SEM. This will involve job-level budgeting and staff scheduling. Eventually, KP intends to implement SEM Manager’s Cockpit which can
represent KPIs graphically across the organization. This also has the facility for management to drill down on any KPI.

KP identified a number of challenges associated with the implementation of the various solutions including project management, availability of skilled resources, and technical issues. However,
these challenges are outside the scope of this article.

Discussion

The Business Intelligence Evolution Model (McDonald, 2004) identified a data warehouse as the bottom tier. It was the first BI solution implemented which provided the necessary infrastructure for
other BI solutions to build upon. The survey results indicated that 60 percent of the sample had implemented SAP’s data warehouse. If the percentages are totaled horizontally for BW in Table 1,
within three years of the completion of the study, 90 percent of the respondents will have implemented a data warehouse.

The implementation of BW is relatively simple compared to other BI solutions. It does not require major process change or job redesign, and the benefits are easily realized. More important,
companies that implement an ERP system to replace legacy systems never replace all previous systems-because of cost and/or because the ERP system lacks the appropriate functionality. Therefore, by
inference, these remaining systems are necessary for processing data and decision making. Often, information is required from both the ERP system and the legacy systems.

The data warehouse extracts data from all systems; the data is then transformed, integrated, and consolidated in preparation for querying and reporting. Therefore, a data warehouse provides access
to information which may not have been readily available. For example, before implementing BW, the Australian Defense Forces had one report that required more than 10 days to process. This report
required only 30 minutes with BW.

The data warehouse is the tool that collects and consolidates this data. The data consolidation provides the foundation for SAP’s other BI solutions. For example, APO relies on collecting data
across the extended supply chain from suppliers to customers to enable planning and decision making.

Companies would be expected to solve many of their operational reporting needs via BW. With increased usage and experience, the solution’s use would be extended. This was reinforced in the case
study, where KP implemented reporting for a specific area and, over time, increased the level and scope of reporting. This familiarization with the reporting functionality available in BW would
also result in companies investigating BI solutions for specific functional areas. For example, companies focusing on supply chain management would investigate APO, while more customer-focused
companies would investigate CRM. KP implemented their SEM solution two years after they first implemented BW, which reinforces this concept of familiarization and BI evolution.

The survey results indicated limited uptake of SAP’s other BI solutions. This may reflect the industry sectors in the survey and the maturity of the companies. The results indicate that within
three years, 35 percent of the sample intend to implement APO. In terms of CRM, this is a relatively new solution, and to be successful in this area, companies must go through a process and culture
change rather than just implement the solution. Although a significant proportion had implemented the CRM solution, very few had implemented the BI component (CRM Analytics). We would argue that
most of these companies are still coming to grips with the basic CRM functionality rather than investigating the more advanced analytics.

The survey results for SEM are surprising, as this solution relies on information related to a company’s overall strategy and the monitoring of KPIs.

Consensus predicts this would be the last BI solution to be implemented. It relies on companies having formulated a sound and effective strategy and identified the appropriate KPI’s, and may
require a number of SAP solutions to be implemented to support those various business processes and assist in the collection of data relevant to the KPI’s. Anecdotally, companies that have
attempted to implement SEM have found flaws in their corporate strategy and have had to revisit it. Therefore, it is surprising that 25 percent of the sample indicated an intention to implement SEM
in 2005. KP implemented SEM nearly six years after the initial implementation of the ERP system and two years after BW. They have yet to implement the more advanced features of the solution, such
as the Manager’s Cockpit. The implementation of the enterprise portal in conjunction with the Manager’s Cockpit would provide the necessary functionality for BAM as described by McDonald. (2004)

Implications for Practitioners

This article provided an understanding of the evolution of business intelligence solutions within Australian companies. Both the survey and the case study demonstrate that there is an evolutionary
approach to the adoption of BI solutions. This evolution can be mapped to the Business Intelligence Evolution Model as proposed by McDonald. (2004) The continuous use of business intelligence
solutions results in companies striving for more strategic solutions. It is argued that the BI maturity process is similar to how companies evolve with their ERP usage.

There is a very strong interdependent relationship between ERP systems and BI solutions. The reliance of business intelligence on data generated by transaction-processing systems and the long-term
dominance of ERP vendors in transaction processing gives these vendors a chance to dominate this market as well. SAP has already been identified as a key player, (Schlegel, 2004) and recent mergers
of other key ERP vendors will produce a more consolidated approach to the development of solutions.

ERP systems are no longer solely responsible for transaction processing; they have evolved a range of value-adding applications of which business intelligence is the latest iteration. There will be
an increasing focus on business intelligence in specific business domains and business solutions. This is partly reflected in SAP’s mySAP Business Suite, which now includes CRM, supply chain
management (SCM), product lifecycle management (PLM), and supplier relationship management (SRM), and the associated decision-support tools in each area. All these solutions are underpinned by
integration solutions branded as NetWeaver. Many of SAP’s industry presentations to customers throughout 2005 focused on the role of integration and the importance of business intelligence
analytics.

Companies face a similar dilemma when choosing their BI infrastructure as they faced with their transaction processing systems: whether to implement “best of breed” solutions or an integrated
solution with less functionality. Experience indicated that, with transaction-processing systems, integration was more important than the extended functionality that “best of breed” offered.

The notion of “best of breed” implies that companies have an understanding of their future BI journey. As companies increase in BI maturity, new solutions will be implemented and integrated with
other solutions. The adoption of ERP vendor BI solutions overcomes many of the integration issues. It would also be reasonable to expect ERP vendors to provide a holistic approach to BI reflecting
the broad reach of their transaction processing functionality.

When considering a BI solution, companies should consider how the solution would support the more mature BI activities as proposed by McDonald, (2004) in addition to satisfying immediate BI
requirements.

Susan Foster is a lecturer in the School of Information Systems at Monash University.
Paul Hawking is a senior lecturer in the School of Information Systems in the faculty of Business and Law at Victoria University, Melbourne,
Austraila.
Andrew Stein is a lecturer in the School of Information Systems in the faculty of Business and Law at Victoria
University.

REFERENCES

Alter, S. L. Decision Support Systems: Current Practice and Continuing Challenge, Reading, MA: Addison-Wesley, 1980.

Benbasat, I., D. Goldstein, and M. Mead. “The Case Research Strategy in Studies of Information Systems.” MIS Quarterly, 11(3) (1987), 215-218.

Chan, R., and M. Roseman. Integrating Knowledge into Process Models-A Case Study. Proceedings of the Twelfth Australasian Conference on Information Systems, Southern Cross
University, Australia. (2001)

Comley, P. The Use of the Internet as a Data Collection Method, Media Futures Report. London: Henley Centre, (1996).

Davenport, T., J. Harris, and S. Cantrell. Enterprise Systems Revisited: The Director’s Cut, Accenture, (2003).

Davenport, T., J. Harris, and S. Cantrell. “Enterprise Systems and Ongoing Change,” Business Process Management Journal, Vol. 10, No.1 (2004).

Deloitte. ERP’s Second Wave, Deloitte Consulting, (1998).

Drucker, P. “The Next Information Revolution,” Forbes, located at www.Forbes.com (1998).

Gartner. Predicts 2004: Data Warehousing and Business Intelligence, located at www4.gartner.com, accessed July 2004. (2003).

Hammer, M. “How Process Enterprises Really Work,” Harvard Business Review, Nov./Dec. 1999.

Holland, C., and B. Light. “A Stage Maturity Model for Enterprise Resource Planning Systems Use,” The Database for Advances in Information Systems, Vol. 32, No. 2 (Spring 2001).

Holsapple, C. W., and A. B. Whinston. Decision Support Systems: A Knowledge Based Approach, Minneapolis, MN: West Publishing, 1996.

Iggulden, T. (Editor). “Looking for Payback,” MIS, June 1999.

Keen, P. G., and M. Scott Morton. Decision Support Systems: An Organizational Perspective, MA: Addison Wesley, 1978.

Knights, M. “BI Spending Outpaces Rest of IT Market,” located at www.computing.co.uk/news/1155945 Accessed December 2004.

Markus, L., D. Petrie, and S. Axline. “Bucking the Trends, What the Future May Hold for ERP Packages,” in Shanks, Seddon, and Willcocks (Eds.) Enterprise Systems: ERP, Implementation and
Effectiveness.
London: Cambridge University Press, 2001.

Mehta, R., and E. Sivadas. “Comparing response rates and response content in mail versus electronic mail surveys.” Journal of the Market Research Society, 37, 429-439, (1995).

META Group, “Business Intelligence Tools and Platforms,” retrieved December 2004, located at ftp.metagroup.com/mspectrum/BusinessIntelligenceTools Market.Summary.pdf

McDonald, K., A. Wilmsmeier, D. C. Dixon, and W. H. Inmon. Mastering SAP Business Information Warehouse, Canada: Wiley Publishing, 2002.

McDonald, K. “Is SAP the Right Infrastructure for your Enterprise Analytics” a presentation at the 2004 American SAP User Group Conference, Atlanta, GA, 2004.

Nesamoney, D. “BAM: Event-Driven Business Intelligence for the Real-Time Enterprise,” DM Review, March 2004.

Nolan & Norton Institute, SAP Benchmarking Report 2000, Melbourne: KPMG.

Rose, C. M., and N. Hashmi. SAP BW Certification: A Business Information Warehouse Study Guide, Wiley Publishing, 2002.

Schlegel, K. SAP BW: Staying One Step Ahead of a Juggernaut, META Group, July 2004.

Somer, T., and K. Nelson. “The impact of Critical Success Factors across the Stages of Enterprise Resource Planning Systems Implementations,” Proceedings of the 34th Hawaii International
Conference on System Sciences,
2001, HICSS.

Stanton, J., and S. Rogelberg. “Using Internet/Intranet Web Pages to Collect Organizational Research Data,” Organizational Research Methods, Vol. 4, No. 3, 199-216, 2000.

Stedman, C. “What’s next for ERP?” Computerworld, Vol. 33, (August 16, 1999.)

Stein, A., and P. Hawking. The ERP Marketplace: An Australian Update, Enterprise Resource Planning Solutions and Management, Hershey: IDEA Group Publishing, 2002.

Walsham, G. “Globalisation and IT: Agenda for Research,” Organizational and Social Perspectives on Information Technology, Boston: Kluwer Academic Publishers, 195-210, 2000.

Yin, R. Case Study Research, Design and Methods (2nd Ed.), Newbury Park: Sage Publications, 1994.

Share

submit to reddit
Top