Evolution of Business Intellignce in the Digital Era - Part Three

April 1, 2024

Customer Relationship Management (CRM) is responsible for supporting the decision associated with customers in terms of marketing, sales, service and interactions. CRM requires information to be captured and applied to a pre-stored scenario much of the required information is supplied via the BW solution. While the Strategic Enterprise Management (SEM) solution facilitates corporate performance management, it has a number of tools that assist with strategy formulation and monitoring. As the data required to assist with this decision making can come from numerous systems. SEM is dependant on a data warehouse solution. The analytical solutions of mySAP.com are increasingly reliant on a data warehouse to supply the necessary information. This is consistent with McDonald (2004) model of BI evolution which identified a Data Warehouse (BW) as the infrastructure to underpin other BI solutions. 

The SAP ‘bolt on’ solutions satisfy many of the characteristics of DSS. The BW solution collects and transforms the data from various systems. This is then provided to the other solutions for analytics in specific domains. An important component of the bi solutions, is the presentation of knowledge to assist with decision making. The BW solution has a number of interfaces that allow end users to create Ad Hoc queries and drill down on the results to get further detail. The level of detail can be as granulated as the individual transaction documents. The final component of mySAP.com is the Enterprise Portal (EP). This solution recognises 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 standardised portal interface. This means that information required for decision making is quickly accessible on one screen rather than moving from system to system with different interfaces. 

At Celeix Digital, we expect the adoption SAP’s BI solutions by companies should reflect an evolutionary approach according to evolution of business intelligence. 

Data in the Digital Era

The business intelligence evolution model (McDonald, 2004) identified a data warehouse as the bottom tier. Accordingly, it was the first BI solution implemented and provided the necessary infrastructure for other BI solutions to build upon. The implementation of BW is relatively simple compared to other BI solutions. It does not require major process change ot job redesign and the benefits are easily realised. More importantly, companies who implement an ERP system to replace legacy system never replace all previous systems. This occurs due to either cost and/or because the ERP system does not incorporate the appropriate functionality. Therefore by inference these remaining systems are necessary for processing data and decision making. 

Often for decisions to be made, information is required from both the ERP system and the legacy systems. The data warehouse extracts data from all systems and this 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. The data warehouse is the tool which collects and consolidates this data. This consolidation of data provides the foundation for SAP’s other BI solutions. For example APO relies on collecting data across the extended supply chain from suppliers through to customers to enable effective planning and decision making. 

It would be expected that companies would solve many of their operational reporting needs via BW and with increased usage and experience the solution’s use would be extended. This familiarisation with the reporting functionality available in BW would also result in companies investigating BI solutions for specific functional areas. For example, companies with an emphasis on supply chain management would investigate APO while companies with a more customer focus would investigate CRM. The results indicated that there was limited uptake of SAP’s other BI solutions however this maybe reflective of the industry sectors and the maturity of the companies. These results also indicate that within three years, 35% of the sample intended to implement APO. 

In terms of CRM, this is a relatively new solution and for companies to be successful in this area requires a process and culture change to occur rather than the just implementation of this solution. Although having implemented the CRM solution very few had implemented the BI component of the solution (CRM Analytics). Most of these companies are still coming to grips with the basic CRM functionality rather than investigating the more advanced analytics. 


The purpose of this blog was to provide an understanding of the evolution of business intelligence solutions. We wanted to demonstrate that there is an evolutionary approach to the adoption of business intelligence 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. This BI maturity process is similar to the how companies evolve with their ERP usage. There is a very strong interdependent relationship between ERP systems and business intelligence solutions. The reliance of business intelligence on data that transaction processing systems generate and the long-term dominance of ERP vendors have in transaction processing gives these vendors a chance to dominate this market. Already SAP has been identified as a key player and with the recent mergers of the other key ERP vendors there will be 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 integrations solutions branded as NetWeaver.  This includes Business Intelligence solutions.  

Further Reading :

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

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

Carlino, J. (1999). AMR Research Unveils Report on Enterprise Application Spending and Penetration, Located at www.amrresearch.com/press/files/99823.asp Accessed July 2004. 

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

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

Davenport, T., Harris, J., & Cantrell, S. (2003). Enterprise Systems Revisited: The Director’s Cut. Accenture. Davenport, T., Harris, J., & Cantrell, S. (2004). Enterprise Systems and Ongoing Change. Business Process Management Journal, Vol. 10, No.1. 

Deloitte, (1998), ERP’s Second Wave, Deloitte Consulting. Drucker, P. (1998). The Next Information Revolution. Forbes, located at www.Forbes.com 

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

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

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

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

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

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

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

Markus, L., Petrie, D., & Axline, S. (2001). 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. 

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

META Group, (2004), Business Intelligence Tools and Platforms, Retrieved December 2004 located at http://ftp.metagroup.com/mspectrum/BusinessIntelligenceT oolsMarket.Summary.pdf 

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

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

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

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

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

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

Somer, T. & Nelson, K. (2001). 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. & Rogelberg, S. (2000). Using Internet/Intranet Web Pages to Collect Organizational Research Data. Organisational Research Methods, Vol. 4, No. 3, 199-216 

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

Stein, A., & Hawking P. (2002). The ERP Marketplace: An Australian Update, Enterprise Resource Planning 

Solutions and Management. Hershey: IDEA Group Publishing.

Walsham, G. (2000) Globalisation and IT: Agenda for Research. in Organisational and Social Perspectives on Information Technology, Boston: Kluwer Academic Publishers, 195-210.

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

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