Main Steps in Evolution
The steps in the evolution of management information systems are the result of a large number of contributions, which through several decades have brought about progressive refinement of the methodological approach to systems management and the achievement of largely popular conceptual models, such as Management Information Systems, Decision Support Systems, and Executive Information Sys- tems (Iivari, 1992).
A complete analysis of these approaches is not the issue here. Nevertheless, some of the main aspects must be called to mind, in order to allow us to spot the stages that have led to modem Customer Relationship Management (CRM) systems.
Management Information Systems were born to supply top management with the data necessary to control internal processes and plan resources correctly, as al- ready mentioned by Anthony - who used to differentiate between operational and executive control- as long ago as in 1965 (Anthony, 1965).
The following contributions in the way of automated decision-help systems (Deci- sion Support Systems) have their roots in two stimulating research trends: decision mechanism studies and interactive information systems. They were born to sup- port decision-makers in the analysis of semistructured problems.
Last but not least came Executive Information Systems, which, according to Rockart's definition, were designed expressly to support summit power decisions (Rockart, 1988).
Looking back a few decades, we notice how all these approaches have found real applications in the complex mosaic we call a management information system.
The most interesting results did not come at once, but only when the available technology allowed the realization of user-friendly systems. However, in many cases such systems proved not to be capable of satisfying company requirements fully so as to deliver relevant information in an integrated way.
Introduction of client/server architectures, improvement in database management techniques, and diffusion of high-performance workstations are just some of the factors that have allowed the spread of decision support forecast in Scott-Morton's studies in 1971, albeit with a 10-year delay. The consequences of technological innovation have gone further, however.
12 The Theoretical Framework of CRM
The last few year is characterized by the widespread acceptance of the Internet and of company intranets at every level. A common and easily operated access interface allows to data and information from different sources to be shared and used for different purposes (Lucas, 1992). Thanks to the new functionalities of- fered by the net, information flows will experience further development, which will be accompanied by increased organizational and economic relevance of in- formation processing activities.
r
Ul (J) OJ III
Ul
~ III 0 C
5 0
>
(J)
1
IS
MIS DSS EIS
BI CRM
OIl
high
ex-post observation
low medium medium
high low medium
high high
management coordination relations with
decisions the environment
company process •
Table 1.1 Decision systems: evolutionary stages and company processes
Unfortunately, even though information technology is progressing rapidly, so far no technical tool or automation mechanism has proved able to translate the greater data availability into a significant improvement in company decision processes (Osterle, 1995; Peppers, Rogers, Dorf, 1998).
As a complement to the growing diffusion of decision support-oriented systems, today's technology once more offers a wide range of options and solutions which enable the extension of decision activities in other company areas.
At first, technological innovation allowed the development of some decision sup- ports, which were mostly not integrated into the existing information system. Data
From Decision Support Systems to CRM: Main Steps in Evolution 13
structures, data-saving approaches and operational system architecture were de- signed to provide exclusive support to administrative and accounting activities.
Subsets were made from existing archives in order to create a new database which could, after proper "restructuring", be used to initiate management support appli- cations (Berry, Linoff, 1997; Berry, Linoff, 1999; Ciborra, 1996).
This process caused great data alignment problems with the operational system and consequently jeopardized analysis reliability, and especially in those indus- tries where management involved intensive use of information systems. Later on, at the end of the 1980s, new trends emerged in the direction of increasing integra- tion of operational and analytical information systems, which set off the diffusion of so-called business intelligence systems. Such systems automate the decision process through systematic access to a database, which makes it possible to carry out analyses and extract information and thus to understand those phenomena that lead to an improvement of the decision process or at least reduce the uncertainties of the decisions to be taken. Some members of the business intelligence family are decision support systems, executive information systems, and all tools that enable querying and reporting activities (Imhoff, Loftis, Geiger, 2001). Reference techno- logical architecture also shows a greater integration between operational and deci- sion support systems (Inmon, 1996). The consequent popularity of data warehouse and data mining systems is now pushing toward ever-increasing integration, which in tum leads to increasing automation in some of the decision activities (Kelly, 1997).
Progressive consolidation of integrated architectures (the operational part with the analytical part) allows all decision activities to be finalized per company area (Ci- borra, 2000).
In particular, and owing to the creation of datamarts, they give users and managers access to all the data and appropriate information that can be found in a com- pany's information system, and they even integrate external sources, which enrich the archive content used to "feed" the decision process. This is how dedicated datamarts come to life: one each for management control, synthesis systems, mar- keting, internal auditing, etc. (see Fig. 1.3). With the aid of data analysis systems (data mining systems in particular) they can uncover hidden patterns, thus helping those concerned to spot interesting points and providing for directions that are likely to reduce the degree of uncertainty in future decisions (Kimball, 1996; Poe, 1996).
This context of the aim of this present work calls for a close examination of the main aspects concerned with management, organization, and automation of com- pany-to-customer relationships.
On the one hand, marketing information systems have gained advantages by the introduction of business intelligence technologies, namely data warehouses and
14 The Theoretical Framework ofCRM
data mining. On the other hand, several factors have led to reconsideration of their strategic value and matters such as the progressive evolution of the Internet, the increased competition level, and the ability to operate in geographically distant markets through e-business initiatives. Therefore, integrated solutions that are well-suited to allowing for automation of company-customer interaction are being looked for, in order to gain a durable competitive advantage (Porter, Millar, 1985).