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Since the beginning of Decision Support Systems (DSS), benchmarks published by hardware and software vendors show that DSS technologies are more efficient by offering better performance for a stable cost and are aiming at lowering operations costs with resources mutualisation functionalities. Nevertheless, as a paradox, whereas data quality and company politics improves significantly, user dissatisfaction with poor performances increases constantly (Pendse, 2007). In reality, this surprising result points the difficulties in globally defining the efficiency of a DSS. As a matter of fact, we see that whereas a “performance/cost” ratio is good for a technician, from the user perspective the “real performance/ expected performance” ratio is bad. Therefore, efficient may mean all or nothing, due to the characteristic of user perspective: a DSS may be very efficient for a developers needs, and completely inefficient for production objectives. In order to improve the global DSS efficiency, companies can act upon (i) cost and/or (ii) expectations. They must be capable of well defining and exhaustively taking into account the costs and expectations relative to their DSS. Concerning the first aspect of cost, the Total Cost of Ownership (TCO) provides a good assessment. It contains two principal aspects: hardware/software costs and cost of operations. With hardware/software, companies are limited by market offers and technological evolutions. The cost of operations, in addition to the potential gain by resource mutualisation, strongly influences the organization costs in rapport with its pertinence. Over this problematic, companies have implemented rationalization based on best practice guidelines, such as the Information Technology Integration Library (ITIL). Nevertheless, an internal study we have conducted at SP2 Solutions in 2007 showed that these guidelines are little or not-known to the DSS experts within or outside company IT departments. Our experience shows that this hasn’t changed almost at all since then. Relating with the second aspect, we state that user expectations cannot be ignored when evaluating the efficiency of a DSS. We estimate that the expected results must be based on the Quality of Service (QoS) and not on the raw technical performances. This way we define the DSS efficiency as a ratio between the DSS QoS and the DSS TCO, which is the foundation of our propositions.

Business Intelligence – CDMB – Implementing BI-CMDB to Lower Operation Cost Expenses and Satisfy Increasing User Expectations