Business intelligent tool performance evaluation mechanism

Abstract

The present invention discloses a business intelligence tool performance evaluation mechanism, which is realized by developing a model to evaluate tool performance according to a series of performance indicators: first determine the key criteria against which the tool performance is evaluated, and then at the most granular level Once these KPIs have been identified, the next step involves assigning the above criteria and the relative weights of the KPIs; creating a pairwise comparison matrix for all criteria items and all KPI items within each criteria item, each identified KPIs are used to evaluate the business intelligence tool, and a set of feedback is defined based on all KPIs, and the weighted average of all KPI scores is the final score. The present invention eliminates the possibility of potential missing parameters when assessing the success of a business intelligence tool, can be effectively used to reduce subjectivity in performance evaluation, and can eliminate bias against any particular standard or performance indicator.

Description

technical field [0001] The invention belongs to the technical field of performance evaluation, and in particular relates to a business intelligence tool performance evaluation mechanism. Background technique [0002] Performance evaluation refers to the evaluation of a project / programme based on some predefined methodology, benefit-cost ratio being the most widely used methodology in this field. Other quantitative methods include calculating NPV net present value, IRR internal rate of return and SRR simple rate of return. These methods essentially focus on comparing cost and benefit parameters to identify net benefits or net losses of the project. The end result of these methods is a numerical value that does not always take into account the details required for qualitative aspects. Another popular technique is the WBS and its variants, including personal interviews, surveys, questionnaires, and expert judgment. These methods fall under the category of qualitative methods...

AI Technical Summary

Problems solved by technology

[0003] The accuracy of these types of models in the performance evaluation process is limited for a number of reasons: first, the various feedbacks are not quantified to provide scalability and flexibility for iterative discussions; differences in the relative importance of different parameters; moreover, all existing methodologies have been developed for the evaluation of projects, programs, or processes without taking into account differences between fields or industries

In particular, no method has been defined for evaluating the performance of business intelligence tools