We have created a template for a benchmark comparison report based upon the survey input. This report compares a benchmark project to the summary of the survey inputs and analysis. The survey analyzed 78 projects for critical success criteria and factors and created a classification model. Based on the model, the classification of the project is given and comparisons are made to other projects in the survey.
Figure 1: Sample Benchmark Report
Decision Support Projects Benchmark Report6413987601
The classification model has been documented and peer reviewed by members of the Computer Science and Information Systems community. In this form, the report is only available to people who participated in the study.
The aim of the research was to understand the success criteria for decision support projects and what influences the performance of those projects. “Decision support projects are implementation projects that deliver data, analytical models, analytical competence, or all three, for unstructured decision-making and problem-solving. They include subspecialties such as big data, advanced analytics, business intelligence, or artificial intelligence” (Miller,2018). This report summarizes the survey inputs from and analysis from 78 projects. The first section provides descriptive statistics for the data that was collected as part of the survey. The second section provides a summary of the analysis that was performed with the survey data.
The majority of the projects were undertaken as internal projects by large organizations, with big teams and networks of involved organizations. They were diverse in terms of complexity, pace, novelty, and team structure. The participants were from 22 countries with 73% being based in Europe.
Analytic competency and building analytical models and algorithms are characteristics that differentiate the decision support project types.
Critical Success Factors
System quality and information quality are critical success factors that influence system usage and system usage influences project success. Project schedule and budget performance are not correlated with the other success measures so they are not critical success factors in most cases.
Business user, senior manager, top management, and data scientist participation in project activities such as requirements and model building is a benefit. It increases the chances of achieving organizational benefits months or years after the project has been completed.
The recommendation is to actively engage business users and senior managers in hands-on project work such as building models and to focus on providing sufficient system and information quality. As a consequent, the project should deliver long term organizational benefits.
On the following dates, we will discuss the study results in our office in Heidelberg, Germany. Please contact us if you wish to join.
- Success Factors for Business Intelligence (BI) and Big Data Projects: Friday, 28.09.2018
- Impact of Multidisciplinary Teams and Data Scientists on Project Success: Friday, 26.10.2018
- Success Criteria for BI and Big Data Projects: Friday, 30.11.2018
- Stakeholder Influence on System Use and Success for BI and Big Data Projects: Friday, 25.01.2019