@article{3372,
  author = {M. M. Silva, A. A. A. Silva, N. Ferraz Jr, A. E. Guelfi, M. T. Azevedo, C. L. C. Larieira, S. T. Kofuji},
  title = {Evaluation of a Business Intelligence Tool with Grey Forecasting Model in a Cloud Environment},
  journal = {Journal of Information & Systems Management},
  year = {2021},
  volume = {11},
  number = {4},
  doi = {https://doi.org/10.6025/jism/2021/11/4/117-133},
  url = {https://www.dline.info/jism/fulltext/v11n4/jismv11n4_3.pdf},
  abstract = {The adoption of cloud computing by small and medium businesses generates several different challenges, such as cost management, lack of human and technological resources, and difficulty in managing the cloud environment in an aligned manner, which makes the decision-making process difficult. Business Intelligence (BI) tools are considered decision support systems and can assist in managing cloud resources, but they lack validation. An approach with other predictive tools, which validates the results of BI tools, can solve this limitation. This work uses the predictive capabilities of a BI, validated by predictive methods, to create scenarios foreseen for the cloud environment that correlate to information relevant to the business area. The results show that the Grey Forecasting Model (GM) can be used to validate forecasts generated by BI. In addition, it was found that the accuracy of forecasts also improves in certain scenarios. These scenarios address different moments, showing correlations between the cloud environment and the business
area, further improving the decision-making process in small and medium-sized companies.},
}