Several functions could be included within these situations so that assistance could be provided to the decision-making process. Therefore, it could be observed that the employees working for longer duration should be considered in the decision making process (Appelbaum et al. 2017). This is because of the fact that the decisions pertaining to the staffs working for longer duration have to be considered for increasing their motivation level, which, in turn, would enhance the overall business productivity.
Another aspect considered is a model used so that demand could be estimated for the upcoming quarter (Kimball et al. 2015). With the help of this estimation, ideas, policies and plans could be obtained in relation to the production process for the upcoming quarter and these would help in paving the path for the determination of effective operational activities.
In order to extract intelligence from business information, two business intelligence tools are used commonly for aiding in decision making through modelling and analysis. In this section, a brief description of the two tools used would be discussed for providing assistance in the decision making process.
The most significant aspect to be considered in this report is to describe the three features inherent in the business intelligence tools widely used. It has been observed that there are various characteristics associated with business intelligence tools and three significant features are demonstrated as follows:
This tool possesses the capability of visualising and mapping data in various formats, which have geographical nature (Larson and Chang 2016). The data visualisation and exploration of the data sets based on spatial elements enables the organisations in gaining understanding regarding their business operations from a fresh perspective. This is termed as sales per region (Moro, Cortez and Rita 2015).
These dashboards offer concise real time data that could be understood easily to the business owners for assisting them in greater and effective decision making procedures by minimising the response time to internal as well as external events (Sauter 2014). The executives have control over the dashboards, which are personalised and this assists in easy understanding of key performance indicators along with scheduled and regular information. In addition, the method of exception reporting would have the ability of providing alerts to the executives towards any sort of unprecedented situations and events requiring immediate actions (Trieu 2017). The customised data delivery denotes the ability of the executives in constructing quick decisions without any sort of estimations and identifying irrelevant information for removing the same.
These reports are beneficial for the users so that data could be transfigured into knowledge. They allow the users to gain insight of the analyses within the reports and the underlying information reliant on data for undertaking effective decisions (Wieder and Ossimitz 2015). Therefore, the users need to possess certain abilities, which are enumerated briefly as follows:
- Evaluating the different aspects of the reports thoroughly
- Undertaking dice and slice related to the evaluation of OLAP
- Incorporation of analysis such as regression and moving averages for dealing with the data patterns
- Using time series for scanning large sets of data so that anomalies could be understood inherent in data
- Use of conditional formatting for forming data alerts highlighting the data exceptions (Wu, Chen and Olson 2014)
It is evident from the above discussion that all the features of the widely used in business intelligence tools are addressed so that better decisions could be undertaken for business improvements. Moreover, it has been analysed that the features play a significant role and such features are necessary for the business organisations to sustain in the current era.
Appelbaum, D., Kogan, A., Vasarhelyi, M. and Yan, Z., 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, pp.29-44.
Kimball, R., Ross, M., Mundy, J. and Thornthwaite, W., 2015. The kimball group reader: Relentlessly practical tools for data warehousing and business intelligence remastered collection. John Wiley & Sons.
Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), pp.700-710.
Moro, S., Cortez, P. and Rita, P., 2015. Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), pp.1314-1324.
Sauter, V.L., 2014. Decision support systems for business intelligence. John Wiley & Sons.
Trieu, V.H., 2017. Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93, pp.111-124.
Wieder, B. and Ossimitz, M.L., 2015. The impact of Business Intelligence on the quality of decision making–a mediation model. Procedia Computer Science, 64, pp.1163-1171.
Wu, D.D., Chen, S.H. and Olson, D.L., 2014. Business intelligence in risk management: Some recent progresses. Information Sciences, 256, pp.1-7.