In the future, business intelligence is likely to advance due to various factors. This includes increased knowledge in business intelligence, advanced technology and growth of business ideologies. Business intelligence is likely to become more integrated into established workflows. With application programming interfaces that allow the users to analyze data using existing systems, technology experts are working towards incorporating modern science in business running. It is also likely that it will become more intuitive in that it will provide insight based on the context of the proposition ( Ulian, 2016) . For example, it can answer questions differently to meet the needs of the user. Networks will also advance to handle a vast amount of data together with their movement in and out of the business systems.
Currently, there exists a number of challenges that come with the use of business intelligence. The first challenge is high cost. The cost raises since manpower needs training on how to use the new system. Apart from training, at times the organization may require to upgrade its systems to incorporate BI. Another challenge is expertise. In many organizations, many lacks the knowledge to use BI systems leading to a waste of time and unutilized essential facilities. The other challenge is that it is expensive to outsource technical experts. It makes many organizations take the option of ignoring the use of BI in their organizations. As we look forward to the future, it is essential to come up with ideologies that will help overcome these challenges and see organizations adopt Bi without restrictions. The first solution is for an organization to hire an expertise who is conversant with business intelligence and use him to train other employees gradually. It will make it cheap and make all employees understand the essence of the new system and appreciate it. It will save time as well since the employees will not go to outside organizations to learn.
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Different strategies are used to help business people in making a future decision. The first strategy is planning. Decision making is formed by two processes. These are historical analysis and analyzed patterns. The historical analysis uses past data to evaluate an incident and create decisions based on forecasting and business planning. The other one is an optimization where decision making considers the combination of business intelligence with an emphasis on analytics to gauge performance from operational behaviors. The other strategy is reflection. Decision - making methods revolve around past and present data to determine what happened based on performance numbers. Then one can make an appropriate decision. Scenario is the other strategy that helps in future decision making. It considers the situation and the root causes of the problem that happened in the past.
References
Ulian, T (2016). "Business intelligence implementation according to customer's needs" . APRO Software .
Inmon, W.H. (2018). "Untangling the Definition of Unstructured Data" . Big Data & Analytics Hub. IBM .
