ACCT 332 Accounting Information Systems

Business Intelligence has become very valuable within today  business environment. What are some of the changes that are driving this need for BI? Do you feel that Predictive Analytics are the wave of the future? Why or why not?

Introduction

Technologies, applications and practices that are used in data collection, integration, analysis and presentation of business information are cumulatively referred as business intelligence (Chen, Chiang & Storey, 2012). It serves the purposes of supporting the manager to take better decision for the business. It provides views on historical, present and predictive business operations from the stored data.

Need for Business Intelligence

Recent businesses have identified various needs in their business for the integration of business intelligence. Some of the significant needs are discussed below.

• Overflowing data – Company after integration of modern technologies in their business is stocking a huge amount of data. However, unsorted data in the system is of no use to the business owners. BI can potentially help the business in sorting their significant data to provide a meaningful context.
• Incapability of Spreadsheet – Excel was manufacture to handle only a limited amount of data. The recent growth in business is overflowing the limits of excel spreadsheet. Even the routine clean and manage of data is not providing any potential benefits for the organizations (Chaudhuri, Dayal & Narasayya, 2011). Incoming data are taking no time to overreach the maximum capacity of the spreadsheets the companies are using that calls for the implementation o business intelligence that has potential to handle the big data of the organizations.
• Multiple Data Sources –The data in today’s business is coming from multiple data sources in a single coherent location. The companies that are using traditional system in the reporting are facing challenges and in turn overusing their resource with no gain. They require shifting to the use of BI for gaining competitive advantage in current market.

Predictive Analytics as the wave of the future

Many scholars assume predictive analysis as the future trend of big data. It rejects the idea of reacting to the insights gained through data analysis. It enables the companies to use a combination of real-time, historical and third party data to build forecasts of upcoming incidents well in advance, ranging from hours, weeks even months. It will further facilitate the company in avoiding the forthcoming problems and risk like equipment failure or depleted stock or even capitalize on the opportunities to market products to customers. Hence, it definitely has secured position in the future of data analysis. However, the prediction is only the beginning of the multiple steps required to evade forthcoming risk. It is of no use until the exposed insight can be deployed directly into software applications and business process. According to Siegel (2016) predictive analysis has its significance in the future market and help organizations over industries in their operation, Some of the significance of predictive analysis are risk reduction, optimizing market campaign, improving operation and detecting fraud. As identified by Waller and Fawcett (2013), it can help the retail industry by identifying the issues in the business process, improving the supply chain, gain better insight on customer demand and many more.

Conclusion

It can be concluded that recent growth in technology increased the demand of business intelligence. The traditional software used by the organizations are proving soiled and failing to serve its purpose, which makes it necessary for the company to utilize business intelligence to better handle their data. Predictive analysis on the other hand has its utility on various grounds as discussed above.

References

Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88-98.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Siegel, E. (2016). Predictive analytics: The power to predict who will click, buy, lie, or die (pp. 103-110). Hoboken (NJ): Wiley.

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.