Business Intelligence & Descriptive Analytics

By Carlos A.

Business intelligence is the process in which a business collects data and information from its operations and environment to help make strategic decisions. Business intelligence includes many difference types of applications, systems, and tools that are used to collect data and give the data meaning. An important area of Business intelligence is descriptive analytics in which a company performs an analysis of data to gain insight into its business’s past performance (Bertolucci, 1). Descriptive analytics are a form of analytics that help describe what has happened in the past and make it easier to notice changes and trends in a business.

Descriptive analytics are essentially records of a business or an organization’s past performance. This information is the type that is collection from reports, spreadsheets and dashboards. Despite being the beginning stage of Business analytics, Descriptive analytics can be very important in distinguishing patterns in a business setting. A business can view how its performance has improved or declined or which of its products are generating most of it profits. By analyzing descriptive analytics, a business can gain a competitive advantage over its competitors when making strategic decisions (Bertolucci, 1).

The collection and analysis of descriptive analytics is a priority for many business and a focus of its Informational Technology department. An organization has many choices for the type of software it can choose to track its performance. For example, a large retailer can record the numbers of sales that a certain product has been generating by using large databases by made by companies such as Microsoft and IBM. An organization may also use software programs such as SAP or Oracle to record and analyze its business data. There has also been an increase in advanced data recording and analysis of data known as Data Mining.
One example of a type of organization that can benefit from descriptive analytics are hospitals and other health care centers. Hospitals can review their descriptive data and see how many patients they have been admitting, what medicines have been in highest demand, and what are the peak times of patient traffic. According to Research and Markets, the market for Healthcare Analytics will by grow by 23.9 percent by 2020 (Research and Markets). The more that healthcare providers review and analyze their past performance through descriptive analytics the better they can prepare for the future. Since the United States will experience an aging population in the coming years, hospitals will need to increase their capabilities to analyze patient information. Another increasing use of descriptive analytics is by business keeping track of employee’s performance, salary information, and education level. By recording and analyze this information, companies can recognize which employees are more likely to be successful or what qualifications employees needed to achieve certain business goals (Kapoor, 1).

Descriptive Analytics also has certain limitations. A considerable concern when analyzing descriptive data is if the business is recognizing correct patterns and analyzing the data correctly. Misinterpretation of past events is a considerable concern and can have potentially harmful effects for a business’s future. However, this risk can be reduced by analyzing data deeply and referencing past performances. Once a business has Descriptive Analytics its can also go begin to use prescriptive analytics and predictive analytics which can help reduce the limitations of descriptive analytics.

Works Cited
Kapoor, B. (2010). Business intelligence and its use for human resource management.The Journal of Human Resource and Adult Learning, 6(2), 21-30.

Research and markets: Global healthcare descriptive analytics market 2015 – growth, trends & forecast to 2020. (2015, Jun 04). Business Wire

Bertolucci, Jeff. “Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive – InformationWeek.” InformationWeek. N.p., 12 Dec. 2014. Web. 04 Apr. 2016.