Business intelligence models have been leading the way in technological innovation and efficiency for decades. The rapid rate at which data science software has been developing to meet consumer and provider needs has made keeping up with the latest trends in business intelligence a challenge.
Thankfully, TIBCO, an industry leader in data science software, provides consumers and providers with an informational hub on its website, in addition to low-cost software licensing. One of the most popular trends in business intelligence today is the effective use of business analytics.
What is Business Analytics?
An analytical model consists of three main components: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics comb through large amounts of data to identify specific trends or patterns. Then, the data is presented in an easily digestible manner, usually via a dashboard. The data may take the form of a line graph or a pie chart. This process has allowed organization members who are not technologically proficient to interpret meaningful data easily.
Predictive analytics uses historical data, trends, and statistical methods like mathematic algorithms to determine possible outcomes and the likelihood of their occurrences, which brings us to the last step in this model. What is prescriptive analytics?
How Do Prescriptive Analytics Work?
Prescriptive analytics differ from other types of analytics because it focuses on actionable insight rather than monitoring or collecting data. Through the use of computer science and statistical algorithms, prescriptive analytics can determine the best course of action in a given scenario. This business process automation can help relieve some of the responsibilities that those in leadership roles face.
Prescriptive analytics has the ability to consider all facets of relevant data without bias, therefore, drawing the most accurate conclusions. Turning an organization towards a data-driven decision-making process rather than relying on gut instinct and prior personal experiences eliminates room for human error.
What Types of Businesses Benefit From Prescriptive Analytics?
Both small and enterprise-level businesses can enjoy the benefits of a complete analytical model. A comprehensive analytical model consists of all three cohorts of business analytics: descriptive, predictive, and prescriptive. The prescriptive analytic solution can be described as a complete, effective use of business analytics. In companies that experience constant fluctuations, such as the oil industry, analytics tools can help keep track of the ever-changing environmental conditions, supply chain data, economic impacts, and more.
Virtually any business model can benefit from a prescriptive model because, in a decision-making process, one of the most important aspects is the ability to visualize future outcomes. Prescriptive analytics technology considers decision options and makes specific recommendations based on the particular goals of an organization.
Business strategy plays a vital role in every company. If you are trying to determine whether your organization’s prescriptive approach has room for improvement, consider your prior experiences. Suppose your company has been subjected to avoidable errors arising out of human error or lack of adequate data management processes in the past. In that case, a prescriptive model could likely facilitate better decisions.
The implication of each decision option is often difficult to measure. This statement stands especially true in business environments that experience regular fluctuation. The intricacy of data is often overlooked due to the streamlined processes that business intelligence technology has facilitated. The best way for any organization to utilize big data is through business analytics.
Additionally, the best way to use business analytics is by completing the cycle with a prescriptive model. By reviewing a simulation of possible events and the likelihood of the same, an organization can make informed decisions based solely on hard facts derived from the careful parsing of big data.