Prescriptive Analytics and How Can It Help?
Business analytics takes the data that an organization generates and turns it into insight. Through the utilization of historical data and machine learning, data scientists can significantly enhance business processes. By using mathematic and statistical algorithms in your predictive model, you will be able to predict the most probable future outcomes.
Keeping current with the latest trends in data science software technology can be challenging. Thankfully, industry leaders in data science software technology like TIBCO simplify some of your research by compiling necessary resources on their website for students and business owners.
What is prescriptive analytics?
By analyzing historical data and the trends and patterns within it, prescriptive analytics help businesses create the best course of action for a given scenario. The primary aspect of prescriptive analytics that sets it apart from other types of analytics is that its focus is on actionable insight rather than data monitoring or management.
Prescriptive analytics considers your decision options, compares the most probable outcomes, and makes intelligent recommendations based on factual evidence about the next step to be the most beneficial for your organization.
How do prescriptive analytics work?
A prescriptive optimization model operates through mathematic and computer science statistical methods. Essentially, prescriptive analytics creates possible patterns in decision-making that could be impactful to a company and compares them alongside previously collected and analyzed data. Business intelligence tools like prescriptive analytics provide a means for responsible, data-driven decision-making. In the business analytics model, prescriptive analytics is the final step.
How do prescriptive analytics relate to other types of analytics?
As mentioned above, prescriptive analytics is the very last step in the business analytics process. Understanding each of the other steps in this optimization model can help provide clarity regarding prescriptive analytics processes and conclusions. After data mining, the first step in the analytical method is descriptive analytics.
This type of analytics examines a large volume of data and identifies patterns and trends within it. This relevant data is then translated into visualizations such as line graphs, tables, or pie charts for easier digestion.
The next step in the business analytics process is predictive analytics. Similar to prescriptive analytics, predictive analytics outlines the probability of future outcomes based on historical data. The difference between predictive and prescriptive analytics is that the predictive process generates possible results and the likelihood of the same. The prescriptive model takes those possible outcomes and creates the best plan of action in a given scenario.
How is business analytics used?
The uses of business analytics are immense. Both small and enterprise-level organizations can enjoy the benefits of analytics because they provide valuable insight into operations processes which saves time, money, and resources. Additionally, business analytics helps to measure and maintain data regarding external sources of influence. Prescriptive analytics is specifically useful because it streamlines the decision-making process and eliminates the risk for human error arising out of unconscious bias.
Machine learning mimics the way that human beings learn through reinforcement training, similar to how past experiences dictate the way we react to new information. However, the prescriptive model is free from human judgment. Therefore, you can confidently rely on prescriptive analytics solutions to recommend solutions based solely on statistical methods and historical data. This heightened accuracy can provide organizations with reliable, precise predictions.
Overall, prescriptive analytics weigh the implications of each decision option through a combination of statistical algorithms and the data provided by descriptive and predictive analytics. This combination of data is then used to derive the best solution in a scenario. Prescriptive analytics and other business intelligence tools lead organizations to better decisions through proven scientific and mathematical methodology.