Prescriptive analytics is the last stage of business analytics. It helps to understand ? what should I do?
Prescriptive analytics is a type of data analytics or business analytics.?Specifically, prescriptive analytics factors information or data about possible outcomes or scenarios, resources available, past performance, and current performance of the business, and suggests the best course of action or strategies to achieve the possible outcome in a given situation.
The opposite of prescriptive analytics is descriptive analytics, which analyzes decisions and outcomes after the fact.
How Prescriptive analytics works?
Prescriptive analytics relies on optimization, mathematics, artificial intelligence techniques such as ? machine learning algorithms to help you understand ?what you must do?? Machine learning makes it possible to process a large volume of data.?Prescriptive analytics works with predictive analytics, which involves the use of statistics. By using predictive analytics? estimation of what is likely to happen in future, predictive analytics suggests what future course of action to take.
Prescriptive analytics use machine learning to help businesses decide the best course of action based on predictions of a computer program. If prescriptive analytics is used effectively it can help organizations make informed decisions based on facts, rather than jump to under-informed conclusions by trusting their instincts.
Business Intelligence and Data science
Business Intelligence describes concepts and methods to improve decision making by using the fact-based support system.?Business intelligence is not just limited to business reporting but also provides interactive dashboards, mobile analytics, what-if planning, etc. It also maintains control and governance around reporting. Business intelligence can vary from company to company. For some, it can be a mere basic Key Performance Indicator (KPI) reporting, other companies may use advanced predicting methods which are based on advanced tools and statistical models. Regardless of methods and tools used? they provide facts and figures for informed decision-making to business stakeholder, as per their requirements.
Business Intelligence and Data Science are same but fundamentally different.
From a business perspective, business intelligence and data science have the same role in the business process ? they both provide fact-based insights to help in business decision making. But from another perspective, they both are so fundamental, that it makes everything different? expectations, outcomes, methods, tools used, people involved, etc.
The difference between business intelligence and data science lies in the types of questions they answer ? Business intelligence works with known unknowns, by using known formulas and methods, a new value of an already known KPI is calculated, while data science is known for working with unknown unknowns, answering data questions that are not answered before. This is a small difference but holds a lot of importance. Without using any known formula or methods given, data science answers unknown unknowns by using trial and error approach and select the best approach. Since it is totally unknown, data science cannot guarantee the success of a project before it begins, it cannot tell how many steps would be required to find a solution and how the project will look like.
In order to find solutions as quickly as possible, data science uses methods and tools optimized for speed such as programming languages, micro-services architecture, libraries, docker containers, etc.
Other differences between business intelligence and data science are: the use of machine learning in business intelligence is nil or very low, while in data science it is very high. The risk involved in business intelligence is low, while in data science it is high.
Business Intelligence and Data science are kind of same but completely different and one needs to look at it from a different perspective.?