Brief Overview of Data Analytics and Vit Tall LLC: Recent Episodes
Vit Tall LLC
This podcast will feature Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics.
Decision Engineering is a process that involves applying one’s relative knowledge to develop, build, maintain, and improve decision-making systems currently in place. This “relevant knowledge” encompasses everything from principles and accepted theories in different fields, to those contributed by the people familiar with the context, and even to the practical know-how of decision engineers themselves.
This discipline essentially requires one to fully analyze and understand a particular situation in order to arrive at the best course of action possible.
With this in mind, it’s fair to say that a decision engineer is mainly responsible for designing and developing ways to improve a specific system to make it more efficient and profitable for the business. This can be done through a four-step process that goes from Decision Theory to System Analysis, to Probability Theory, and finally, to Information Science.
Resilience, in the engineering field, refers to the ability to absorb or sustain damage without falling apart completely, making it the ultimate goal for engineers everywhere. In the simplest of terms, resilience is being able to recover from change while stability is being resistant to change. This is the basic idea behind Resiliency Engineering.
Simply put, Data Analytics is looking at random bits of facts and figure and putting them all together in an understandable manner. The techniques that data analysts use optimize information and uncover trends, metrics, and statistics that would have otherwise been lost in the huge amount of data. With these, businesses can formulate better strategies and plans to help improve their operations.
The field is generally divided into four different types, the first one being Descriptive Analytics, which shows what occurred over a specific period. On the other hand, Diagnostic Analytics focuses more on the “why” or the reasons behind a particular event. Analysts in this field are usually required to use the data they find to make hypotheses and postulations. Predictive Analytics, as the name suggests, makes forecasts on what events and occurrences are likely to happen based on the given data. Lastly, Prescriptive Analytics suggests a plan of action that a business can take.