Data Analytics is mainly a broad term that is used by professionals to help explain how the data is being manipulated and reviewed to be useful for an organization. Typically, it uses large pieces of data to create a structured method of viewing/reading information to find problems and solutions within the organization.
When it comes to data and analytics, imagine you are a doctor. You look at problems that your employer (patient) wants to solve. You ask certain questions, to figure out the root of the problem and then you make solutions to remedy it. One question is not more important than the other and will strongly overlap one another.
There Are 5 Categories in Which Data Analytics is Divided Into:
Descriptive Analytics asks the “What happened?” question. It is the type of analytics that explains what happened with a product over a given period. It typically involves using past data to make conclusions.
Using this method of analytics, information can tell a story about the different variables inputted and allows for executives reviewing the information to see where certain trouble points start.
Diagnostic Analytics asks “Why does this happen?”. Once again using previous data and a bit of hypothesis testing to figure out the root cause of the problem.
Oftentimes correlation testing is used to ensure that the variables you’re inputting match the expected output you’re looking for, thus the need for a hypothesis and proving it. Trial and error is a foundation for this type of analytics so do be patient when attempting to find causations for problems.
Predictive Analytics asks the “What is going to happen?” question. It involves taking all the current and past data sets you have to attempt to forecast an accurate future for the organization with it. A wide variety of different industries and positions use this type of analytics to make business decisions.
The banking and investment industries use a lot of predictive analytics to project future value of products/ markets. Allowing them to make better investments.
Prescriptive Analytics asks “What is the Solution?” question. This type of analytics helps to find a solution to the different problems that Descriptive and Diagnostic Analytics located. Based on this summarized data, Prescriptive Analysts ask themselves “If we know x will happen, how should we respond?”.
Simulation testing could often be used to test different potential alternatives. By testing these alternatives, companies can find the most optimal solution, creating a more efficient system for the organization.
Cognitive Analytics asks “How can we automate this?”. This is the newest and most advanced style of data analytics. It typically uses artificial intelligence and machine learning to create a program that copy’s the problem-solving abilities a person would have. Therefore eliminating human error and making the process more efficient.
This type of analytics is very advanced and the limitations and possibilities of it are being discovered every day! The world is slowly pushing towards this advanced method of analytical efficiency.