Introduction
Have you ever worked on a dissertation? Ohh, it is good to know that you have worked on a dissertation before. During working, you might have run an analysis of the data. If you have run an analysis, then you must have known about different types of data analytics. Data analysis is important in a dissertation as it helps the researcher draw meaningful conclusions. Many student researchers do not know about different types of data analytics. They just conclude the dissertation results based on what they think. This practice is very wrong as it undermines the quality of their dissertation. Therefore, you must know about all the types of analysis used in a dissertation. Today’s article is all about those types. There will be a mention of the basic types of analytics along with examples. Before discussing those types, let’s define the term data analytics first.
What is data analytics?
Data analytics is the use of different techniques and tools to capture several trends. In this process, the researcher analyses the raw data and notes down the trends he finds. The use of a technique depends pretty much on the research goals. As the goal of the research differs, so is the data analytics technique. Data analytics, basically, lies at the intersection of the data, statistics and analysis. The researcher collects the raw data and processes it using some statistical techniques. After that, he analyses the results using different types of data analytics to conclude the topic. Thus, it is a brief introduction to data analytics.
What are the types of data analytics?
After describing the data analytics, a bit, let’s talk about the different types of it. Normally, there are four types of analytics that researchers can use to analyse the data. The use of those types depends on the research goals and objectives. However, a brief description of all the types is as follows;
Descriptive analytics
The first type of data analytics is descriptive analytics. This type is the foundation of every other type of analysis. In simple words, this type of analysis is the simplest and most commonly used among all other types. This analytics type gives an idea of what happened in the past. As the name suggests, this analysis aims to provide a full picture of the historic happenings. Remember that this analysis type does not explain “why”. It only describes “what” and describes it in detail.
The most common example of this type of analytics is Google analytics. It describes what has been happening with your website or blog since its launch. It shows you how many people have visited your website in the last 10 days. It also allows you to know where your visitors have come from. Therefore, descriptive analysis is the first type you can use in your dissertation.
Diagonostic analytics
It is the second type of data analytics. The researchers use this type of answer “why it has happened.” The main purpose of the diagnostic analysis is to identify and respond to the anomalies in your data. You can say that this analytics type is performed to know the cause and effect of an event. Also, you must keep in mind that diagnostic analysis is performed on the past data. Researchers often employ techniques like data mining, data discovery, drill-down, and correlations are often employed by researchers.
One possible example of diagnostic analysis is if your descriptive analytics show a 20% decrease in sales. This decrement has been observed in sales in March. Now, you know what has happened. The next step is to diagnose the problems and find out why sales have dropped in March. Therefore, you employ a diagnostic analysis technique.
Predictive analytics
Among all other types of data analytics, this type tells about the future. It is clear from this analytics that it is all about predicting the future. This analysis is based on past trends and patterns. After analysing those patterns, the researcher predicts the future happening. This model is specifically useful because it enables the researchers to plan ahead. The researchers make future predictions based on their gathered results. This type of data analytics is the most widely used type.
The predictive analytics model is further categorised into two models. One is a statistical model, and the other is a predictive model. In statistical analysis, the researcher uses different methods to forecast future events. One thing that is important to remember here is that this analytics type only provides an estimation of what is happening in the future. It does not tell about anything the accuracy of the estimation. The accuracy of the future prediction depends on the accuracy of the collected data.
Prescriptive analytics
After performing the steps mentioned above, you know what has happened. You also know why it happened and what might happen in the future. Till now, one thing that you do not know is what should be done in future. Among other types of data analytics, prescriptive analytics tells you about the course of action that you should take. In simple words, this type of analytics tells you how you can take advantage of the predicted future. Normally, it answers the following two questions.
- What steps can you take to avoid a future problem?
- What can you do to capitalise on an emerging trend?
Prescriptive data analytics is the most complex type of analysis. It involves machine learning, big data, statistical methods and computational modelling procedures. This analysis considers all the possible pathways to solving a problem and defining the course of action.
Conclusion
Setting the research strategy for a dissertation is a difficult task. You have to choose one type among many types of data analytics. Also, the usage of data analytics techniques depends pretty much on the research objectives and goals. The types mentioned above of data analytics are the most widely used ones. You can also use those types in your dissertation but keep in mind the research objectives before using them.
Author Bio:
Robert Fawl is a professional Content writer & Content Marketer. Based in London, Robert is an author and blogger with experience in encounter composing on various topics including but not limited to Essay Writing, Dissertation Writing, Coursework Writing Services, Thesis Writing Services and Assignment Writing etc.
