Data analytics is the science of analysing raw data and drawing conclusions from it. The collected information helps businesses to see the impact of their actions, optimise their performance, maximise profit, and make more strategically-guided decisions. But what are the four approaches to data analytics and which fields does each give access to?
1. Descriptive Analytics
Descriptive analytics describes what has happened over a given period of time. Have sales increased over the past year? Are YouTube views higher this month than the last?
The conclusions drawn from this data are often given to other departments in the company. That is the case for Web Analysts, who will analyse data around specific pages, topics or websites to give the marketing and programming departments useful and useable insights. This type of data analytics is also used in budgeting, to create reports for the financial department, in employment, to analyse compensation and benefits to give insights to the human resources department, or in fraud prevention, to monitor and analyse fraud data.
2. Diagnostic Analytics
Diagnostic analytics focuses more on why this or that happened. Have sales increased because of the latest advertisement? Did the weather impact views?
Like descriptive analytics, the conclusions made are given to other departments to be analysed. This method is used by Data Analysts to investigate vicarious departments such as sales, by focusing on how to support, improve or optimise the sale process, business products, by investigating which characteristics customers like, how well products are responding to advertisements and what the optimal price is, or insurance underwriting, by analysing individuals, companies, and industry data for decisions on insurance plans.
3. Predictive Analytics
Predictive analytics moves to what is likely going to happen in the near term by crossing old data and predictions. What happened to views last time we had a rainy weekend? What are the predictions for the next few weeks?
Predictive analytics are very useful in fields where you need to cross data from various sources internal and external. This method is used for instance by Actuaries to help insurance companies to create probability tables, risk forecasting, and liability planning by analysing mortality, accidents, sicknesses, disabilities and retirement rates. It is also widely used by Credit Analysts to report and monitor credits, calculate lending risks and decide which loans should be approved.
4. Prescriptive Analytics
Prescriptive analytics suggests a course of action by taking into account statistics and predictions. If weather models are predicting the next three weekends to be rainy, we should post one extra video per week and plan a live event to fulfil viewers’ desire to watch more content from our channel.
Prescriptive analytics is the most complete type of analytics since it provides both the conclusions of the analysis and the suggested response to this conclusion. Therefore, it is often given to executives and directors to make their decisions. It is used for instance in business analysis, management reporting, and corporate strategy analysis.
If you want to learn more about these four types of Data Analytics or would like to work in this highly in-demand industry, Dublin CODING School offers a Data Analytics course which will help you get there in a few weeks! For more information, click here.