Data Analytics: Predicting the future, understanding the past
We are constantly bombarded with information from multiple sources throughout our day. However, not all information is necessary, and we often need to turn this unstructured data into usable information for decision-making. Though making sense of data can sometimes be tricky, we can begin to understand the different types of data and their applications by asking a few simple questions.
- What happened? Descriptive Analytics
Descriptive analytics is the foundation of data analytics, representing the simplest and most common use of data in business today. It seeks to answer the question “what happened?” by summarizing data from the past, usually as dashboards. Descriptive analysis is primarily used in business to track Key Performance Indicators (KPIs) which are used to assess the performance of a business based on how well it achieves certain predetermined targets. Applications of Descriptive Analysis in business include KPI dashboards, monthly revenue reports, and sales lead overviews.
- Why did this happen? Diagnostic Analytics
After you have summarized your data and seen what your sample looks like, the next question that you most likely want to be answered is why certain things are happening. Diagnostic Analytics attempts to answer this question by examining specific situations in depth so that problems or opportunities can be identified. A critical aspect of the diagnostic analysis is creating detailed information so that when new problems arise, previously collected data can be used to address them and eliminate unnecessarily repeating work.
In diagnostic analytics, data mining, data recognition, and drill down are technologies commonly used by data scientists to study key churn indicators or by companies to create connections between data and identify behavioural patterns.
- What is likely to happen? Predictive Analytics
Predictive analytics tries to predict future trends based on what is currently happening as opposed to looking at what happened in the past. It uses the results of both descriptive and diagnostic analytics to convert insights into actionable steps. In so doing, it can determine what will happen if certain conditions are met and can help companies to plan. It is important to note, however, that the quality of these predictions depends on the quality of, and details of the data provided as even small mistakes in data entry can cause significant errors.
This type of analytics is widely used in medicine to predict a patient’s likelihood of developing a disease.
- What should be done? Prescriptive Analytics
Prescriptive analytics combines the insight from all previous analyses with state-of-the-art technology and data practices to determine the course of action that a company should take in a current problem or decision and suggests choices for future approaches.
Artificial Intelligence (AI) systems are an example of prescriptive analytics that consume large amounts of data to continuously learn and then use this information to make informed decisions. Well-designed AI systems can communicate and even action these decisions, with the possibility of performing and optimizing daily business processes without any human intervention. Prescriptive analytics and AI have been used by big data-driven companies such as Facebook and Netflix to enhance their decision-making process.
By using these types of data analytics, you can unlock limitless insight, growth, and possibilities for your company. Let StarApple Analytics show you how.
- Wolvius, C. (2020, July 29). Predict the future, understand the past: the four types of data analysis. Akvo.
- Michigan State University. (2019, October 8). 4 Types of Data Analytics and How to Apply Them.
- Gibson, P. (n.d.). Types of Data Analysis. CHARTIO.
- Stark, S. (2021, June 4). 5 Types of Big Data Analytics and How They Help Customer Success. Last Call.