Unpacking the different data analysis methodologies

Data analysis is a vital part of any business these days and it’s easy to  see why. With an abundance of data available at the fingertips, it’s possible to foresee upcoming trends so that businesses can adapt or be ready to adapt when the time comes, gather valuable insights into the core of the business, and find out where they can improve.

Instead of decision-makers making assumptions and trying to figure out where they went wrong or what the future may bring, businesses can now turn to data to reveal valuable insights and make better-informed decisions.

What is data analysis?

Data analysis is the process of collecting, cleaning, transforming, and modelling data to discover patterns and useful insights, such as metrics, facts, and figures. This useful extracted information from the data analysis is then used to make better-informed decisions. There are two primary methods for data analysis, which are qualitative data analysis techniques and quantitative data analysis techniques. These techniques can be used independently or in combination with each other to help decision-makers obtain business insights from different data types.

What is quantitative data analysis

Quantitative data analysis revolves around working with numerical variables, such as statistics, percentages, calculations, measurements, etc. Because numbers are the nature of quantitative data analysis, these data analysis techniques typically include working with algorithms, mathematical analysis tools, and software to manipulate data and to discover patterns and insights that reveal the business value.

What is qualitative data analysis

Working with qualitative data involves working with information that is typically nonnumerical, such as conceptual information or user feedback. A data analyst may utilise participant observation approaches, conduct interviews, run focus groups, or review documents in order to gain valuable insights into the business using qualitative data analysis techniques.

The different data analysis methodologies

When it comes to the different types of data analysis methodologies, there are four that you will see across all industries. These are:

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Although they are individually categorised, they are all linked together and are able to build upon one another to gain more valuable insights. To get the most useful insights from the data, data analysts will need to understand the four key types of data analytics in order to utilise them to the fullest extent and use them in tandem with one another in order to maximise the business’s benefits and insights from the data. Below are the four key types of data analytics.

Descriptive analytics

Descriptive analytics, also known as observation and reporting, is the most basic level of analytics, and the foundation the other types of analytics are built on. Descriptive analytics answers the question, “What happened?” as it pulls trends from raw data to paint a picture of what is happening and what has happened by summarising past data, usually in the form of dashboards.

The main use of descriptive analytics in business is to track Key Performance Indicators (KPIs). Business applications of descriptive analysis also include monthly revenue reports and sales leads overviews.

Diagnostic analytics

Diagnostic analytics is all about the why. After asking the main question, “what happened?” during the descriptive analysis, the next step is to dive deeper into the data to create detailed information and to find out why it happened. Taking the data insights from the descriptive analysis, the diagnostic analysis’s purpose is to find the causes of those outcomes.

The more familiar and the more types of data analysis you conduct will only help you and the business further understand how to improve and meet all the business objectives. Businesses make use of diagnostic analysis to create connections between the data and identify patterns of behaviour so that they may address these issues head-on.

Predictive analytics

As the name suggests, predictive analytics is about predicting the future outcomes based on past or current data; it shows data analysts what is likely to happen. Forecasting future outcomes is just an estimate, and in order to have the most accurate predictions, data analysts will need to dig into detailed information. The more information and the more detailed it is, the better.

Predictive analytics involves technologies such as machine learning, algorithms, and artificial intelligence to gain valuable insights. This data analysis technique allows businesses to predict different decisions, test out their success, find areas of weakness in the business, and make more accurate predictions.

Prescriptive analytics

Prescriptive analysis combines all the insights from the descriptive analytics, diagnostic analytics, and predictive analytics to determine which action to take in a current situation or which course of action to take to solve a business problem. Prescriptive analysis is the last level of analytics to be used, as it requires all the previous data to be able to be completed. This is what makes it the most advanced and most powerful data analysis.

Prescriptive analytics is when the data itself prescribes what course of action to take and how the data analyst presents the findings to the decision-makers in a business can heavily influence the future and the success of the business. This is due to the fact that data-driven decision-making is tied most closely to predictive and prescriptive analytics, as they are more than just facts and numbers but offer solutions and insights into the future.

Final words

The four types of data analysis methodologies mentioned above play a vital role in turning data into meaningful insights that can help any business make better-informed decisions. Utilising one or all four techniques will help businesses answer pressing questions such as what happened and why it happened. Knowing the answer to these questions will help businesses to come up with solutions and answers that can help shape the future and success of the business for the better.

Learn the art and science of data analysis by signing up for an online data analysis short course to gain valuable knowledge that will help you along your career, regardless of the industry you’re in.