2.2.3 Types of data analytics: descriptive analytics, prescriptive analytics and predictive analytics

Types of data analytics

Business Analytics is the process by which businesses use statistical methods and technologies for analyzing data in order to gain insights and improve their strategic decision-making.

There are three types of analytics that businesses use to drive their decision-making; descriptive analytics, which tells us what has already happened; predictive analytics, which shows us what could happen, and finally, prescriptive analytics, which informs us what should happen in the future. Whilst each of these methods is useful when used individually, they become especially powerful when used together.

1. Descriptive analytics

Descriptive analytics is the analysis of historical data using two key methods – data aggregation and data mining – which are used to uncover trends and patterns. Descriptive analytics is not used to draw inferences or make predictions about the future from its findings; rather it is concerned with representing what has happened in the past.

Descriptive analytics are often displayed using visual data representations like line, bar and pie charts and, although they give useful insights on its own, often act as a foundation for future analysis. Because descriptive analytics uses fairly simple analysis techniques, any findings should be easy for the wider business audience to understand.

For this reason, descriptive analytics form the core of the everyday reporting in many businesses. Annual revenue reports are a classic example of descriptive analytics, along with other reporting such as inventory, warehousing and sales data, which can be aggregated easily and provide a clear snapshot of a company’s operations. Another widely used example is social media and Google Analytics tools, which summarize certain groupings based on simple counts of events like clicks and likes.

Whilst descriptive data can be useful to quickly spot trends and patterns, the analysis has its limitations. Viewed in isolation, descriptive analytics may not give the full picture. For more insight, you need delve deeper.

2. Predictive analytics

Predictive analytics is a more advanced method of data analysis that uses probabilities to make assessments of what could happen in the future. Like descriptive analytics, prescriptive analytics uses data mining – however it also uses statistical modelling and machine learning techniques to identify the likelihood of future outcomes based on historical data. To make predictions, machine learning algorithms take existing data and attempt to fill in the missing data with the best possible guesses.

These predictions can then be used to solve problems and identify opportunities for growth. For example, organizations are using predictive analytics to prevent fraud by looking for patterns in criminal behaviour, optimizing their marketing campaigns by spotting opportunities for cross selling and reducing risk by using past behaviours to predict which customers are most likely to default on payments.

Another branch of predictive analytics is deep learning, which mimics human decision-making processes to make even more sophisticated predictions. For example, through using multiple levels of social and environmental analysis, deep learning is being used to more accurately predict credit scores and, in the medical field, it is being used to sort digital medical images such as MRI scans and X-rays to provide an automated prediction for doctors to use in diagnosing patients.

3. Prescriptive analytics

Whilst predictive analytics shows companies the raw results of their potential actions, prescriptive analytics shows companies which option is the best. The field of prescriptive analytics borrows heavily from mathematics and computer science, using a variety of statistical methods.

Although closely related to both descriptive and predictive analytics, prescriptive analytics emphasizes actionable insights instead of data monitoring. This is achieved through gathering data from a range of descriptive and predictive sources and applying them to the decision-making process. Algorithms then create and re-create possible decision patterns that could affect an organization in different ways.

What makes prescriptive analytics especially valuable is their ability to measure the repercussions of a decision based on different future scenarios and then recommend the best course of action to take to achieve a company’s goals.

The business benefit of using prescriptive analytics is huge. It enables teams to view the best course of action before making decisions, saving time and money whilst achieving optimal results.

Businesses that can harness the power of prescriptive analytics are using them in a variety of ways. For example, prescriptive analytics allow healthcare decision-makers to optimize business outcomes by recommending the best course of action for patients and providers. They also enable financial companies to know how much to reduce the cost of a product to attract new customers whilst keeping profits high.