What are the four main types of data analysis?

We’re big on data analysis at Permea, and if you’ve been following our blog for a while, you’ll definitely have heard us talk about a wide variety of types of analytics – but what are the main branches within this field, what exactly is the difference between them? In this blog post, we’ll unpack the four primary types of data analytics and explore how they can be used in real-world contexts to shake up the future of healthcare.

In data analytics, there are a few main types of analysis: predictive, descriptive, diagnostic, and prescriptive analytics. Although they sound similar, there are a number of subtle differences in how they operate which affect the direction of the insights they deliver. Descriptive analytics, for example, tells you what happened in the past, detailing the technicalities of a certain event. Diagnostic analytics, on the other hand, helps us understand why something happened in the past, providing an explanation for this occurrence based on descriptive analytics. The two operate separately, but often find themselves overlapping when trying to determine a specific series of events and their causes, such as within a clinical trial setting.

Predictive analytics explores what is likely to happen in the future – essentially, predictions and estimates will be made based on the information we already know. Conversely, prescriptive analytics recommends certain actions that can be made in order to influence those outcomes, making them more likely or unlikely to happen. For example, in the case of drug development, predictive analytics might provide research and development teams with key insights into the predicted success rate of certain medications. If a new drug is predicted to be unsuccessful based on a number of specific factors, prescriptive analytics will determine potential changes that could be made in order to increase the likelihood that the drug will receive a positive response.

There are myriad ways in which these different branches can be used together in order to create the best possible outcome for a variety of different stakeholders and patients. By using descriptive and diagnostic analytics to establish why something happened as it did, data analysts are able to use these valuable insights in order to employ prescriptive and predictive analytics and reach their desired targets and goals.

For example, using a combination of these four models of data analysis within the context of the clinical trial process might allow research and development teams to determine why a particular product candidate received the response it did, enabling drug developers to either repeat these practices or avoid these mistakes accordingly throughout the next trial. Not only with this save substantial amounts of time on money on the part of the pharmaceutical company, but it will also result in an end product that is effective and efficient – and that patients will actually want to use. 

Data analytics in healthcare is a complex and fast-changing field, and these are just a handful of examples of the way in which this exciting technology can be harnessed. By adapting existing models within the biotech and pharmaceutical sectors to embrace the power and potential of these four main types of data analytics, we’re opening the door to a more progressive and forward-thinking future within the drug development and wider healthcare industry.

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