Understanding 5 types of Data Analytics

Data analysis is an essential aspect of managing an effective organisation. Whenever data is used efficiently, it leads to a greater knowledge of a company’s past performance and more informed decisions about its future operations. Data can be utilised in a variety of ways at all stages of an operation of the company.

5 types of Data Analytics

Data analytics can be broadly divided into five types. It is crucial to understand that no specific type of analytics is superior to another. In fact, they coexist and enhance one another.

Descriptive Analytics

To better comprehend what is happening or what has happened, descriptive analytics describes or summarises the existing data utilising current business intelligence technologies. Standard reports, customer behaviour, customer profitability, historical rival activity, monthly profit and loss statements, and demographic data are examples of descriptive analytics.

Diagnostic Analytics

Focus on prior performance to ascertain what transpired and why. An analytical dashboard is frequently the outcome of the analysis. This kind of analytics often aims to go further into a certain cause or set of hypotheses based on the descriptive analytics. Diagnostic analytics delve deeply, looking into the costs of problems, whereas descriptive analytics cast a wide net to comprehend the breadth of the data.

Predictive Analytics

Predictive analytics transforms the data into useful and practical knowledge. Predictive analytics utilises data to estimate the chance of a condition arising or the likely course of an occurrence. In order to anticipate future events, predictive analytics uses a number of statistical approaches from modelling, machine learning, data mining, and game theory. These techniques evaluate both current and past data.

Prescriptive Analytics:

One of the three primary forms of analytics used by businesses to evaluate data is prescriptive analytics, alongside descriptive and predictive analytics. Although this sort of analytics is frequently referred to as a form of predictive analytics, its emphasis is slightly different.

Cognitive analytics:

It combines a number of clever technologies, including deep learning models, machine learning algorithms, and artificial intelligence to simulate the human brain and provide results that are similar to how people think.

Conclusion

In a nutshell, data analytics is the process of turning data into knowledge to improve decision-making. Obtaining actionable insights that lead to wiser decisions and improved business outcomes is the aim of data analytics. If you want to learn more about it, contact our team of experts at Princeton IT services.