Types of Data Analytics and their Applications in Real World

Data analytics is a powerful tool that can be used to solve various business problems. Organizations of all sizes increasingly turn to data analytics to gain insights, improve decision-making, and optimize operations. In this blog we will talk about different types of data analytics to help you decide on picking the most suitable data analytics method for your business.

Data analytics is the method of using data to identify patterns and trends and extract valuable business insights, which can further contribute to decision-making and strategy formulation. 

Types of Data Analytics

• Descriptive Analytics
• Diagnostic Analysis
• Predictive Analytics
• Prescriptive Analytics

Descriptive Analytics

Descriptive analytics is used when the organization has a large data set on past events or historical events. For this data to be useful, it must be simplified and summarized so that it is understandable to the audience to which it is intended to be communicated.

Descriptive analytics involves using large data sets that are cleaned, transformed, and visualized understandably, usually in the form of dashboards, bar graphs, pie charts, infographics, and others.

Applications of Descriptive Analytics

Example 1 – An example of descriptive analytics is all the shopping information a supermarket chain collects daily. If you only look at the number of records produced daily, it is difficult to know how the business has operated with respect to certain attributes. With descriptive analysis tools, we will be able to know, for example, which products were the most sold, in which geographical areas certain items have sold the best, or if the marketing campaigns were successful compared to previous ones. Moreover, the organization can more effectively plan its inventories with that data.

Example 2 – Another example would be the granting of consumer credit, an automated business process. With descriptive data analytics, we could know how many requests the platform has processed, how many of these requests have resulted in a disbursement, how many were rejected for each type of cause, what is the average time that each activity of the process has lasted or how many have exceeded the stipulated time to complete them. In this way, the organization can measure how the process has been operating and thus deploy improvement actions that help it meet its objectives.

Diagnostic Analytics

Diagnostic analytics is the process of determining trends from the available data and establishing correlations between variables. In simpler words, diagnostic analytics can be called a process of analyzing – “Why did that happen“? Diagnostic analysis can be performed using MS Excel, algorithms, or even manually. The diagnostic analysis uses data insights from descriptive analytics and digs in to identify the reasons for the available outcomes and patterns.

Applications of Diagnostic Analytics

• Examining market demand based on consumer attributes and behaviour
• Explaining the user behaviour and further improving products and user experience
• Determining the cause of software or technology failures, also referred to as running diagnostics

Predictive Analytics

The amount of data we produce today has made it possible to popularize certain mathematical or statistical techniques and models that have been around for many years. Predictive analytics applies techniques such as statistical modelling, forecasting, and machine learning to the results of descriptive and diagnostic analytics to predict future outcomes.

Although predictive analytics do not predict the future 100%, because this type of analysis is probabilistic, they predict what might happen. Predictive analytics understand the correlations between variables and their behaviour in the future. Applying machine learning in predictive analytics has led to a certain degree of reliability in predicting an outcome.

Applications of Predictive Analytics

Aerospace – Aircraft engine health monitoring increases aircraft uptime and reduces maintenance costs.

Finance – Predictive analytics helps forecast the organisation's overall financial health by projecting expenses, sales, and profits to create a picture that helps in overall business decision-making.

Health – Analyzes the patients' overall health and devises an action plan for further treatment.

Industrial Machinery and Automation – Predicting machine failures, thereby preventing loss of life and costs, reducing downtime, and minimizing waste.

Retail – Predictive analytics is used to forecast the characteristics of potential customers who might be interested in purchasing certain products.

As organizations progress through the maturity levels in their digital transformation, predictive analytics will stop being an experiment and become a necessity.

Prescriptive Analytics

The prescriptive analysis goes beyond the previous three types of analytical methods. Prescriptive analytics answers the question – “What should we do? “Or” How can we make it happen?

First, it recommends courses of action that a company can take. In addition, it quantifies the effect of each of these actions to help make the best decisions in pursuit of the organization’s business objectives, such as entering a new market, locating a product in specific areas of a warehouse with better probabilities of sale, or mitigating a risk you may face.

Prescriptive Analytics is one of the crucial types of data analytics dedicated to the automation of decision-making, relying mainly on two essential disciplines:

• Business Rules Management Systems
• Optimization Mathematics

Applications of Prescriptive Analytics

• Calculate the sales forecast of a product to calculate the replacement orders of the merchandise.
• A customer’s buying propensity for certain items can be used to calculate which campaigns are best to launch for this customer.
• Using a customer’s credit score when evaluating the granting of a loan

Conclusion 

Previously, organizations based their decisions on the intuition of the most experienced people or the management cadre within the company. This is useful when you are experimenting with a new product or service, want to enter a new market that does not exist, or cannot obtain data to support decisions. Organizations are more focused than ever on making informed decision-making based on data analytics.

Companies can start this process with descriptive analysis and invest more in developing predictive and prescriptive analysis capabilities. Understanding the application of different types of data analytics and using them in the right context can help businesses make effective business decisions, understand their consumers better, and meet their business objectives.

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