Data Analytics: A gentle introduction

The process of studying and interpreting massive data sets in order to derive valuable information and insights is known as data analytics. Making decisions based on data requires the use of a variety of tools and approaches that can be used to derive insights from data.

The development of artificial intelligence (AI) and machine learning (ML) has had a considerable impact on the field of data analytics in recent years. We will discuss the many layers of data analytics in this article, as well as how AI and ML enter into the equation.

Level 1: Descriptive Analytics

Descriptive analytics is the fundamental level of data analysis. The process of summarizing and describing the data is known as descriptive analytics. To comprehend the data, it includes using fundamental statistical methods, such as computing means and standard deviations. Typically, descriptive analytics is used to give a broad picture of the data, including spotting trends and patterns. AI and ML are not commonly used at this level.

Level 2: Diagnostic Analytics

Diagnostic analytics is the second level of data analytics. Diagnostic analytics is the process of delving into the data to identify the causes of specific events. It entails utilizing more sophisticated statistical methods like regression analysis and hypothesis testing. Diagnostic analytics is frequently used to pinpoint the source of an issue or comprehend the underlying causes of a specific result. At this level, AI and ML can be employed to carry out intricate studies and spot patterns that may be challenging for humans to notice.

Level 3: Predictive Analytics

Predictive analytics is the third phase of data analysis. Using historical data to forecast future events is known as predictive analytics. Advanced statistical methods, including time series analysis and machine learning algorithms, are used in this process. Making data-driven decisions and predicting future trends are typical uses for predictive analytics. At this level, creating predictive models and making forecasts heavily relies on AI and ML.

Level 4: Prescriptive Analytics

Prescriptive analytics is the fourth level of data analytics. Prescriptive analytics is the practice of using information to offer advice and prompt action. It makes use of sophisticated methods like machine learning algorithms, simulation, and optimization. The optimum course of action is often determined via prescriptive analytics. At this stage, data analysis and suggestions are done using AI and ML.

To summarize, data analytics is a multi-level process that uses a variety of tools and approaches to draw conclusions from data. Descriptive analytics is used to give a broad picture of the data at the most fundamental level. As we advance through the tiers, we delve deeper into the data to comprehend why specific events occurred, forecast what will happen in the future, and finally offer advice and take action. At the diagnostic, predictive, and prescriptive stages, when they are employed to carry out intricate studies and provide forecasts or recommendations, AI and ML enter the picture.

It’s important to note that a company’s decision to use a certain amount of data analytics will depend on the demands and goals of its particular sector. For instance, a business that wants to cut expenses might decide to concentrate on descriptive analytics, whereas a business that wants to create new goods or services would decide to concentrate on prescriptive analytics.

Additionally, there are difficulties in applying AI and ML to data analytics. Companies must invest in the infrastructure and tools required to enable AI and ML, such as data storage and processing capabilities, and must also make sure that their staff members are equipped with the knowledge and abilities required to use AI and ML.

To sum up, data analytics is a multi-level process that uses a variety of tools and approaches to draw conclusions from data. At the diagnostic, predictive, and prescriptive levels, where they are employed to carry out intricate studies and provide forecasts or recommendations, AI and ML come into play.

Enjoyed the read? Join our (free) thriving community of 50,000+ readers, including professionals from top companies like Apple, Amazon, Google, Meta, Microsoft, and more.