A massive volume of data is handled at various levels of business analytics. The four types of analytics are often applied in phases, and no one kind of analytics is superior to the others. They are interconnected, and each one provides a unique perspective. With data becoming so crucial in so many different industries, from manufacturing to energy grids, most businesses rely on one or more of these forms of analytics. There are various Post Graduate Program in Data Science and Business Analytics that offer great career options.
1. Descriptive Analytics
It is the most fundamental type of analytics. The massive size of big data is beyond human comprehension; hence the initial stage entails processing the data into manageable bits. This form of analytics aims to summarize the findings and comprehend what is going on. It is estimated that 80 percent of business analytics consists primarily of descriptions based on prior performance aggregations. It is a critical step in making raw data understandable to investors, shareholders, and management. It makes it easier to identify and handle areas of strength and weakness, which can aid in strategizing.
2. Diagnostic Analytics
Diagnostic analytics is used to figure out why something occurred in the past. Drill-down, data discovery, data mining, and correlations are some of the approaches used. Diagnostic analytics examines data in more depth to determine the underlying causes of events. It aids in determining what circumstances and events influenced the outcome. The analysis is based chiefly on probabilities, likelihoods, and the distribution of results. Diagnostic analytics would help you understand why sales have decreased or increased for a given year or so in time-series sales data. However, it only provides a backward-looking comprehension of causal links and sequences.
3. Predictive Analytics
A predictive model builds on the early descriptive analytics stage to extract the possibility of the outcomes. Predictive analytics aims to create models that use existing data to extrapolate future occurrences or anticipate future data. One of the most prominent applications of predictive analytics is sentiment analysis. All social media opinions are collected and analyzed to predict a person’s positive, negative, or neutral sentiment on a specific subject. For learning and testing data, predictive analytics uses machine learning algorithms such as random forests, SVM, others, and statistics.
4. Prescriptive Analytics
It is based on predictive analytics, but it extends beyond the three listed above to identify future solutions. It can offer all beneficial outcomes based on a specific path of action and multiple courses of action to achieve a particular conclusion. As a result, it employs a robust feedback system that constantly learns and adjusts the relationship between action and outcome. Optimization of some functions linked to the intended output is included in the computations. As a result, it optimizes the distance for a quicker arrival time. Recommendation engines also use prescriptive analytics.
The Bottom Line
The four analytics methodologies may appear to require sequential implementation. However, in most cases, businesses can proceed immediately to prescriptive analytics. Most companies are aware of or are already employing descriptive analytics. Still, if a crucial area has to be optimized and worked on, prescriptive analytics must be used to get the desired result. Click here to learn more about the Full-Stack Web Development course.