Data analytics is the process of analysing datasets to extract the insights they contain. Data Analytics enables Business Analysts to take raw data and reveal patterns in order to extract significant knowledge. Business Analysts use Data Analytics techniques in their work to make sound business decisions.
The use of Data Analytics in Business Analysis can help organisations better understand their customers’ patterns and needs. Finally, organisations can use various types of data analytics to improve their business performance and products. The business analytics course is divided into 4 categories which are:
Descriptive Analytics
It is the most straightforward of the top analytics categories. Descriptive analytics shuffles raw data from various data sources to provide meaningful insights into the past, i.e., it assists you in understanding the consequences of previous actions. However, without further explanation, these discoveries can only indicate whether something is correct or incorrect.
Making raw data justifiable to stockholders, investors, and leaders is a significant step. This makes it easier to identify and address flaws that need to be addressed. Data aggregation and data mining are the two fundamental procedures in descriptive analytics. This technique is useful for understanding the underlying behaviour rather than making any assumptions.
Diagnostic Analytics
Diagnostic analytics is one of four broad types of analytics used to determine why something happened in the past. Drill-down, data discovery, data mining, and correlations are some of the techniques used.
Diagnostic Analytics examines data to determine the primary causes of events. It is useful in determining what elements and events resulted in a specific outcome. The analysis generally makes use of probabilities, likelihoods, and the distribution of results. It provides comprehensive insights into a specific problem.
Predictive Analytics
Predictive analytics is one of four types of data analytics used by Business Analysts to predict what will most likely happen. It makes use of descriptive and diagnostic analytics discoveries to distinguish groups and exceptional cases and predict future patterns, making it an essential tool for forecasting.
Sentiment analysis is a key application of predictive analytics. All online media opinions are collected and analysed (existing text data) to forecast the individual’s opinion on a specific subject as positive, negative, or neutral (future prediction). As a result, predictive analytics entails developing and validating models that produce accurate predictions.
Prescriptive Analytics
Predictive analytics is the foundation of these types of data analytics used in business analytics. Nonetheless, it goes beyond the other three categories of analytics mentioned above to recommend future solutions. It can recommend all favourable outcomes based on a predefined game plan and propose a different course of action to achieve a specific result.
As a result, it employs a robust feedback system that continuously learns and updates the relationship between actions and outcomes. Prescriptive analytics employs cutting-edge technologies and tools, such as Machine Learning, Deep Learning, and Artificial Intelligence algorithms, making it easy to implement and manage. Furthermore, in order to provide users with favourable results, this cutting-edge data analytics type requires both internal and external historical data.
Conclusion
Business Analysts use four types of analytics to unlock the potential of raw data in order to improve business performance: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. You can also try Great Learning’s data science course content that is integrated by Corporate Leaders in the market. Start your learning today.Â
