What does the term “data analytics” mean? It is the science of analyzing and interpreting data to derive knowledge that can be applied to enhance performance. The modern company relies on data analytics to answer critical concerns that affect their companies.
Data analysts use a multitude of strategies to transform raw data into intelligence. These four data analytics categories are considered the most typical ones.
Descriptive analytics is the practice of using data to explain events that have occurred or current events. Data analysts track real-time occurrences and look at trends over time in any industry using both historical and current data.
Descriptive analytics are used by businesses to contextualize their data. For illustration, a company might report $100,000 in product sales for the second quarter. According to a descriptive analytics study, sales may have decreased from the first quarter but increased from the second quarter of the before year. That knowledge can be used by decision-makers to address the advantages and disadvantages of their business units.
Diagnostic analytics focuses on why something occurred, whereas descriptive analytics explores what happened. Businesses use diagnostic analytics to identify successes, solve issues, and make better decisions.
The data analytics firm Selerity describes diagnostic analytics as an advanced subset of data analytics that employs regression analysis, data mining, data drilling, and other methods to uncover relationships and causes in data. Decision-makers can view their data more contextually with diagnostic analytics, which builds on the insight gained via descriptive analytics.
Data analysts use predictive analytics to determine what will occur.
By analyzing previous and present data, predictive analytics aims to assess the likelihood of future outcomes.
For decades, organizations have been utilizing data to create projections and predictive models, so predictive analytics is no longer a novel concept. However, as AI, machine learning, data mining, and other technologies have advanced, models have become easier to create and more capable of handling massive volumes of data. This has led to an increase in the usage of predictive analytics. These technologies also help generate predictive models from the different structured and unstructured data that firms today gather.
What should we do? is what an authoritarian analytics model aims to answer. Data analysts look into data to make recommendations for potential solutions and decide what steps should be performed. Data analytics is a field that is expanding.
Prescriptive analytics, like predictive analytics, uses some of the newest techniques and equipment in data science, including machine learning, neural networks, and graph analysis. Prescriptive analytics can assist decision-makers in setting a business course based on data-driven projections rather than intuition or hunches when used successfully and with the correct data.