Analytics is defined as “the systematic computational analysis of data or statistics.” In other words, it is the science of analyzing data to draw conclusions from it.
There are four main types of analytics: descriptive, predictive, prescriptive, and big data analytics.
Descriptive analytics answers the question of “what happened?” It uses data to describe past events in order to better understand them and identify patterns. Predictive analytics uses past data to answer the question of “what could happen?” It employs statistical models and machine learning algorithms to make predictions about future events. Prescriptive analytics goes one step further than predictive analytics by answering the question of “what should we do?” It provides recommendations for actions that can be taken to achieve desired outcomes. Big data analytics is a type of descriptive analytics that deals with large amounts of unstructured data. It is used to identify trends and patterns in order to make better decisions.
Descriptive Analytics. Descriptive analytics is the simplest type of analytics and the foundation the other types are built on
Descriptive analytics is all about describing data. It answers the question of what happened in the past. Descriptive analytics includes both basic summary statistics (such as means, medians, and mode) and more sophisticated techniques (such as correlation and regression analysis).
Correlation analysis is used to identify relationships between two or more variables. For example, you might use correlation analysis to determine whether there is a relationship between how much someone smokes and how likely they are to develop lung cancer.
Regression analysis is a more sophisticated technique that can be used to predict future outcomes based on past data. For example, you might use regression analysis to predict how changes in the economy will affect your company’s sales.
Descriptive analytics can be used to answer questions such as: What was the sales last month? How many customer complaints were there last year? What was the average wait time at the customer service desk last week?
While descriptive analytics can provide valuable insights, it has its limitations. First, it can only tell us what happened in the past – it can not tell us why it happened or what will happen in the future. Second, descriptive analytics relies on accurate data – if the data is inaccurate, so too will be the results of any analyses conducted using that data.
Diagnostic Analytics. Diagnostic analytics addresses the next logical question, Why did this happen?
Diagnostic analytics is the process of analyzing data to identify the root cause of a problem. It is a type of advanced analytics that goes beyond simply describing what has happened in the past to explain why it happened.
The goal of diagnostic analytics is to help businesses find answers to complex questions by providing them with visibility into the underlying data and relationships. This type of analytics can be used to troubleshoot problems, improve decision making, and prevent future issues from occurring.
There are a number of different techniques that can be used for diagnostic analytics, including data mining, statistical analysis, and machine learning. The best approach will depend on the specific question being asked and the type of data available.
Data mining involves exploring large datasets to look for patterns or correlations that might provide insight into a problem. This technique can be used to identify unusual behavior or trends that could be indicative of a issue. Statistical analysis is another common approach that can be used to identify relationships between different variables. This technique can help businesses understand how changes in one area might impact another area within their operations.
Machine learning is a more sophisticated form of diagnostic analytics that uses algorithms to automatically detect patterns in data. This approach can be used to predict future events or outcomes based on past behavior.
Predictive analytics has been used for many years in a variety of industries, but it has only recently become more widely available due to advances in technology and data collection methods. The rise of big data has made it possible to collect large amounts of data more efficiently, and the use of machine learning algorithms has made it possible to automatically detect patterns and relationships in this data.
Predictive analytics can be used for a variety of purposes, such as predicting customer behavior, detecting fraud, or forecasting demand. In each case, predictive models are built using historical data and then used to make predictions about future events. These predictions can then be used to make decisions about what actions to take in order to achieve a desired outcome.
Predictive analytics is a powerful tool that can be used to improve decision-making in many different areas. However, it is important to remember that predictive models are only as good as the assumptions they are based on and the quality of the data they are trained on. As such, it is important to carefully consider both when building predictive models and when using them for decision-making.
There are many different types of prescriptive analytics algorithms, each designed to tackle a specific type of problem. Some common examples include:
Linear optimization: This algorithm is used to find the best way to allocate resources in order to achieve a specific goal. For example, it could be used to determine the most efficient route for a delivery truck or the mix of products that will maximize profits.
Constraint satisfaction: This algorithm is used to find solutions that meet all the constraints imposed on a problem. For example, it could be used to schedule workers shifts so that everyone has the required number of hours while still being able minimize overtime costs. Predictive modeling: This algorithm builds models that predict future events based on past data. For example, it could be used to forecast demand for a product so that inventory can be stocked accordingly. Decision trees: This algorithm creates diagrams that show how different decisions lead to different outcomes. For example, it could be used by a bank considering whether or not to approve a loan application.