Predictive analytics is used in multiple industries to conduct various types of advanced analysis about future outcomes or otherwise unknown events. Predictive analytics deals with predicted variables from past occurrences and exploits them to predict unknown outcomes. However, the accuracy and usability of these predictions depend on the level of data analysis and the quality of assumptions.
Predictive analytics software is used in technology management, planning and decision making, marketing campaign optimization, risk assessment, market analysis, and fraud detection. It provides a predictive score (probability) for each individual customer to determine, inform, or influence organizational processes.
The predictive analytics process involves defining the project outcomes and business objectives, collecting and preparing data from multiple sources, analyzing the data by inspecting, cleaning, and modelling useful information, providing statistical analysis to validate assumptions, creating accurate predictive models about the future, deploying analytical results to generate possible outcomes, and monitoring models to review the performance of the model.
Importance of Predictive Analytics
The use of predictive analytics is increasing because of a better understanding of its value, availability of the required computing power, and the expanding toolset that allows it to be conducted. Predictive analytics tools can be used for both traditional data sets as well as Big Data. Advanced analytics in Big Data is used to unveil new patterns, subtle correlations, and new trends to develop better decision-making processes.
Predictive analytics is used in numerous ways to enhance business productivity in different areas such as marketing, financial services, insurance, telecommunications, actuarial science, retail, travel, mobility, healthcare, pharmaceuticals, capacity planning, social networking, and many more. Predictive analytics is a key element in search advertising and recommendation engines as it uses historical data to make predictions and convert them into actionable insights.
The predictions made using predictive analytics allow decision-makers in a business to implement a plan of actions to deal with all probabilities. Analytics are business-oriented by nature and far from statistical research—where one can analyze historical data.
Two techniques are used to conduct predictive analytics–regression and machine learning.
In regression techniques, the focus lies more on establishing a mathematical equation as a model to represent the interactions between the different variables under consideration. Depending on the situation, several models can be used for predictive analytics, such as linear regression model, discrete choice models, logistic regression, multinomial logistic regression, probit regression, time series models, survival or duration analysis, and multivariate adaptive regression splines.
Machine learning is a branch of artificial intelligence. It can be used in a variety of fields including medical diagnostics, credit card fraud detection, face and speech recognition, and stock market analysis.
Limitations of predictive analytics
For predictive analytics, it is important to have a clear definition of the concept that is to be predicted. Additionally, training data obtained from past trends must be stable over the observed time period because otherwise, it may not be useful for accurately predicting future test data. In contrast to predictive analytics, which helps improve the effectiveness of future decisions, descriptive analytics provides insights about what has happened in the past. Descriptive analytics receives information from past events while predictive analytics predicts the future based on obtained historical data. Descriptive and predictive analytics are collectively known as ‘knowledge discovery in data’ (KDD).