Data is in the thick of everything businesses do in the 21st century. Data is used to make decisions for the present and finds use in predicting the future. When organizations can predict business-related events, planning and strategizing become easier. Hence, predictive data analytics has become vital to businesses.
Organizations that underestimate the importance of predictive analytics look forward to churning customers. Furthermore, such companies inadvertently undermine their business. Hence, predictive data analytics is critical to success, whether your company is in the automotive, aerospace, manufacturing, law, finance, etc.
So, it’s essential to take the information in this article seriously. Let’s start by properly defining predictive data analytics.
What Is Predictive Analytics?
Predictive data is used in analytics, coupled with algorithms and machine learning techniques to determine the probability of future events occurring based on past data trends. Predictive analytics aims to understand the rudiments of why past events happened, to predict future events to a high accuracy degree.
In predictive analytics, both past and current data are used. The probabilities these data are used to identify are; opportunities, risks, trends, behaviors, etc. Predictive analytics of recent years leverage technologically-powered concepts like data mining, artificial intelligence, machine learning, and statistical modeling. All these combine to do the predictive data analytics work.
How Do Predictive Analytics Work?
Leveraging known insights, a model is developed. The model is then used in predicting new data values. The results from the models are predictive values representing the probability of a target variable.
It’s vital to note that predictive data analytics and its models differ from descriptive analytics. The latter aims to understand events. In the same vein, predictive analytics differ from diagnostic models, which aim to understand relationships in determining why an event happened.
Predictive data analytics rides on two main categories of models; classification and regression models. Classification models focus on soft and behavioral details, while regression models work for numerical information.
Every predictive modeling technique falls in either classification or regression analytics. Hence, the rest of this section explores the common predictive data analytics.
This s a prevalent statistical method. The method analyses the relationship between variables. The main aim of this method is to establish continuous data that is predicted to follow a particular distribution.
With this model, analysts identify patterns in vast data sets. Commonly, this model is used to determine how much influence factors like price have on an asset. In regression analytics, the resultant predictive number is called the ‘response’ or ‘Y variable.’
There are five types of regression:
- Linear regression: uses one independent variable to predict a ‘response.’
- Multiple regression: uses more than one independent variable to denote a ‘response.’
- Logistic regression: known variables are used to predict unknown variables of discrete variables.
- Binary Logistic regression: there are only two values in this case; 0 and 1.
- Multiple logistic regression: the response, in this case, has several levels; 1, 2, and 3, or low, medium, high.
Decision trees fall under the classification model of predictive data analytics. Here, data is categorized based on the categories of the variable input. Thanks to this, you can better understand how an individual arrived at a decision.
The popularity of the decision tree is based on the ease of understanding. The tree breaks data into logical groups that are easy to comprehend.
Decision trees can also handle lost values and also find use in essential variable selection. Hence, if multiple values are missing from your data set, a decision tree helps you quickly map things visually.
These are sophisticated predictive analytics techniques that can handle complex relationships. What makes this model popular is the degree of complexity it can take and its flexibility. The primary use of neural networks is in confirming ‘response’ from decision trees or regression.
AI powers neural networks. With AI, the system focus on prediction rather than explanation.
Other Predictive Data Techniques
- Bayesian Analysis
- Ensemble models
- Gradient boosting
- Incremental response
- K-nearest neighbor
- Partial least squares
- Memory-based reasoning
- Principal component analysis
- Support vector machine
- Time series data mining
How to Apply the Predictive Analytics Process
The following steps help you maximize any predictive data analytics process.
- Defining the objective: Start by identifying what you want to achieve with predictive analysis. Then, determine the expected project outcome and available data resources. Also, ensure that your data is updated.
- Data collection: Ensure you have a vast amount of past data to use.
- Data analysis: Ensure that the data is clean and properly inspected. In this process, you’ll discover critical information. You may need to employ data mining techniques at the data analysis stage.
- Statistical tools: You can put your hypothesis to test and subsequently validate it with these tools. Statistical tools here include; multivariate statistics, forecasting, and regression analysis.
- Generate a Model: Predictive analytics aims to automate repetitive processes. You can use a programming language like python to achieve this. First, consider various tools, then decide what works best for you.
- Deploy results: Upon subjecting your variables to statistics and models, interpret the results, and use them to inform actionable decisions.
- Progress monitoring: Predictive analytics is a continuous process. Constantly review your model, its functionality, and its efficiency.
Importance of Predictive Analytics
When the predictive analysis is done right, your organization can make reliable, concrete decisions. Hence, you won’t waste resources on elements that can’t contribute to company growth. Here are more benefits of predictive data analysis.
- Helps your organization forecast future needs.
- It helps your organization improve the experience for the customers.
- It helps your organization detect crime.
Use Cases for Predictive Analytics
Predictive analytics is used across industries. However, the application differs. Let’s consider how some industries use predictive analytics:
- Automotive: used to determine strengths and weaknesses in future vehicle manufacturing plans. Also used in developing driver assistance features
- Manufacturing: to predict details about machine failures
- Finance services: help financial services to predict credit risks across accounts
- Aerospace: used to determine fuel usage and the reliability of an aircraft.
- Weather: to predict the weather, while driving further insight into the climate
- Energy: to determine weather’s impact, regulations, equipment failure, etc.
- Retail: to gain sales leverage against the competition and improve customer experience
- Law enforcement: to predict neighborhoods that’ll need extra protection.
- Investment: Predictive data also finds use in investment analysis tools.
Predictive data analytics plays a significant role in 21st-century business. As a stakeholder of an organization, ensure you have a team member well-versed in conducting predictive analysis. Lacking in this area can cost your business market share.