The field of data analytics doesn’t just provide valuable insights into current trends, but it helps to predict what may occur based on past and current actions as well. There are four kinds of data analytics: descriptive, diagnostic, prescriptive, and predictive, which can be used independently, as well as in concert, for a company’s specific analytic needs. This article will take a closer look at the benefits of using predictive analytics, as well as how Tableau users can use predictive models.
What is Predictive Analytics?
Once the “What happened?” and “Why did it happen?” are asked in the analytic process, predictive analytics draws on the summarized data, as well as past trends and behaviors, to offer logical predictions on what may occur in the future. This branch of analytics uses statistical modeling to make forecasts that seek to answer the question: “What happens if?” The accuracy of these forecasted estimates depends on the quality of the data.
Unlike descriptive and diagnostic analytics, which are common to many businesses, predictive analytics is used less frequently. Not all companies have the desire or resources to implement predictive analytics, as it requires a combination of advanced statistical algorithms and machine learning. However, companies that use predictive models tend to see immense benefits, in terms of customer retention and satisfaction, as well as revenue.
Real-World Applications of Predictive Analytics
Organizations that rely on predictive analytics to tackle problems as well as to discover new opportunities receive many benefits from implementing this form of analytics. The following are some of the main uses of predictive analytics in various professional sectors:
- Healthcare: Perhaps the most widespread use of predictive analysis is in the healthcare industry. Health data plays a vital role in patient care; it helps doctors and healthcare providers get a full picture of a patient’s medical history, as well as any current illnesses they may be battling. This information plays an integral role in helping to diagnose patients. By looking at specific health factors, predictive analysis can be used to identify the underlying cause of a disease. This allows for early treatment options, which mitigate the negative health effects that can occur from waiting too long to diagnose and treat serious illnesses.
- Cybersecurity: Each year, billions of dollars are lost due to fraudulent activities. With the help of predictive analytics, activities that are deemed potentially fraudulent can be analyzed, and then predictive models can be generated to detect anomalies. This process allows fraud to be detected earlier based on patterns of suspicious financial activity.
- Hospitality: Predictive analytics plays an important role in the hospitality industry. It can help casinos and hotels predict their staffing needs for specific times, such as during the holidays or when a major sporting event or concert is in town. This not only ensures that venues will be properly staffed to handle crowds but helps to prevent overstaffing, which wastes revenue, affects customer service, and leaves employees feeling overworked.
- Real estate: Predictive analysis plays an important role in various aspects of the real estate industry. It has applications for real estate brokers, who can use it to offer potential homebuyers a projected home value so that their houses can be priced accordingly.
- Retail: Large retail chains use predictive analytics to learn everything they can about customers. They are interested in customers’ buying habits at various points in time, such as during the holidays or around natural disasters. Amazon incorporates predictive analytics to offer its customers personalized recommendations that are generated based on what they have previously purchased.
- Weather: Over the past several decades, weather forecasting has become much more accurate thanks to the use of predictive analytics. It’s now possible to provide weather forecasts a month in advance by analyzing historical data and satellite imagery. In addition, predictive analysis is a powerful tool for helping humans understand how global warming is impacting the planet. When paired with data visualizations, it’s possible to visually depict such trends as rising carbon dioxide and sea levels, as well as to forecast where these levels are headed. If enough information is gathered and interpreted, action can be taken to counter adverse effects.
- Entertainment content: Within the entertainment industry, digital entertainment options often use predictive analytic techniques to help transform viewer experience. Netflix uses predictive models to offer suggestions about which shows customers may be most interested in based on those they have previously watched.
- Sports: In order to stay competitive in professional sports, predictive analysis can be applied in a variety of ways. It can be used to forecast how valuable a player may be in the future, as well as to inform a team about how best to maximize their budget.
- Marketing campaigns: Applying predictive models to a business provides valuable insights into customer purchasing patterns, as well as responses to products and services. It also assists businesses with attracting and retaining profitable customers.
Using Tableau for Predictive Modeling
In Tableau, predictive modeling functions rely on linear regression to create predictive models capable of offering predictions based on the user’s data. There are two table calculations, MODEL_PERCENTILE (which is used to determine the posterior predictive distribution function and calculate the quantile of a given value between 0 and 1) and MODEL_QUANTILE (which is used to calculate the expected value at a given quantile or posterior predictive quantile) that supply surface relationships and prediction about data. In addition, these calculations have applications for spotting outliers, estimating values for omitted data, and offering predictions about future time periods.
Predictive models in Tableau are a powerful tool for enhancing visualizations. They rely on statistical engines to create models that are able to understand how your data is distributed around a trend line. There is no longer a need to integrate Tableau with Python or R to do so; instead, Tableau users can work with the predictive modeling function to make data predictions and incorporate them in table calculations. Tableau’s predictive modeling functions also allow users to choose targets and predictors when they update variables and create visualizations based on multiple models with various combinations of predators. In addition, it’s possible to filter, aggregate, and transform data to conform to any desired level of detail, and the model and subsequent prediction will automatically recalculate to match the data.
Tableau’s predictive modeling functions are able to support Gaussian process regression, linear regression, and regularized linear regression. Because each of these models is appropriate for different prediction types and use cases, they have different limitations. In order to decide which is right for you, consider the following:
- Gaussian process regression is most helpful for making predictions about a continuous domain, like time, in which a nonlinear relationship between the variable and prediction target is present. When using this form of regression in Tableau, it’s important to have a single ordered dimension as a predictor; however, it may also include various unordered dimensions as predictors as well.
- Linear regression is most suited for situations in which there are one or more predictors that have a linear relationship between the prediction and its target. It is helpful when these predictors are not influenced by the same condition and do not stand for two instances of the same data. This is Tableau’s default predictive model.
- Regularized linear regression is most effective in instances when there is a multicollinearity, or approximate linear relationship between at least two independent variables. This model has many applications for real-world datasets.
Using Tableau for predictive modeling empowers Data Analysts to extract actionable insights from data and apply them to make predictions about future events. Those who work with Tableau to create predictive models have the power to better understand the past and apply these insights to the future.