Tableau Forecasting Examples

Introduction

A kind of statistical technique called prediction is used to forecast additional data points before they're measured. It is an important component of corporate intelligence, and it helps in determining options with enterprises. A number of the most popular tools for displaying information, Tableau, have extremely configurable and intuitive built-in predicting features. The following tutorial will lead you through Tableau's degree capabilities for forecasting and offer beneficial instances that ensure that you may become an experienced user of this effective tool.

Understanding the Basics of Forecasting

Tableau forecasting uses past data to project future patterns. To provide these forecasts, it makes use of complex statistical models, mostly based on exponential smoothing. A time series forecasting technique called exponential smoothing gives more weight to current observations by applying weighted averages to historical data points.

Essential Ideas in Predicting

• A series of time measurements is an amalgamation of points of information acquired at scheduled times (such as monthly sales data).
• A time series' direction is a longitudinal movement that demonstrates either an upward or downward inclination.
• Seasonality is the regular variations in a time series that are associated with the season, week, or month.
• Noise: Unpredictable changes in the data that the model is unable to explain.

Setting up Your Data for Forecasting in Tableau

It's important to prepare your data before beginning any predictions. For forecasting, Tableau needs a continuous date field (like Date or Year) and a measure (like Sales, Revenue). Make sure your data is comprehensive, well-organized, and easy for Tableau to understand.

An example dataset might be a retail company's monthly sales information for the last five years. The dataset may have columns like these:

• Date: The sales record's month and year.
• Sales: The month's overall sales amount.

Step-by-Step Guide to Forecasting in Tableau

Linking and Setting Up Data

• Connect to your dataset by opening Tableau. This might be an Excel workbook, CSV file, or a direct database connection.
• Once linked, drag the measure (like sales) to the Rows shelf and the appropriate date field (like date) to the Columns shelf.
• A time series line chart will be automatically created by Tableau.

Making Forecasts Possible

• Forecasting may be enabled by right-clicking on the graphic and choosing "Show Forecast."
• Tableau will provide a prediction in real-time using the available data. With confidence intervals colored, the prediction takes the form of an extension of the line chart.

Tailoring the Prognosis

You may modify the forecast model in Tableau to make it more suitable for your data.

Prediction Options

• The Forecast Options box may be accessed by right-clicking on the forecast and choosing "Edit".
• You may select between automated and custom models in the "Model" section. Tableau uses the automated model to choose the optimal model, and the custom option lets you specify the forecast duration and seasonality.

Time of Year

• Set the season duration manually if your data follows a known seasonal pattern (e.g., quarterly sales surges).
• Set the season duration to 12 months, for instance, if your sales data is seasonal and peaks every 12 months.

Forecast Intervals

• The range in which the actual future values are anticipated to fall is represented by prediction intervals.
• To change the width or narrowness of the intervals, you can change the confidence level (for example, 95%).

Understanding Forecast Output

It's critical to accurately understand the forecast's outcomes once it has been created.

• Point Forecast: The anticipated future values are shown by the middle line.
• Prediction Interval: The range of possible future values is represented by the prediction intervals, which are indicated by the shaded region surrounding the forecast line.
• Actual vs. Forecast: To evaluate the model's accuracy, compare the actual data points with the predicted values.

Best Practices for Forecasting in Tableau

• Prepare and tidy Data: Make sure your data is consistent and tidy. Outliers or missing data might skew the prediction. Before executing the prediction, appropriately address these problems.
• Recognize Seasonality: If your data exhibits seasonality, ensure that Tableau accurately detects and incorporates it into the prediction. If Tableau's automated recognition doesn't match your understanding of the data, you may manually modify the seasonality parameters.
• Customize Forecast Models: Although Tableau's default settings are frequently satisfactory, the particular context of your data may need you to alter the model type, smoothing parameters, or forecast duration.
• Assess Confidence Intervals: Tableau forecasts include confidence intervals that show the range of possible outcomes around the predicted values. Keep an eye on these intervals as they might provide you with information about how reliable the forecast is.
• Constantly evaluate and modify: Predicting is a continuous process. Update your projections whenever fresh data becomes available to check if seasonality or patterns have altered. By using an iterative process, you can be confident that your projections are accurate and up to date.

Advanced Forecasting Techniques in Tableau

Tableau provides sophisticated tools for those who want greater control over their projections:

• Custom Seasonality: You may manually adjust the seasonal cycle's duration if Tableau's automated seasonality doesn't match the patterns you know exist in your data.
• Forecast Length: Change the forecast length to fit your requirements, whether they are several years or a few months in the future. Longer projections should be used with caution since the level of uncertainty tends to rise.
• Exclude Data Points: To increase prediction accuracy, you can exclude data points that are outliers or unrelated to upcoming trends.
• Examine Forecast Descriptions: Tableau offers thorough explanations of the model it employed for forecasting. Understanding how the prediction was created is made possible by this transparency, which may be helpful when presenting the findings to stakeholders.
• Combining Predictions with Other Analyses: Cohort analysis, clustering, and what-if scenarios are just a few of the Tableau analyses that you may combine predictions with. This can provide you with a more comprehensive picture of your data and support you in making decisions based on data.

Advantages of Tableau Forecasting

User Friendly Interface

Tableau is widely known for providing a straightforward as well as simple-to-use architecture that makes it possible for individuals who have no expertise in technology to make the most of it.

Example: Suppose an industrial manager who doesn't seem knowledgeable in analytics for data. The manager doesn't need to comprehend intricate statistical models to quickly and simply generate a forecast using past sales data and Tableau. With only a few clicks, they can create forecasts and visually engage with the data thanks to the drag-and-drop feature.

Interactive Visualization

An advantage of Tableau's highly interactive visuals is that they let users interactively examine the data and projections.

For instance, a marketing analyst may use Tableau to build an interactive dashboard that displays the anticipated demand from customers for various items. The analyst may observe how different factors affect the prediction by engaging with the dashboard and filtering the data based on period, product category, or geography.

Combining Different Data Sources for Integration

Benefit: Extensive information integration has been made accessible by Tableau's accessibility to a multiplicity of different information resources, such as spreadsheets, relational databases, and services in the cloud.

For example, an accounting professional might want to develop an investment prediction using knowledge collected through many different places, which includes market movement news accounts, sales databases, and indicators of economic activity. They can combine all of these data sources into a single, coherent prediction model with Tableau, giving them a more precise and comprehensive picture.

Advanced Forecasting Techniques

Tableau employs advanced statistical models for forecasting, such as exponential smoothing, which can produce more accurate forecasts.

Example: To anticipate future inventory requirements, supply chain management might make use of Tableau's sophisticated forecasting techniques. They may account for trends and seasonal fluctuations by using exponential smoothing techniques, which improves inventory management and prediction accuracy.

Real-Time Forecasting

Tableau's real-time data integration and forecasting capabilities let companies react swiftly to changes.

For instance, a logistics business may anticipate delivery timeframes and track package deliveries in real time using Tableau. The organization may quickly modify its processes to prevent delays and raise customer satisfaction thanks to real-time forecasting.

Scenario Analysis

Tableau gives users the ability to do scenario analysis by changing variables and observing how various elements impact the projection.

Example: Using Tableau, a company's budget planner might do a scenario study in which they change different income and spending factors. They may produce several budget predictions and get ready for various financial scenarios by doing this, which will assist the business in making better judgments.

Tableau's Collaborative Tools

Teams may collaborate on predictions, exchange insights, and decide as a group thanks to these collaborative tools.

For instance, several divisions within a big company can work together on the same Tableau dashboard. For example, the finance team can provide their cost projections, while the sales team can offer feedback on anticipated revenue growth. A more thorough and mutually agreed-upon financial projection for the organization is the product of this partnership.

Configurable Dashboards

Tableau gives customers the ability to design highly configurable dashboards that facilitate the presentation of forecasts in a manner appropriate for certain business requirements.

An illustration of this would be a project manager creating a unique dashboard that showcases key performance indicators (KPIs) pertinent to their work. The management may successfully communicate this information to stakeholders by using the dashboard, which may contain projected deadlines, expenditures, and resource requirements.

Conclusion

Businesses may use Tableau's forecasting skills to make data-driven choices by using this powerful tool for anticipating future trends. You can fully utilize Tableau's forecasting tools by following the methods and examples in this article, regardless of whether you're predicting sales, website traffic, or financial performance.

You may generate accurate projections that will direct your company strategy by following the above-described stages and best practices. Refine your models continually and remain up to date on cutting-edge methods to keep your forecasts accurate and relevant.