7 common advanced analytics scenarios and resources for Tableau
As businesses continue to rely on data to make informed decisions, demand for advanced analytics has skyrocketed. Advanced analytics involves using techniques such as predictive analysis, machine learning, and data mining to uncover insights and patterns that are not easily visible. However, for businesses to fully realize the benefits of advanced analytics, they need a tool that can effectively visualize the insights and communicate them to key stakeholders. This is where Tableau comes in.
Tableau is a powerful data visualization tool that can perform advanced analytics. With its drag-and-drop interface, Tableau allows users to quickly and easily create interactive visualizations that can be used to explore data, identify trends, and communicate insights to others. Due to this, Tableau consulting services are on the rise, and businesses are quick to implement them. In this blog post, we will explore seven common advanced analytics scenarios and how Tableau can address them.
How Tableau can be used to perform advanced analytics
Scenario 1: Time-series and predictive analysis
Time-series analysis analyzes and identifies trends and patterns in data over a specific time period. In contrast, predictive analysis is used to forecast future trends and patterns based on historical data. You can easily perform both types of analysis thanks to Tableau’s advanced analytics capabilities. Tableau offers several tools, including forecasting models and trend lines, that enable you to visualize trends and predict future outcomes. You can also create calculated fields and use statistical models to make predictions and generate forecasts.
The dual axis plot shows the relationship between profit ratio and average discount.
Scenario 2: External services integration
Tableau allows easy integration with external services such as R and Python. This enables you to leverage machine learning algorithms to analyze and visualize data. Machine learning allows you to discover insights and patterns that would be difficult to uncover using traditional statistical methods. By integrating external services, you can also access a vast library of pre-built models and algorithms. This will help you quickly perform complex analyses and gain valuable insights.
Example of k-means clustering algorithm with R, visualized in Tableau.
Scenario 3: Geographical analysis
Tableau’s geographic mapping capabilities make location-based analysis easy. You can visualize data using maps, create custom territories and use a variety of spatial analysis tools. Tableau also integrates with geographic data sources such as GIS and Google Maps, providing access to location-based data. With these tools, you can easily identify trends and patterns in your data depending on location. You can make data-driven decisions based on regional insights.
Scenario 4: Text analytics
Text analytics enables you to extract insights from unstructured data such as social media posts, customer reviews and other text-based sources. Tableau offers several text analysis tools that allow you to visualize sentiment, identify key phrases and perform other types of text-based analysis. By combining text analytics with other data sources, you can gain a deeper understanding of your customers, competitors and market trends.
Scenario 5: What-if analysis
What-if analysis simulates scenarios and explores the impact of different variables on outcomes. Tableau makes it easy to perform what-if analysis by enabling you to create interactive dashboards that allow you to explore different scenarios and variables. By simulating scenarios and testing different variables, you can gain a deeper understanding of the factors that influence outcomes. This enables you to make data-driven decisions based on insights and predictions.
With this parameter-driven sales report, the user can explore the effect of quotas, commissions and salaries within the organization.
Scenario 6: Cohort analysis and segmentation
Cohort analysis enables you to group customers based on shared characteristics or behaviors, making it possible to analyze trends and patterns among different groups. Tableau offers several tools for cohort analysis, including calculated fields and visualizations that allow you to segment and analyze data into different categories. By performing cohort analysis, you can gain a deeper understanding of your customers and their behavior. This will enable you to personalize your marketing and improve customer retention.
This interactive clinic dashboard shows the number of patients on a particular day and time, what they came in for and how long they waited.
Scenario 7: Sophisticated solutions
Tableau offers several sophisticated solutions that enable you to perform complex analysis and gain deeper insights into your data. For example, Tableau’s Hyper engine allows you to analyze billions of rows of data in seconds. It enables you to perform real-time analysis and generate insights quickly. Tableau also offers several integrations with third-party tools and services. This allows you to leverage the power of AI and machine learning to perform advanced analytics and gain valuable insights.
A real-life example of the usage of Tableau
The New York Times, a leading American newspaper, uses Tableau for data journalism and visualization. The newspaper’s data journalism team uses Tableau to analyze and visualize complex datasets, providing insights for readers on topics such as politics, sports, and economics.
For instance, in the 2020 US presidential election, the New York Times used Tableau to create interactive maps and visualizations to help readers track the election results in real-time. The newspaper’s data team used Tableau to display results by state and county. This highlighted key battleground states and provided context for the election outcome.
By using Tableau for advanced analytics and visualization, the New York Times provided readers with a deeper understanding of complex topics. This has also made data-driven reporting more accessible and engaging.
The final say
To sum up, Tableau’s advanced analytics capabilities are a game-changer for businesses looking to make data-driven decisions. With the explosion of big data, it’s becoming increasingly important for organizations to make sense of the vast amounts of data they collect. That’s where Tableau comes in. By providing intuitive and powerful visualization tools, Tableau allows businesses to uncover insights and trends that would otherwise be hidden in the data.
The seven advanced analytics scenarios we discussed in this blog offer a glimpse into the wide range of features Tableau has to offer. From time-series and predictive analysis to cohort analysis and segmentation, each scenario represents a unique challenge businesses face when making sense of their data. But with Tableau, these challenges can be overcome and valuable insights can be gained. To gain a deeper understanding of Tableau, you can check out this webinar, which can help your business.
Whether you’re a data analyst, business intelligence professional, or decision-maker, mastering Tableau’s advanced analytics capabilities is a must-have skill in today’s data-driven world. This will enable your business to make better-informed decisions and stay ahead of the competition by leveraging the power of data.
