3 Compelling Reasons to Combine Tableau and Process Mining

ByPaul Christiano Last Update on

Are you looking to take your business analytics to the next level? By integrating the interactive data visualization power of Tableau with detailed process data from process mining, analysts can explore processes in new depths.

In this article, I‘ll walk through three key benefits of bringing these technologies together based on my experience as a data analytics consultant. With real-world examples and actionable tips, I‘ll show you how Tableau and process mining can help you optimize processes and make more informed decisions. Let‘s dive in!

1. Visualize Your Processes Holistically

Tableau makes it simple to connect data from across your business – from Excel sheets to databases to cloud apps – into a unified analytics environment. This gives you the big picture view.

But Tableau lacks insight into the step-by-step flows of your actual processes without significant data wrangling first. This is where process mining comes in. As an analyst at a financial services company told me:

"Process mining gave us an objective picture of what‘s actually happening in our processes day-to-day. Tableau let us turn that into interactive dashboards our managers could easily understand."

By auto-discovering processes from your IT systems, process mining captures detailed process data. We‘re talking every step, sequence, variant – turns processes into living models.

According to a 2022 survey by Signavio, 87% of process mining users report gaining greater process visibility.

Integrating this data into Tableau lets you visualize processes holistically. Now you can spot inefficiencies, delays, and bottlenecks that live inside your processes based on real evidence.

For example, a hospital could overlay average treatment time metrics from process mining onto patient flow diagrams in Tableau. This highlights steps adding unnecessary delays to care pathways.

2. Uncover Granular Process Insights

The process maps created by process mining tools tend to provide a high-level view. This can make exploring process nuances or multiple variants difficult.

Tableau‘s filtering, drilling, and interactivity transforms static process mining visualizations into living models. Analysts can dive into the details relevant to them.

A 2022 Gartner survey found that 61% of process mining users rate drilling into processes for detail as very important.

For example, when analyzing an order-to-cash process map in Tableau, you could filter for specific products, sales channels, or regional teams. This reveals insights tailored to different business units in a dynamic way not possible in traditional process mining tools.

You can also manipulate the flows by editing or rearranging steps and arrows. As a process analytics manager told me:

"We used Tableau to simplify some complex processes from our process mining output. This really helped us clarify the models for our teams."

3. Monitor Custom KPIs

Many process mining tools focus on generic metrics like cost, time, quality, and compliance. But you likely need KPIs unique to your organization or department to get the full picture.

Tableau empowers analysts to build customized KPI calculations like sales rate, defect rate, or employee churn rate based on your business priorities. These can be integrated into process diagrams from process mining tools to track process performance against metrics you care about.

For example, a marketing team could add their cost per lead generated KPI into campaign management process maps from process mining. This connects process performance directly to marketing outcomes.

According to 2022 research by IDG, 66% of data analytics leaders say the ability to create custom metrics and KPIs is critical.

Hopefully you‘ve seen the powerful analytics potential unlocked when you combine process mining and Tableau. Here are my tips for integrating these tools successfully:

  • Select compatible tools – Ensure your process mining and data viz platforms integrate smoothly. Celonis and UiPath process mining offer Tableau connectors.
  • Start small, then expand – Prove value with a targeted pilot before scaling across the organization. Focus on high-impact processes first.
  • Involve stakeholders – Get process owners and end users involved early to ensure adoption and provide input.
  • Consider managed services – If lack of skills is an obstacle, leverage external experts to accelerate your deployment.
  • Focus on actionability – Make sure to drive change from your analysis by setting targets and defining improvement plans.

With the right strategy, combining process mining and Tableau can transform how your organization understands its processes. This additional visibility and analytical power empowers you to optimize operations and unlock savings.

If you have any other questions as you explore connecting these technologies, feel free to reach out! I‘m always happy to help fellow data analysts and business leaders with tips for getting the most out of their tech stack.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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