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  • Writer's pictureMarty Pavlik

Process Mining vs. Analytics (or Business Intelligence)?

The answer is both!!


Process mining and analytics are two powerful techniques for gaining insights into business processes, but they differ in their focus, methodology, and application. As Doculabs engages with clients adopting process mining technology, many struggle with questions like: Why do I need process mining if I have BI or analytics already? In this blog, we will explore the differences between process mining and analytics, and how they can be applied in real-world scenarios, using Power BI and Celonis as examples. Ultimately this is not an either / or question, the objective should be how to combine the two powerful toolsets to drive the best business results.



Process Mining vs. Analytics: Understanding the Differences

Process mining is a technique for analyzing business processes based on event logs and other process data. The goal of process mining is to provide insights into how processes actually work in practice, as opposed to how they were designed to work or how they are supposed to work in theory. Process mining connects key data points for your business with their associated process or processes, adding an additional level of insight. Process mining can help organizations identify inefficiencies, bottlenecks, and other issues that may be impacting performance, and provide actionable insights for improving processes.

Analytics, on the other hand, is a broader term that refers to the use of data and statistical methods to gain insights into a variety of business-related phenomena. Analytics can be applied to many different types of data, from customer behavior to financial data, and can be used to answer a wide range of questions about business performance. Analytics provides data points, KPIs, and metrics as of any point in time; e.g. monthly, quarterly, annually. While both process mining and analytics both involve the analysis of data, they differ in their focus and methodology. Whereas analytics can be applied to many different types of data to produce metrics, KPIs and trend analysis, process mining connects those data points to the task and activities within the processes that drive them. Process mining uses event logs and other process data, combined with other data attributes to gain insights into how processes operate in the real world. , while analytics can use a wide range of data sources and statistical methods to gain insights into a variety of business-related metrics at various points in time. Simply put analytics can tell you that something is performing well or not, process mining can help to tell to why.


Power BI vs. Celonis: Two Examples of Process Mining and Analytics Tools

To illustrate the differences between process mining and analytics, let's consider two popular tools: Power BI and Celonis.

Power BI is a business intelligence tool that allows users to visualize and analyze data from a variety of sources. Power BI provides a wide range of visualizations and interactive features that can be used to gain insights into data, such as charts, graphs, and tables. Power BI is a powerful tool for analytics, as it allows users to combine data from multiple sources and gain insights into many different aspects of business performance.

Celonis, on the other hand, is a process mining tool that provides insights into how processes operate in practice. Celonis uses event logs and other process data to create visualizations of business processes, such as process maps and flowcharts. These visualizations can be used to identify bottlenecks, inefficiencies, and other issues that may be impacting performance, and provide actionable insights for improving processes.

While Power BI and Celonis both provide powerful tools for gaining insights into business data, they differ in their focus and methodology. Power BI and other analytics tools build their own databases for collecting, storing and analyzing data to produce key metrics, while Celonis combines key process activity data with the same source data used for analytics to provide a more comprehensive level of analysis and insights.


Ownership

The ownership of process mining versus analytics in a company ultimately depends on the company's organizational structure, culture, goals, and needs. In some companies, both process mining and analytics may fall under the same department or team, while in others, they may be separated into different departments or teams.

Advantages of having process mining and analytics under the same ownership include:

Seamless integration: If process mining and analytics are owned by the same team, it can lead to better collaboration and seamless integration of the two disciplines, resulting in more accurate insights and recommendations.

Consistency: Ownership by the same team can ensure consistency in data analysis and interpretation across both process mining and analytics, leading to better alignment of insights with the company's goals.

Efficiency: Having process mining and analytics under the same ownership can lead to more efficient use of resources and time, as the same team can handle both disciplines.

Disadvantages of having process mining and analytics under the same ownership include:

Limited focus: Ownership by the same team (especially if within a single business unit) may result in a limited focus on one area over another, which could potentially lead to overlooking important insights or opportunities and limit enterprise impact.

Skillset: The skillset required for process mining and analytics can be quite different, and it may be challenging for one team to possess both skillsets. Staffing the right levels of depth and breadth of business knowledge within a single team can be challenging.

Accountability: Strong governance and effective organizational structures are needed to ensure accountability across multiple business areas.


How do we determine the best tool and ownership?

Generally, Doculabs would start by assessing the client's current organizational structure, capabilities, and goals related to process improvement and data analysis. We would also consider factors such as the size and complexity of the organization, the types of processes being analyzed, and the existing tools and technologies being used.

Based on this assessment, Doculabs may recommend different ownership structures for process mining and analytics. For example, we may suggest that process mining be owned by a dedicated process improvement or operations team, while analytics is owned by a centralized data analytics or business intelligence team. Alternatively, we may suggest a more hybrid approach where both process mining and analytics are owned by a cross-functional team or center of excellence.

The advantages of a dedicated team owning process mining may include a stronger focus on process improvement and a deep understanding of the underlying business processes. Having analytics owned by a centralized team may provide more consistency and standardization in data analysis across the organization.

The centralized vs. distributed positioning of the process mining and analytics groups is a topic requiring further analysis in a future blog.

Ultimately, Doculabs would work with the client to identify the best ownership structure based on their unique needs and circumstances and help them establish the necessary processes and governance to ensure success.


How do you communicate the differences and symbiotic relationships internally?

To communicate the difference between analytics and process mining through an organization, we recommend using the following methods:

Clear definitions: First, provide clear definitions of both analytics and process mining, highlighting the differences between the two. For example, explain that analytics involves using statistical methods to analyze data and derive insights, while process mining focuses on analyzing process data to identify inefficiencies, bottlenecks, and opportunities for improvement.

Examples: Next, provide concrete examples of how analytics and process mining are used in different parts of the organization. For instance, describe how analytics can be used to analyze sales data to identify customer trends and preferences, while process mining can be used to identify bottlenecks in the production process.

Benefits: Highlight the benefits of both analytics and process mining, emphasizing how they can help the organization to achieve its goals. For example, explain how analytics can help to increase revenue and profits, while process mining can help to improve efficiency, reduce waste, and enhance customer satisfaction.

Training: Finally recommend providing training opportunities for employees to learn about analytics and process mining, and to understand when to use each method. This can help to build a culture of data-driven decision-making throughout the organization and ensure that everyone understands the differences between these two important methods.

Conclusion

Process mining and analytics are two powerful techniques for gaining insights into business data, but they differ in their focus, methodology, and application. Process mining is specifically focused on combining business data with process data, while analytics can be applied to produce key KPIs, metrics and trends. Understanding both the differences between process mining and analytics and the interdependence between the tools can help organizations choose the right tools for their needs and gain the insights they need to improve performance and achieve their business goals.

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