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Process Analytics - Build or Buy?

Lari Numminen

April 6, 2023

In this article we compare the advantages and disadvantages of building vs. buying process analytics. In other words, we compare the option of building a process analysis solution with business analytics resources and methods against the option of buying process intelligence software.

What is process analytics?

Process analytics is the practice of using data-driven techniques and tools to analyze, monitor, and optimize business processes within an organization.

Process analyticsinvolves collecting and examining data from various sources, such as event logs, transaction records, or system logs, to gain insights into the performance, efficiency, and effectiveness of processes. The goal of process analytics is to identify inefficiencies, bottlenecks, and areas for improvement, ultimately leading to better decision-making, increased productivity, and cost savings.

Key aspects of process analytics include:

  1. Data collection. Process analytics starts with collecting relevant data from various sources, such as system logs, databases, or direct observation. This data can include information about tasks, activities, resources, durations, and outcomes related to a specific business process.
  2. Data preprocessing and transformation. The collected data is often raw and unstructured, requiring preprocessing and transformation to make it suitable for analysis. This can involve cleaning, filtering, aggregating, or normalizing the data to ensure its accuracy and consistency.
  3. Analysis techniques. Process analytics employs various data analysis techniques, such as descriptive, diagnostic, predictive, or prescriptive analytics, to gain insights into process performance. Techniques can include statistical analysis, data mining, machine learning, or process mining, depending on the specific goals and requirements of the analysis.
  4. Performance measurement. Using the analyzed data, process analytics calculates performance metrics and Key Performance Indicators (KPIs) to evaluate the efficiency and effectiveness of the business process. These metrics can include cycle times, throughput, resource utilization, error rates, and customer satisfaction scores.
  5. Insight generation and recommendations. Process analytics aims to generate actionable insights that can help organizations improve their processes. By identifying inefficiencies, bottlenecks, or deviations from best practices, analysts can develop targeted recommendations for process improvement, such as streamlining tasks, reallocating resources, or implementing new technologies.
  6. Monitoring and continuous improvement. Process analytics involves ongoing monitoring of process performance to ensure that improvements are effective and sustainable. By fostering a culture of continuous improvement and regularly reviewing and updating processes, organizations can adapt to changing business needs and maintain optimal process performance.
Process analytics example - process variations visualization

Building Process Analytics with Tableau/PowerBI etc

If you're planning to adapt process analytics in your business one option is to build your own solution using a business intelligence (BI) tool such as Tableau or Power BI leveraging some open source or commonly available process analysis modules, such as process mining algorithms available in python and process modeling visualization standards defined by BPMN.

There are advantages and disadvantages to this approach.

Advantages of self-built process analytics

  1. Customization: Building process analytics dashboards in-house allows for greater flexibility and customization to meet the unique requirements and preferences of the organization. You can tailor the dashboards to specific business processes, data sources, and metrics, ensuring a better fit with your organization's needs.
  2. Integration: In-house dashboards can be more deeply be integrated with existing systems, databases, data lakes and applications, as the development team has in-depth knowledge of these systems and can build connectors or APIs as needed.
  3. Control: Developing process intelligence dashboards in-house provides greater control over the design, functionality, and data security. This allows the organization to adapt and modify the dashboards as business needs evolve or new data sources become available.

Disadvantages of self-built process analytics

  1. Resource requirements: Developing process analytics in-house requires a skilled development team with expertise in data analytics, visualization, and software development. This can be a significant investment in terms of time, effort, and personnel costs.
  2. Longer development time: Building custom dashboards typically takes longer than implementing off-the-shelf solutions, as the development team must design, code, test, and deploy the solution from scratch.
  3. Maintenance and updates: In-house dashboards require ongoing maintenance, updates, and support, which can be time-consuming and costly. This includes fixing bugs, adding new features, and ensuring compatibility with changes in data sources or systems.
  4. Scalability: Building a scalable BI dashboard in-house can be complex and resource-intensive, especially if the organization's data volumes and user base grow rapidly. Third-party solutions may offer better scalability and performance due to their focus on these aspects.

Example: performing process mining with python

A very simplified example is that you can access and use open-source process mining algorithms quickly with the python programming language.

To perform process mining in Python, you can use the pm4py library, which provides various functionalities for process discovery, conformance checking, and process enhancement. Here's a simple step-by-step guide to get started with process mining using the pm4py library:

Step 1: Install pm4py

First, you need to install the pm4py library. You can do this using pip:

bashCopy code

pip install pm4py

Step 2: Import required libraries

Next, import the required libraries in your Python script:

pythonCopy code

import pm4py
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
from pm4py.visualization.petrinet import visualizer as pn_visualizer

Step 3: Load the event log

Load the event log data using the appropriate importer. In this example, we will use an XES event log file:

pythonCopy code

event_log_file = "path/to/your/event_log.xes"
log = xes_importer.apply(event_log_file)

You can also create an event log from a CSV file using the pm4py library. Here's an example:

pythonCopy code

import pandas as pd
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.util import dataframe_utils

csv_file = "path/to/your/csv_file.csv"
dataframe = pd.read_csv(csv_file)
dataframe = dataframe_utils.convert_timestamp_columns_in_df(dataframe)
dataframe = dataframe.sort_values("timestamp_column_name")
log = log_converter.apply(dataframe)

Step 4: Apply a process discovery algorithm

Apply a process discovery algorithm to the event log data to extract the process model. In this example, we will use the Alpha Miner algorithm:

pythonCopy code

net, initial_marking, final_marking = alpha_miner.apply(log)

You can also try other process discovery algorithms like the Inductive Miner or Heuristics Miner, which are available in the pm4py library.

Step 5: Visualize the process model

Visualize the discovered process model using the Petri net visualizer:

pythonCopy code

gviz = pn_visualizer.apply(net, initial_marking, final_marking)

This will display the Petri net visualization of the discovered process model.

Step 6: Perform additional analysis (optional)

You can use the pm4py library to perform additional analyses like conformance checking, bottleneck analysis, or performance analysis. Explore the library's documentation and examples to learn more about these functionalities.

By following these steps, you can perform basic process mining in Python using the pm4py library. For more advanced use cases and customizations, refer to the official pm4py documentation and examples:

The above example is an oversimplification for the needs of most enterprise leaders. The reality of developing and applying process analytics is often a lot more complicated, so increasingly businesses are opting for dedicated solutions from vendors.

Buying Process Analytics from a Process Intelligence Vendor

Your second viable option is buying process analytics from a process intelligence software vendor. There are a few different variants, so here are some key categories:

  • Process mining software. Tools that mine event logs in enterprise systems to get process and workflow intelligence.
  • Process discovery software. Tools and dashboards that uncover the "as-is" state of business processes either through process mining or manual process mapping.
  • Task mining software. Tools that use task capture technology to capture task, process and workflow intelligence from the user interface.
  • Intelligent automation platforms. Broader software solutions that offer process analytics as part of a suite-solution for intelligent automation or business process management.

Like any other software decision, there are advantages and disadvantages to consider when buying process mining software.

Looking to combine process mining and task mining in your process analytics? Read the Work API whitepaper.

Advantages of buying process analytics

  1. Faster implementation: Commercial process analytics software typically offers faster implementation times compared to developing a custom solution in-house. Most solutions are designed to be user-friendly and have pre-built connectors or APIs for popular data sources, reducing the time required to set up and start using the software.
  2. Feature-rich: Commercial process analytics solutions come with a range of features and functionalities designed to address various process mining requirements. These can include process discovery, conformance checking, performance analysis, and visualization, among others. These features are often more advanced and robust than what could be built in-house within a reasonable timeframe.
  3. Scalability: Process intelligence software vendors generally design their solutions to handle large volumes of data and accommodate the growth of the organization. This means the software can scale as your business processes and data sources expand, without significant additional development efforts.
  4. Support and updates: When purchasing process intelligence software, you typically receive access to customer support, product updates, and patches provided by the vendor. This ensures that you have a reliable source of assistance for troubleshooting issues, and your software stays up-to-date with the latest features and security enhancements.
  5. Community and best practices: Commercial process mining software often comes with a user community, forums, or other resources where you can learn from the experiences of other users and share best practices. This can be valuable in gaining insights and improving your own process mining initiatives.
Example of advanced process analytics - Workfellow Process Intelligence

Disadvantages of buying process analytics

  1. Cost: The cost of purchasing, and maintaining process analytics software can be a significant investment, particularly for smaller organizations. This can include licensing fees, subscription costs, support contracts, and any additional costs associated with customization or integration.
  2. Customization limitations: Commercial process analytics software may not offer the same level of customization as an in-house solution. While some software allows for a degree of customization, it may not be enough to meet the unique requirements or preferences of your organization.
  3. Integration challenges: Although many process analytics solutions offer pre-built connectors or APIs, integrating the software with your existing systems and data sources can still be challenging and time-consuming. This may require additional development or customization efforts, increasing the overall implementation cost and complexity.
  4. Data security and compliance: Using third-party process analytics software may raise data security and compliance concerns, particularly if sensitive or regulated data is involved. You must ensure the software meets your organization's data security and privacy requirements and complies with relevant regulations.

In conclusion, buying process analytics software offers advantages such as faster implementation, feature-rich capabilities, and scalability, but has potential drawbacks like implementation cost and customization limitations. Organizations should carefully evaluate the advantages and disadvantages of purchasing process mining software based on their specific needs, resources, and objectives before making a decision.

If you're looking to build a business case for buying process analytics read the case study below highlighting the value of buying a fast, ready-made solution.

Read case-study how one medium-sized business process outsourcing (BPO) firm uncovered over € 2 million ($2.17 million) in process waste with process analytics.

Process Analytics Q&A

What is business process analytics?

Business process analytics (BPA) is the practice of examining, measuring, and evaluating business processes to identify areas for improvement, increase efficiency, and optimize overall performance.

What is the goal of process analytics?

The goal of process analytics is to gain a better understanding of how business processes function, identify areas for improvement, and optimize processes to enhance efficiency, reduce costs, and drive better results.

What is an example of process analytics?

An example of process analytics is analyzing the workflow of a customer support team to identify bottlenecks, reduce response times, and improve customer satisfaction.

What is the difference between process analytics and data analytics?

The difference between process analytics and data analytics is that process analytics focuses specifically on analyzing and optimizing business processes, while data analytics involves the broader examination and interpretation of data to extract insights and inform decision-making.

Written by

Lari Numminen

Chief Marketing Fellow