Every business function and company runs on hundreds or millions of processes. Every employee has their own processes and tasks, whether they are documented or not. For decades, these functions have been analysed and improved through process analysis.
In more recent years, advanced technology has emerged to take the analysis of processes to a new level by adapting proven business intelligence methodologies. As a result, process intelligence software has emerged as of the hottest topics in business operations. In this guide we go through key concepts, best practices as well as advanced topics.
About the authors: this advanced process intelligence guide is written by process excellence enthusiasts at Workfellow with over 20 years experience in process analysis consulting and software development.
Why is process intelligence so popular and what can it be used for? We'll get into that - but let's start first with some core definitions.
What is process intelligence?
Process intelligence is the use of business intelligence strategies and technologies in business process management. Process intelligence can be used to remove bottlenecks or improve operational efficiency, and it can be used as a catalyst for business process re-design.
For a process analyst, business process intelligence is the practice of collecting and analyzing business process data to identify bottlenecks and improvement opportunities. The goal of process intelligence is to improve operational excellence, streamline workflows or increase the pace of digitalization.
Five steps of process intelligence analysis
Process intelligence analysis is the techology-driven analysis of process data and delivering actionable insights for process improvement. It has it's roots in business process analysis - one of the core aspects of business process management.
On a high level, process analysis can be segmented into a number of individual steps, such as:
- Process and task discovery - exploring and documenting the "as-is" state of processes in a team or organization.
- Process mapping - the visual representation of processes based on expert interviews or software analysis.
- Process re-engineering - the documentation how processes should be improved to achieve set business results.
- Process implementation - the important step of actually driving process improvements across teams and systems.
- Process monitoring - the continuous or repeatable evaluation of how teams conform to agreed processes.
In the past, process analysis was the domain of process analysts who fulfilled analysis manually based on expert interviews, surveys or by data-crunching process information on a needs basis. Each company and analyst would have their own methods, so the analysis itself would not be as standardized. It would not be uncommon for analysis to take up to 6 months and because of the manual nature of the method companies spending 3-5 years between process analysis audits.
Why analyze business processes?
Some of the common reasons for using analysing business process data include:
- Process discovery. Discover the true state of processes within a team or organization,
- Process analysis. Deep-dive the individual tasks or process steps within complex workflows,
- Conformance checking. Visualize variations of key processes to map conformance to agreed processes,
- Root cause analysis. Explore the root-causes of inefficient processes,
- Process optimization. Track progress to key process improvement initiatives and company targets.
Process analysis is a well defined practice - and soon we'll find how it has been taken to the next level through process intelligence software.
Why is process intelligence important?
To understand why process intelligence is important for businesses we can look at fresh research published by HFS Research.
According to a survey of executives in large (global 2000) enterprise businesses, 91% of respondents saw process intelligence as hugely significant in driving business value. Almost 86% of respondents also felt the impact of process intelligence in their business today.
However, the same research points to the challenges of process intelligence technology and projects. Over 2 in 3 respondents had been disappointed in past process intelligence projects and also over 2 in 3 respondents felt process intelligence technology is too complex to implement effectively.
A conclusion reached by many in the HFS Research and beyond is that process intelligence is hugely important for effective enterprise businesses to maintain competitiveness, while laggards in investments into process intelligence may see themselves falling increasingly behind in value creation.
How does process intelligence work
While there are many different ways process intelligence can be implemented they mostly follow an extract-transform-load (ETL) flow familiar from business intelligence solutions where raw process data is organized automatically for advanced data analysis.
- Extract - data is collected from enterprise resource systems or from employees completing tasks on workstations.
- Transform - the collected raw data is processed and transformed for analysis, for example through cleansing and de-duplicating.
- Load - data is moved to a database or data warehouse where it can be analysed and visualized in dashboards
Once the data collected is made available in a database, process mining algorithms often applied to produce a time-and-motion view that can be visualized as a directly followed graph. In other words, the process and task steps can each be timestamped and shown in a sequence, helping the process analyst understand the flow of work.
In most cases, process intelligence is done automatically through dedicated software solutions that can provide real-time or near real-time analysis using web browser-based analytics.
Process intelligence vs process mining
Process intelligence and process mining are easily confused but not the same thing. While process mining software is a method to achieve the "how" of data mining process information, process intelligence is the "what" of adapting business intelligence methods to business processes.
Let's see it visually in the graph below.
Process mining is one of the most common ways to automatically collect process data for process intelligence. Other methods include task mining and Work API.
Today many process mining vendors are also utilizing task capture capabilities and many analyst firms have recognized the different solution offerings belonging to the same use-case of enterprise process analysis. However, the solutions are not mutually exclusive. It is possible for enterprise organizations to utilize more than one process intelligence solution.
Benefits of business process intelligence
Each organization will have unique requirements and expected outcomes for process intelligence. Some of the most common benefits include:
- Improved efficiency: Business process intelligence can help to identify processes that are inefficient or ineffective, and allow for faster, more efficient process execution.
- Risk mitigation: Business process intelligence can help to identify risks and alert organizations to potential problems before they become critical.
- Improved visibility: Business process intelligence can provide an overview of the entire business process, allowing organizations to better understand their operations and make more informed decisions.
- Improved compliance: Business process intelligence can help organizations to ensure they are compliant with industry regulations and standards.
- Increased productivity: Business process intelligence can help organizations to optimize their processes, leading to increased productivity and cost savings.
Process intelligence helps enterprise leaders discover the real state of tasks, processes and workflows today in order to improve operational excellence in the future.
Process intelligence is important because it provides a systematic and data-driven way to make improvements to business processes by eliminating bottlenecks, identifying automation opportunities and improving operational efficiency.
Tools and software used for process intelligence
If you're looking for a process intelligence tool you're spoiled for choice. There are over 35 different process intelligence software solutions on the market today - and many more solutions offering task mining or different forms of process analysis and management. Broadly your options include:
- Self-built solutions running on business intelligence platforms (such as Microsoft PowerBI, Qlik Sense or Tableau.)
- Dedicated process mining software that extract process intelligence form event logs in enterprise resource systems.
- Task mining software that collect process information from the workstations of specific teams or groups of employees.
- Process discovery software that focuses on the discovery element of process and task mining.
- Hybrid process intelligence solutions that combine process mining and task mining into the same solution.
Process intelligence vs business intelligence
While process intelligence can be run on business intelligence tools, such as PowerBI or Tableau, many enterprise leaders choose to go for dedicated software.
The key advantage of process intelligence software is the speed-to-insight, where dedicated solutions can adapt templates, integration capabilities and process mapping that leverage built up tacit experience of experienced business process management professionals.
Examples of process intelligence dashboards
Below is a real-world example of process intelligence software in the form of a process variants analysis of an accounts payable workflow. In this popular type of process intelligence visualization you see how a standard process is executed in different ways or process paths from start to end with average times taken in each process step.
Below you see another example of process intelligence in the form of a process overview dashboard including both process and task level insights identifying key performance indicators for an insurance company, such as cost of work and throughput time.
A third example of process intelligence dashboards is the work view - where you can see the interactions of different IT systems within a process flow. You can also see in more detail where information has been transferred (eg. copy-paste) from one system to another.
How to build a case for process intelligence
The best way to create a business case for process intelligence is to identify the potential benefits and savings that can be achieved through the implementation of a process intelligence system. These benefits and savings can be quantified in terms of time, money, and resources. Additionally, it is important to identify the potential risks associated with the implementation of a process intelligence system, so that these can be weighed against the potential benefits and savings. Finally, it is important to assess the cost of implementing the system, as well as any ongoing costs associated with maintenance and support.
Here are a few elements of a good business case for process intelligence:
- Align your key objectives with company strategy and goals.
- Get the right people on board.
- Explain your expected benefits in simple terms.
- Build the case on realistic costs and outcomes.
Use cases for intelligent business processes
As you're building a business case for process intelligence, you may consider the business value drivers you aim to communicate with key stakeholders.
- Creating a single source of truth / system of record for processes and workflows.
- Helping prioritize the process development backlog in a data-driven and measurable way.
- Development and management of process automation techniques and tools.
- Continuous monitoring of the real-time health of your processed, workflows and work efficiency.
- Development of agile process optimization methods and helping the change management in digitalizing work.
These use cases can be applied to a variety of industries (eg. insurance or banking) or functions (eg. finance or procurement.) For more information on your use cases, request a demo.
When to request an RFP?
As you're evaluating your options you may consider creating a request for proposal (RFP) for process intelligence software. An RFP may help you refine the case and requirements within your organization and help identify potential software or solution providers who can match your needs.
Some typical process intelligence RFP scoring criteria to consider:
- Are you looking for a process mining, task mining, or full-feature process intelligence solution that covers systems, processes, and work?
- Are you looking for a turnkey solution requiring limited implementation or more extensive deployment resources?
- Do you have or need the capabilities to deploy the solution across your organization, or will you require extensive support from the vendor or one of their partners?
- What is your timeframe and urgency for the project? Is it within weeks, months, or years?
- Are you looking for a solution that you can pilot and expand over time across your organization, or do you expect clear, measurable outcomes from the start?
- How important is the solution’s current state, and how much weight do you give the vision and future roadmap of your chosen partner?
- Can you estimate the total cost of ownership of the solution based on the proposal answers, and will it meet your budget requirements?
Future of process intelligence
Process intelligence is a fast-developing new technology area where we expect to see innovation and increased investment in the coming years. According to the findings from Workfellow expert interviews here are five areas where process intelligence has a major role in the future of work.
- Democratization of analytics and insights. Increasingly autonomous process intelligence solutions will reduce the need for tactical process mining and task mining. This will give business leadership and stakeholders access to the most valuable process data without need for heavy investments in data science.
- Radically transparent business operations. As more process intelligence solutions combine process, task and system data across end-to-end workflows, enterprise leaders can measure and improve operations in radically transparent ways across business units and teams.
- Data-driven BPM lead by decision intelligence. Increased process intelligence enables operational excellence teams to shift from data-informed process management to data-driven process optimization.
- The AI-augmented workforce. Process intelligence uncovers ways that automation captures and enhances the subject matter expertise of the workforce, enabling them to focus more time on value-added tasks.
- The fully automated enterprise. Process intelligence accelerates the adoption of intelligent automation and robotic process automation resulting in the ultimately automated enterprise.
Glossary: key terms related to process intelligence
There are many terms related to process analysis and intelligence that are good to keep in mind.
Analytics - analytics is the automated software solution used for analysis, this can be provided by a specialized software vendor or developed by an in-house IT team.
Algorithm - a sequence of instructions given to computer programs to solve a specific issue, for example process mapping.
Artificial intelligence - the simulation of human intelligence by machines, for example through machine learning algorithms.
Business intelligence - the combination of analytics, data mining, data visualization and infrastructure to help businesses make data-driven decisions.
Bottlenecks - a stage or stages within a process that cause a delay in productions, services or operational worklows.
BPMN - (business process model and notation) is a standard graphical representation of business processes used in some process analysis.
Citizen-developer - an employee who creates applications for work outside of the IT team's resources and capabilities.
Data visualization - the graphic representation of data to inform better business decisions, communicate key trends or summarize key insights.
Data mining - data mining is the process of analyzing large amounts of data to discover patterns and rules automatically.
ETL - (extraction, transfer and loading) refers to how data is collected, harmonized and processed for data analysis.
Intelligent automation - the use of artificial intelligence to automate key workflows or processes in a business.
Flowchart - a common form of diagram used to depict processes, task and workflows in process analysis.
KPIs - (key performance indicators) are agreed measurements of performance within an organization and process analysis.
Machine learning - a branch of artificial intelligence where algorithms imitate and improve behaviors without being explicitly programmed.
Throughput time - measure of time taken to complete a workflow or process.
OLAP - (online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data.
Process map - a visual representation of the flow of work within an organization or team with a clear series of events and an end point.
Reporting - in the context of process intelligence reporting refers to the agreed and standard way to represent data within an organization.
RPA - (robotic process automation) the automation of key tasks or sets of workflows by machine workforce or computer systems.
UML - (unified modelling lanaguage) is a flowchat visualization technique used in some process analysis.
Work API - automated collection of business data for process intelligence directly from graphical user interfaces (GUIs)
