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Process Mining

What is process mining?

Process mining is a family of techniques relating to fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Process mining is an integral part of data science, fueled by the availability of event data and the desire to improve processes. 

Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify the execution path taken by operational processes and address their performance and compliance problems.

Process mining starts from event data. Input for process mining is an event log. An event log views a process from a particular angle. Each event in the log should contain:

  1. A unique identifier for a particular process instance (called case id),
  2. An activity (description of the event that is occurring),
  3. A timestamp. 

There may be additional event attributes referring to resources, costs, etc., but these are optional. With some effort, such data can be extracted from any information system supporting operational processes. Process mining uses these event data to answer a variety of process-related questions.

What is process mining in data mining?

Process Mining enables you to automatically analyze business processes based on the event logs from company systems (ERP, CRM, Service Management, etc.) to identify specific areas for improvement on the operational level. It is an innovative analytical approach to gain objective insights and uncover hidden problems.

Process Mining executes a non-invasive procedure, despite how it sounds.

An IT department can export the event logs from your IT systems overnight, then the next day, your team can sit down and feed those exports into your Process Mining software, which will set about creating a visual mapping of your processes in real-time.

This view can then be compared to the map that was created as part of the BPM cycle, giving you the most accurate picture possible of where bottlenecks, inefficiencies, or gaps may exist in your processes.

Data Mining, on the other hand, aims to discover patterns in massive quantities of raw data and large data sets to predict future outcomes based on previously unknown relationships within the data.

There is both art and science involved.

Data Mining sits at a junction of its own, between statistics and computer science. Data scientists use algorithms to sift and sort through massive amounts of raw data in order to make sense of what the data is saying. Then they transform it into actionable information for marketing and sales teams, software designers, and nearly every other department of the company.

What are the steps in process mining?

1. Event collection in process mining

Everything you do in your operational systems leaves a trace. Date, time, user, activity and more are all captured and can be found in an 'event log' – a record of what happens to perform a task. Event Collection, as its name implies, gathers the event log data from your systems so it can be analyzed for process performance.

Before process mining technology, the information stored in event logs was rarely used to analyze the underlying processes. Now process mining is used extensively to gain control over business processes by eliminating the need for hypotheses or guesswork, and instead actively identifying opportunities in places you might not even think to look.

2. Discovery in process mining

Process mining doesn’t simply pull data together into an organized sequence; it creates a rich dynamic visual representation of that sequence. For anyone unfamiliar with process mining technology it inspires a true moment of clarity – an unparalleled look into the functioning of your process. 

How does it work? By applying algorithms to your data. An algorithm is a set of rules to be followed in calculations or other problem-solving operations. That’s what distinguishes process mining from other process discovery methods – it shows you, based on your own data and fact-based rules, exactly how your process flows through your organization.

3. Analytics in process mining

Once you see how your process flows, it’s time to answer the questions that are key to any investigation: who, what, when, where, why and how. Because process mining uses the event logs from your systems, that data is already available.

Using AI and machine learning technologies to analyze your process data, process mining quickly uncovers the root causes of any inefficiencies and execution gaps (as well as what's working well).

Process Analytics turns the transparency of Process Discovery into truths about where you have opportunities for improvement.

4. Automated insights in process mining

Using process mining, it is possible to detect different variations of a process or compare a process between regions, periods of time, suppliers, customers, etc. Furthermore, process mining provides insights into the distribution of the different users active in a process (e.g. manual users versus, system users) and handover of work between them.

Other important process mining techniques are conformance checking and model enhancement. Conformance checking is done by mapping the extracted log against the discovered or hand-drawn process model. 

What are the benefits of Process Mining?

Process Mining comes with serious advantages both over traditional approaches to data analysis or process management and in terms of competitiveness in the market.

Compared to old approaches

Real processes are much more complex than the ideal model. Understanding how you’re currently doing things is crucial to come up with an improvement strategy. Process Mining has a lot of benefits compared to the traditional approach and is able to resolve the weaknesses and problems that arise during old-fashioned data gathering and analysis.

Fast

Process Mining is much faster than the old approach to process optimization. Instead of conducting interviews and holding workshops that then need to be analyzed, the results appear at the push of a button.

The entire picture

You get the complete picture. Where individual people know what they are doing, there usually is no-one who has an idea of the entire process. With Process Mining, you see the entire process from start to finish including every individual step. This makes it much easier to communicate problems and find optimization potential.

With most processes happening digitally, it can be very hard to see how and where work is done. In the old days of huge piles of paper that moved from one desk to the other for different steps, things were easier. Process Mining makes visible what is hidden. It shows the movement of those ‘virtual stacks of paper’.

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Process Mining

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is process mining?

Process mining is a family of techniques relating to fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Process mining is an integral part of data science, fueled by the availability of event data and the desire to improve processes. 

Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify the execution path taken by operational processes and address their performance and compliance problems.

Process mining starts from event data. Input for process mining is an event log. An event log views a process from a particular angle. Each event in the log should contain:

  1. A unique identifier for a particular process instance (called case id),
  2. An activity (description of the event that is occurring),
  3. A timestamp. 

There may be additional event attributes referring to resources, costs, etc., but these are optional. With some effort, such data can be extracted from any information system supporting operational processes. Process mining uses these event data to answer a variety of process-related questions.

What is process mining in data mining?

Process Mining enables you to automatically analyze business processes based on the event logs from company systems (ERP, CRM, Service Management, etc.) to identify specific areas for improvement on the operational level. It is an innovative analytical approach to gain objective insights and uncover hidden problems.

Process Mining executes a non-invasive procedure, despite how it sounds.

An IT department can export the event logs from your IT systems overnight, then the next day, your team can sit down and feed those exports into your Process Mining software, which will set about creating a visual mapping of your processes in real-time.

This view can then be compared to the map that was created as part of the BPM cycle, giving you the most accurate picture possible of where bottlenecks, inefficiencies, or gaps may exist in your processes.

Data Mining, on the other hand, aims to discover patterns in massive quantities of raw data and large data sets to predict future outcomes based on previously unknown relationships within the data.

There is both art and science involved.

Data Mining sits at a junction of its own, between statistics and computer science. Data scientists use algorithms to sift and sort through massive amounts of raw data in order to make sense of what the data is saying. Then they transform it into actionable information for marketing and sales teams, software designers, and nearly every other department of the company.

What are the steps in process mining?

1. Event collection in process mining

Everything you do in your operational systems leaves a trace. Date, time, user, activity and more are all captured and can be found in an 'event log' – a record of what happens to perform a task. Event Collection, as its name implies, gathers the event log data from your systems so it can be analyzed for process performance.

Before process mining technology, the information stored in event logs was rarely used to analyze the underlying processes. Now process mining is used extensively to gain control over business processes by eliminating the need for hypotheses or guesswork, and instead actively identifying opportunities in places you might not even think to look.

2. Discovery in process mining

Process mining doesn’t simply pull data together into an organized sequence; it creates a rich dynamic visual representation of that sequence. For anyone unfamiliar with process mining technology it inspires a true moment of clarity – an unparalleled look into the functioning of your process. 

How does it work? By applying algorithms to your data. An algorithm is a set of rules to be followed in calculations or other problem-solving operations. That’s what distinguishes process mining from other process discovery methods – it shows you, based on your own data and fact-based rules, exactly how your process flows through your organization.

3. Analytics in process mining

Once you see how your process flows, it’s time to answer the questions that are key to any investigation: who, what, when, where, why and how. Because process mining uses the event logs from your systems, that data is already available.

Using AI and machine learning technologies to analyze your process data, process mining quickly uncovers the root causes of any inefficiencies and execution gaps (as well as what's working well).

Process Analytics turns the transparency of Process Discovery into truths about where you have opportunities for improvement.

4. Automated insights in process mining

Using process mining, it is possible to detect different variations of a process or compare a process between regions, periods of time, suppliers, customers, etc. Furthermore, process mining provides insights into the distribution of the different users active in a process (e.g. manual users versus, system users) and handover of work between them.

Other important process mining techniques are conformance checking and model enhancement. Conformance checking is done by mapping the extracted log against the discovered or hand-drawn process model. 

What are the benefits of Process Mining?

Process Mining comes with serious advantages both over traditional approaches to data analysis or process management and in terms of competitiveness in the market.

Compared to old approaches

Real processes are much more complex than the ideal model. Understanding how you’re currently doing things is crucial to come up with an improvement strategy. Process Mining has a lot of benefits compared to the traditional approach and is able to resolve the weaknesses and problems that arise during old-fashioned data gathering and analysis.

Fast

Process Mining is much faster than the old approach to process optimization. Instead of conducting interviews and holding workshops that then need to be analyzed, the results appear at the push of a button.

The entire picture

You get the complete picture. Where individual people know what they are doing, there usually is no-one who has an idea of the entire process. With Process Mining, you see the entire process from start to finish including every individual step. This makes it much easier to communicate problems and find optimization potential.

With most processes happening digitally, it can be very hard to see how and where work is done. In the old days of huge piles of paper that moved from one desk to the other for different steps, things were easier. Process Mining makes visible what is hidden. It shows the movement of those ‘virtual stacks of paper’.

Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

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