This is the twelfth article in our series on data quality problems for process mining. You can find an overview of all articles in the series here.
When you get a data set and assess the suitability of the data for process mining, you start by looking for the three elements: Case ID, activity name and timestamp.
For example, when you look for the case ID then you start looking at the candidate columns to see whether there are multiple rows in the data set that refer to the same ID (see image below). If you don’t have multiple rows with the same case ID, then most likely the field that you thought could be your case ID is just an event ID and does not help you to correlate the steps that belong to the same process instance1.
When you continue looking for the other fields, it sometimes seems as if you have all the fields that you need at first. But then you find out that you actually miss the history information in these fields. Read on to learn about four situations, where this can happen.
Missing Activity History
When you look for a field that can be your activity name, you may encounter a situation like shown in the picture below: The status is the same for each event in the case.
In this situation, you do have a column that tells you something about the process step, or the status, for each case. However, you don’t have the historical information about the status changes that happened over time. Often, such a field will contain the information about the current status (or the last activity that happened) for each case. However, this is not enough for process mining, where you do need the historical information on the activities.
How to fix:
If the activity name or status column never changes over the course of a case, then you cannot use the column as your activity name. You need to go back to the system administrator and ask them whether you can get the historical information on this field.
You can also look for other columns in your data set to see whether they contain information that does change over time (like an organizational unit, so that you can analyze the transfers of work between different units).
The same can happen with the timestamp fields. At first, it might seem as if you had many different timestamp columns in your data set. But does any of them change over time for the same case? Or are they all the same like in the example below?
How to fix:
If your timestamp field never changes over the course of a case, then this is a data field but not a timestamp field as you would need for process mining. If you only have timestamp columns that never change, then you don’t have a timestamp column at all.
If your data is sorted in such a way that the evens are the right order, then you can still import the data set into Disco. Even without a timestamp, you can then still analyze the process flow and the variants (based on the sequence information in the imported data set), but you won’t be able to do a performance analysis.
Missing Resource and Attribute History
A similar situation can occur with other data fields, like a resource field or another data attribute. For example, in the data set below, the resource column does not change over the course of the case.
Instead of the person who performed a particular process step, the ‘Resource’ field above could indicate the employee that started the case, who is responsible for it, or the person that last performed a step in the process.
The same can happen with a data field, like the ‘Category’ attribute in the example above, where you might know that the field can change over time but in your data set you only see the last value of it.
How to fix:
If you can’t get the historical information on this field, request a data dictionary from the IT administrator to understand the meaning of the field, so that you can interpret it correctly.
Realize that you cannot perform process flow analyses with this attribute (for example, no social network analysis will be possible based on the resource field in the example above). You can still use these fields in your analysis as a case-level attribute.
Missing History for Derived Attributes
Finally, the missing history information on attributes might even be trickier to detect. For example, take a look at the data set below. We see that the registration of the step ‘Shipment via forwarding company’ in case C360 has been performed by a ‘Service Clerk’ role. However, for case C1254 the same step was performed by a ‘Service Manager’ role, which if we know the process might strike us as odd.
If we look deeper into the problem, then we find out that the ‘Role’ information was actually extracted from a separate database and linked to our history data set later on. However, the ‘Role’ information that was linked contains the roles of the employees today.
In 2011, when case C1254 was performed, Elvira Lores still was a ‘Service Clerk’. But by 2013, when case C360 was performed, Elvira had become a ‘Service Manager’. However, we can’t see that Elvira performed the step ‘Shipment via forwarding company’ back then in the role of a ‘Service Clerk’ because we only have her current role information!
How to fix:
As with the other examples above, there is typically not much that you can do about this in the short term. The most important part is that you are aware of this data limitation, so that you can interpret the results correctly.
Today is a special day for us. We are very excited to introduce you to a new member of the Fluxicon team: Rudi Niks!
Here at Fluxicon, we have tried to stay as small as we can for as long as possible. We value the efficiency of having a small team, which makes it much easier for us to maintain our obsessive focus on quality and the close, personal contact with our customers. However, since our customer base has been growing so much lately, we started thinking about who would be a good fit to join the team.
We immediately thought of Rudi. In addition to his extensive experience with process mining and process improvement work, Rudi shares our values of honesty and quality. He is every bit as much of a process mining enthusiast as we are, and we are very happy that he agreed to join us! Together we will continue to build the best process mining software for professionals, and to support and grow the process mining community worldwide.
But we will let Rudi introduce himself to you in his own words:
My Journey of Becoming a Process Miner
13 years ago, I was one of the early adopters of process mining. I studied Business Information Systems at the Technical University in Eindhoven and we were introduced to this new technique of discovering processes from event data.
Process mining was still in its infancy and for many of my fellow master students it was a frustrating experience: First of all, good data sets for process mining were hard to come by. Secondly, the early versions of the academic process mining tool ProM had a particularly long learning curve. And thirdly, ProM was typically jamming just when you were about to begin your analysis! Christian — then a Ph.D. student in the process mining group — was our instructor and helping this first group of fledgling process miners on their way.
In 2011, I had an appointment as a management consultant at a major Dutch bank. We had made coffee and found a quiet spot. Frank introduced himself and the impact of digitization within the bank was soon the topic of conversation. He talked passionately about how these changes had a major impact on the processes of tomorrow. To survive this transition from a traditional bank to a digital bank Frank wanted to accelerate the change of the processes with … ‘Process Mining’.
I put down my coffee, and said: “You want to generate processes based on IT data? This is far to technical and scientific!” Frank laughed, opened his laptop, and gave me a brief demonstration of Disco. Amazed, I watched him, magically, creating a process map from his data set in seconds, and how easy it was to zoom in on all the variations.
A few days later, we were at the table with our first sponsor, the IT Service Manager. His ambition was to improve the services while lowering the costs. There were regular complaints that resolving incidents took too long. They were already working with continuous improvement methodologies, but it remained unclear what the route of an incident was through the different teams.
We analyzed the data and scheduled appointments with the teams that were responsible for handling different types of incidents. In earlier assignments I had realized that most of the improvement suggestions that came out of the workshops with the various departments were based on gut feeling. As a consequence, there was a lot of resistance to improve. In contrast, the process maps that we obtained with Disco told the story of what really happened with these incidents. The group delved deeper into the picture, uncovered the root cause for why these incidents were taking so long to resolve, and found other problems in the process that we had not even noticed. After 50 minutes, Frank and I walked out of our first meeting and we asked ourselves how many steps and time we can prevent in the other 500 types of incidents.
Over the coming years, I worked on many different process mining projects at different companies. In these projects, I saw first-hand how process mining empowers both the process improvement teams as well as the people who are responsible for these processes, and how fast you can move based on the new insights. Instead of six months like in a classical process improvement project, with process mining we typically succeeded to collect data, analyze it, and implement the improvements within 4 weeks.
When I give masterclasses or share my experiences at conferences today, I still see the surprise in the faces of colleagues and managers once I show them how you can magically discover processes based on data with process mining. I see the enthusiasm and ease by which professionals start analyzing their own processes in Disco. And I am continuously surprised by the new applications that they find that I had not thought of myself.
I began to realize that digitalisation does not only change organizations but that it also changes us as professionals. In an increasingly digital world processes produce more data, making them more and more traceable. These digital processes are no longer hidden in the minds of people, but in the databases of information. And these digital processes are also changing faster and faster.
I believe that Process Mining is a game changer to extract real value from these digital processes. As a proud member of the Fluxicon team I am looking forward to working with and supporting all of you who are taking on these changes and new opportunities in our profession!
Last year, we introduced the Process Miner of the Year awards to help you showcase your best work and share it with the process mining community.
This year, we will continue the tradition and the best submission will receive the Process Miner of the Year award at this year’s Process Mining Camp, on 29 June in Eindhoven.
Have you completed a successful process mining project in the past months that you are really proud of? A project that went so well, or produced such amazing results, that you cannot stop telling anyone around you about it? You know, the one that propelled process mining to a whole new level in your organization? We are pretty sure that a lot of you are thinking of your favorite project right now, and that you can’t wait to share it.
What we are looking for
We want to highlight process mining initiatives that are inspiring, captivating, and interesting. Projects that demonstrate the power of process mining, and the transformative impact it can have on the way organizations go about their work and get things done.
There are a lot of ways in which a process mining project can tell an inspiring story. To name just a few:
Process mining has transformed your organization, and the way you work, in an essential way.
There has been a huge impact with a big ROI, for example through cost savings or efficiency gains.
You found an unexpected way to apply process mining, for example in a domain that nobody approached before you.
You were faced with enormous challenges in your project, but you found creative ways to overcome them.
You developed a new methodology to make process mining work in your organization, or you successfully integrated process mining into your existing way of working.
Of course, maybe your favorite project is inspiring and amazing in ways that can’t be captured by the above examples. That’s perfectly fine! If you are convinced that you have done some great work, don’t hesitate: Write it up, and submit it, and take your chance to be the Process Miner of the Year 2017!
How to enter the contest
You can either send us an existing write-up of your project, or you can write about your project from scratch. It is probably better to start from a white page, since we are not looking for a white paper, but rather an inspiring story, in your own words.
In any case, you should download this Word document, which contains some more information on how to get started. You can use it either as a guide, or as a template for writing down your story.
When you are finished, send your submission to firstname.lastname@example.org later than 31 May 2017.
We can’t wait to read about your amazing projects!
If you are involved with process improvement, then reducing rework is most likely one of your concerns.
Two common causes for rework are:
The task was not done right the first time, so someone has to go and do it again.
Information that would have been necessary to work on a case was missing, so it had to be sent back.
Rework is bad because it adds to the workload (and costs) of the company, because it delays the process completion time for the customer, and because — due to the extra effort — it often impacts the completion times for the following cases as well.
Process mining can help you to identify and pinpoint rework patterns in your process. By letting the process mining tool map out your actual process based on the IT data, you will be able to see where rework occurs, how often, and which process categories are affected by it.
Of course, once you have found where and how often rework occurs, you will still have to go and talk to the people in your process to find out why this is happening. But you will be armed with objective information and visual evidence that will be enormously useful to engage the people who are responsible for the process, and to focus the discussion on facts rather than keep arguing about opinions and gut feeling.
How come that people don’t know about the rework already? It is normal that not everything goes according to plan all the time. But because each of the employees handles just a few “exceptional” cases, what seems to be a small extra step here and there amounts to a lot of waste if you look at the complete picture. Sometimes, this effect is called “hidden factory”. With process mining, you will be able to provide an objective overview about the complete process and make the hidden process patterns visible.
The solution does not always need to be technical either. For example, in a call center the increase of repeat calls typically indicates a quality problem: The people who had called earlier needed to call again because their problem was not solved in the first call. If the agents had been instructed to keep the call times as short as possible, this might seem to save money at first. However, in the long run it does more harm than good, because the customers are less happy and keep calling back. Shifting the measurement focus to ‘first time right’ can greatly enhance both the customer experience and the efficiency at the call center.
With process mining, you will have the process with all its problems right there, magically and objectively, at your fingertips. This is exactly what makes it possible for your team to focus on the why (and not the what) in the process analysis, which is one of the big benefits of process mining.
1) Filter direct loops with the ‘Filter this path…’ shortcut
Rework manifests itself in different kinds of loop patterns in your process. Often, you will directly see the loop in your process map.
For example, take a look at this call center example below (one of the demo data sets you can download from the Disco website). The process map shows cases that are started by inbound calls, and you can see from the self-loop at the ‘Inbound Call’ activity that there are repeat calls for some cases. You can click on the images to see a larger version.
To focus on these repeat calls, you can click on the loop arrow and press the ‘Filter this path…’ button in the overview badge (see picture below).
You will be taken to a pre-configured Follower filter for this path and can simply press the ‘Apply filter’ button in the lower right corner. (Press the ‘Copy and filter’ button instead if you want to save your repeat call analysis for later.)
As a result, you can see the new process map for the repeat calls and you can see that 16% of all cases show this repeat call pattern.
2) Catch global repetitions with the ‘Eventually follows’ option
Now, this gives us the direct repetitions for the ‘Inbound Call’ activity. But wat about repeat calls that come in after some other activity happened in the process?
For example, there may have been an initial call from the customer. Then the agent called back (‘Outbound call’ activity) and then, some time later, the customer calls again (another ‘Inbound Call’). In this scenario, there has been a repeat call, but the two calls did not happen directly after each other. So, they are not reflected by that self-loop in the process map that we focused on in scenario 1) above.
What can you do if you still want to count this case that includes the pattern ‘Inbound Call’ -> ‘Outbound Call’ -> ‘Inbound Call’ in your repeat call analysis result?
That’s easy: You can simply go back to your Follower filter (click on the filter symbol in the lower left corner) and change the mode from ‘directly followed’ to ‘eventually followed’ (see below).
Your filter result now includes all cases that at any point in the process had a repetition (or more) of the activity ‘Inbound Call’.
3) Filter loops of the same type
So far, we have seen how you can filter cases where the same activity occurs multiple times. However, sometimes the rework patterns you want to analyze are more general.
For example, you may have combined multiple fields to unfold your activity name in additional dimensions (see the article Change in Perspective with Process Mining for three alternative view points that you can try for your own process).
The screenshot below shows how the ‘Operation’ and the ‘Agent Position’ columns were both configured as part of the activity name during the import step.
As a result, you can see a more fine-grained view of the call center process: The hand-over points between the first-level support staff (FL) and the backoffice employees (BL) are now explicitly represented in the process map (see below).
This is great, but if you want to filter for repeat calls as before, you now have multiple instances of the type ‘Inbound Call’ in your activity list (‘Inbound Call – FL’ and ‘Inbound Call – BL’). You could select multiple activities in the list, but you can also simply use the more general type attribute that you care about for filtering (see below).
Now, you can define your rework pattern directly on the ‘Operation’ field type ‘Inbound Call’ (see below).
The possibility to choose another attribute for your rework pattern in the Follower filter is also handy if you don’t want to focus on activity repetitions in the first place. Instead, you might be interested in, for example, cases that are reworked by the same person, the same department, or any other attribute dimension that you have included in your data set.
4) Filter for repetitions without knowing where they are
But what do you do if you don’t really know which loops you should be focusing on? Say, you want to filter all cases that have some rework in it, regardless of which activity was involved in the rework.
You can use the Follower filter to do that, too. Take a look at the Sandbox example that comes with Disco to see how:
First, click on the filter symbol in the lower left corner to add a filter.
Then, directly add a Follower filter from the list of filters (see below).
In the Follower filter settings, first select all activities as reference and follower values. This by itself will not yet have any effect, because if you match every activity pattern in the data set then all cases will be retained.
But now comes the trick: Below the reference and follower event list you can also add an additional constraint based on another attribute, which can be asked to have the same or a different value. Often, this is used to find violations of segregation of duties or analyze other compliance rules, but here we are using the ‘the same value’ option to filter repetitions of any kind.
Enable the checkbox Require and configure the settings so that it says the same value of Activity as shown below. Then click ‘Apply filter’.
The result of this filter are all cases that have some repetition somewhere in the process, no matter which activity has been repeated. We can see that in total 41% of the cases have some form of rework in this process.
5) Visualize repetition hotspots with the ‘Max repetitions’ option
To see where the biggest rework occurs, you can switch the process map view to ‘Max repetitions’ as shown below. The numbers in your process map will change to show the maximum number of times an activity was performed for a single case.
In the purchasing example, the activity ‘Amend Request for Quotation Requester’ really stands out as it has been repeated up to 12 times in the same case.
6) View detailed repetition statistics by focusing on a single activity
We now know that activity ‘Amend Request for Quotation Requester’ has been performed up to 12 times for the same case, but how many cases exactly repeated this activity so often? Just one? How many repetitions are most typical?
If you want to focus in on one specific repeating activity in more detail, you can do the following:
First, click on the activity you want to focus on and press the ‘Filter this activity…’ button in the overview badge (see below).
Then, in the Attribute filter change the mode from ‘Mandatory’ to ‘Keep selected’ as shown below (we only want to keep this one activity right now). Use the ‘Copy and filter’ button to save this rework analysis in your project.
After applying the filter, change from the Map view to the Statistics view and …
… change to the ‘Events per case’ statistics to see how many times this particular activity was performed for how many cases.
For example, now we can see that there was indeed just one case that performed activity Amend Request for Quotation Requester 12 times, and that there were three cases, where this activity was performed 6 times (see screenshot below).
For each of the scenarios above, you can then further analyze the context of your rework pattern by looking at further statistics. For example, you might want to see which process categories (regions, product types, etc.) are most affected. And you can inspect individual cases in the Cases View to get more context information and talk to the people who were involved in these cases to learn what the reason was and how they would improve it.
Which rework patterns can you find in your own process? If you need help, just get in touch and we will help you to get started!
One of the challenges of applying process mining is that different skills need to come together to make it a success. Sometimes, you will find multiple skills in one person, but often you need to put together a multi-disciplinary team of people complementing each other.
Here is an overview of the most important roles that your team should cover.
While you will define what kind of data you need for your process mining project, you will typically not extract the data from the IT system yourself. Instead, you will work together with the IT department who will extract the data for you.
The IT administrator will also be able to help you clarify questions about the data itself and provide you with a data dictionary about the meaning of the different data fields.
It is a good idea to involve the IT team early in your project, so that they understand what you want to do and what kind of data you need.
Some systems can provide a data extract that can immediately be used for your process mining analysis. However, more often than not you will need to combine different data sources or re-format your data in some way.
While most process analysts will be able to re-work their source data in Excel, for larger data sets you need skills to merge and process your data via SQL, ETL tools, or via scripting languages like Python or R. For such projects, you need to have someone on board who can do these data transformations for you.
Data / Process Analyst
The actual analysis of the data is the home turf of the process mining analyst. Keep in mind that the data analysis does not only cover the answering of your process questions but also includes tests for data quality and the fixing of data quality problems.
If your project is a process improvement project, it is a very good idea to make sure that you have a Lean Six Sigma practitioner or some other kind of process improvement expert on board. They are trained to suggest and evaluate process improvement alternatives from a business perspective.
If your analysis falls into another process mining use case — for example, you may be using process mining to support your internal audits — then you need someone in your team who is an expert in this profession.
Project and Change Management
Just like with any other project, you need project management skills to scope your project, define realistic milestones, and manage the progress of the project.
Furthermore, actually implementing the process changes is necessary to realize the benefits from your process mining analysis. You need a change manager to help the business unit through the process changes that come out of your process mining project.
In many situations, the process mining team will perform projects for different business units in the company. To ensure that your process mining analysis will have an impact, you need a strong sponsor who is actually interested in the results.
A sponsor who crosses their arms and says “Surprise me” is a read flag. Instead, look out for someone who is also enthusiastic about the possibilities of process mining and who is willing to provide you with the support and the resources that you need.
One of the resources that you need for a successful process mining project is access to a domain expert. Typically, this is not the process manager themselves but another process expert in their team.
This subject matter expert will help you define the analysis questions for the project, perform the data validation session with you, and review intermediary findings in a series of workshop sessions throughout the project.
A last stakeholder who is not in the picture above but nevertheless very important is the privacy and ethics expert in your company. Read our guidelines on Privacy, Security, and Ethics in Process Mining here and take those lessons aboard in your process mining project.
The date has been set: Process Mining Camp 2017 will take place in Eindhoven1, the birth place of process mining, on 29 & 30 June 2017.
For the sixth time, process mining enthusiasts from all around the world will come together for a unique experience. Last year, more than 210 people from 165 companies and 20 different countries came to camp to listen to inspiring talks, share their ideas and experiences, and make new friends in the global process mining community.
For the first time, this year’s Process Mining Camp will run for two days:
On the second day (30 June), we will have a half day of hands-on workshops. Here, smaller groups of participants will get the chance to dive into various process mining topics in depth, guided by an experienced expert.
Eindhoven is located in the south of the Netherlands. Next to its local airport, it can also be reached easily from Amsterdam’s Schiphol airport (direct connection from Schiphol every 15 minutes, the journey takes about 1h 20 min). ↩
When you perform a process mining analysis, then the discovered process map and the variants are only the starting point. You then want to dive deeper into the process based on the questions that you have about it.
One of the typical questions is about the performance of the process. For example, you may have a service level agreement (SLA) with respect to the overall throughput time of the process. Within Disco, you can analyze the case duration distribution and you can filter your data to focus on the slow cases to find out where in the process they lose so much time (see also the video at the top for a demonstration of how to do this).
Once you discover a bottleneck in your process, the animation is a very powerful tool to visualize the bottleneck to your co-workers. Rather than just giving them abstract statistics and charts, they can literally see where a lot of the cases are piling up and where the queuing occurs (see below). This will help you to explain your findings and engage them in discussions about how the process can be improved. As soon as a bottleneck has been resolved, you can focus on the next one to support a continuous improvement of your process.
Once you dig into the performance analysis for your process, there are two things to know that can be helpful. So, in this article, we want to give you these two tips that will help you perform better bottleneck analyses on your own data.
Tip No. 1: Consider the median instead of the mean
All the performance metrics in Disco, for example, the case durations, the activity durations, but also the performance metrics in the process map, give you both the mean and the median duration.
Often, there is quite a difference between the two. For example, if you look at the case duration below (click on the image to see a larger picture) then you will notice that the mean case duration is 21.5 days while the median case duration is just 12 days — That means the median case duration is almost half of the mean case duration for this process!
The reason that this can happen is that the mean is much more susceptible to outliers. To understand why, let’s take a look at how both the mean and the median are calculated. In the figure below, you can see seven measurements lined up according to their size. For example, these could be seven cases of which we have measured the throughput time: Two cases were measured with 1 day throughput time, one case was measured with 2 days throughput time, three cases were measured with 3 days throughput time, and one case — our outlier — had a throughput time of 30 days.
Now, the median is defined as the value in the middle of the lower 50% and the higher 50% of measurements. So, 3 would be the median value in this example, because half of the cases took longer (or equally long) and half of the cases were faster. In contrast, the mean or average value is calculated as the sum of all values divided by the number of values. So, the mean yields 6.14 in this example. The mean is more than twice as high compared to the median, because the mean is much more influenced by the one extreme case with the 30 days throughput time.
In practice, many processes have a distribution similar to the picture above. For example, your customer service process may typically take up to two weeks, but you have these few, very complicated cases that took one or two years to resolve. Or when a typical incident can be closed with 8-10 steps, there is this one extreme case that was ping-ponged between different groups more than 200 times.
In such processes, the median (also known as the 50th percentile) gives you a much better idea of the typical performance characteristics of a process than the arithmetic mean. Therefore, the median can often better point you to the places in the process that typically are quite slow. For example, from the mean durations visualized in the illustration below on the left, you can get the impression that basically the whole area on the left of the process is problematic in terms of performance. The median performance view, shown on the right, makes it clear that the bulk of the problems actually lies with one activity on the lower left.
Of course there are still situations, where you might want to use the mean. One reason can be that it is easier understood by people who are not statistically minded. Or your KPIs might be defined based on the mean, so you should use the mean for your analysis, too. But keep in mind that if you have a skewed distribution with heavy outliers, the mean can be misleading and the median will be a better metric to get a sense of what a typical value looks like.
Tip No. 2: Combine total duration with the median
The second tip that we want to give you is to keep in mind that neither the mean nor the median take the frequency into account. This can be a problem, because you want to focus your improvement efforts on those places in the process, where they can have the most impact.
For example, let’s take a look at the process map below. We have used the median for the performance visualization and it looks like that path that typically takes 5.6 days is the biggest problem.
However, once we switch to the frequency view, we can see that the path right next to it is about 10 times as frequent. So, although the median delay on that path was just 3 days (instead of 5.6 days), the impact of improving this particular bottleneck will be greater.
The best way to take the frequency into account in your bottleneck analysis is to use the total duration (see the screenshot below). The total duration gives you the sum of all the delays in the data set and, therefore, naturally takes both the actual delays but also the frequency into account. So, you can clearly see the big, fat, red arrow in the process map point to the biggest bottleneck that you should address first.
The only drawback of the total duration is that the numbers easily add up to months or years. As a result, it is hard to get a sense of what the typical delay of a path or activity is in the process. To address this, you can add the mean or median duration as a secondary metric (see screenshot below). The secondary metric will appear in smaller font below the primary metric in the process map. We can see the 5.6 days median measurement re-appear in the process map, but it is now clear that the path to the left is the bigger problem we should focus on.
Now, you have the best of both worlds: The total duration as the primary metric is driving your attention to the right places in the process map and helps you to focus on the high-impact areas for your improvement project. At the same time, you can easily see what the average or the typical delay is in this place through the secondary metric.
By using Process mining, organizations can see how their processes really operate. The results are amazing new insights about these processes that cannot be obtained in any other way. However, there are a few things that can go wrong.
Process mining doesn’t usually begin as a top-down initiative. Typically, there are a few enthusiastic people who want to do something with it. When they start a process mining initiative within their organization, they need to bypass the following classic pitfalls.
First of all: Being too fascinated with the technology itself can lead to an inability to show the added value from a business perspective. Secondly: An unrealistic image of the data availability, coming from the promise of Big Data, can lead to overblown expectations. And the third pitfall: Due to a wrong understanding of what process mining can do, the first project is often too ambitious in scope. Too much is being promised and it takes too long before the first results can be shown. This undermines the belief within the business that process mining produces a good ROI. A failed project then not only leads to a decrease in the entrepreneurial and innovative spirit among the process mining enthusiasts, but there is also the risk that process mining will not be picked up again in a new project for years.
In this article, Frank van Geffen and Anne Rozinat give you tips about the pitfalls and advice that will help you to make your first process mining project as successful as it can be.
So, how can you make sure that your process mining initiative is successful? What makes the difference between success and failure? We provide you with a roadmap (see Figure 1) and discuss four success factors.
Figure 1: Roadmap to making your process mining project successful
Success factor No. 1: Focus on the business value
Do: Define the business value in terms of effectiveness (customer experience and revenue), efficiency (costs) and risk (reliability). Determine into which process aspects you want to gain insights. To which business driver does this insight contribute? Better customer experience, cost reduction, risk mitigation?
Don’t: Don’t be overly fascinated with the possibilities of the technology. There are often multiple ways to get answers for your questions, and sometimes multiple data analysis techniques must be combined to get the full picture. Do not become fixated on ‘only’ using process mining.
Success factor No. 2: Start small, think big
Do: Connect the business driver to a specific business domain. Choose a process where the beginning and the end are clearly defined. Check whether this process is supported by an IT system. For example, call center or service desk processes are very suitable for a first project, because the data can be easily extracted from these systems. Also workflow systems are a good source of data for your process mining project. Each manager of such a process will benefit from insights that help to reduce costs or increase the effectiveness. This allows sponsorship on the management level. Choose a sponsor who is willing to support you (a sponsor who crosses their arms and says “Surprise me” is a red flag). And while you think about the possible use cases and application possibilities, also make sure to communicate what process mining is not (see Figure 2). By indicating clear boundaries, you can manage expectations on what it is.
Don’t: Do not start with the most important core process of your company. That will come later once the first results have convinced people of the approach. For example, don’t choose the production and supply process of your beer company for your first process mining process. Instead, start with the purchasing process. You will be amazed about how much value is added to the primary process through an effective and efficient purchasing process.
Figure 2: To fully communicate what process mining is, you need to understand what Process Mining is not
Success factor No. 3: Work hypothesis-driven and in short cycles
Do: Divide the main business driver into sub hypotheses that you can confirm or disprove with a process mining analysis. For example: There is a gut feeling that this service process takes too long. How long does the process really take? How much does it deviate from the expectation? Where are the bottlenecks that cause the delays in this process? In practice, measuring and making the actual throughput times visible already provides an insight over which the ‘business’ loses sleep. In addition, you can then indicate where exactly the delays are in the process. Take your business stakeholders from insight to insight. Stimulate them to ask questions. Explore, analyze and innovate. Time-box the intermediate results and the project. Eight weeks for the first project is usually a good aim.
Don’t: Do not try to immediately answer all questions. The first insights often raise further questions, which then require further analysis. Avoid the pitfall of wanting to answer all possible questions beforehand (analysis paralysis) and use your initial hypotheses as a guideline to avoid being lost in the data and its possibilities.
Success factor No. 4: Facts don’t lie
Do: Process mining allows you to analyze processes based on facts instead of subjective opinions. Speak openly and transparently about the data that you use and about the facts that come out of this analysis. This can be confrontational and for some people even unwelcome. Put a change management team together that has the competency to handle resistance. For example, you can integrate process mining in a project, where the Lean philosophy is used. In these types of projects, people are stimulated to tell each other the ‘truth’ and, therefore, are enabled to tackle and solve the real problems. Process mining can be the perfect assistance in this truth finding. Always use experts from the business process domain and the IT-domain for a sanity check of the data and the analysis. Use process mining as a constructive starting point to ask the right questions and avoid too quick judgments.
Don’t: Never be careless in handling, preparing and analyzing the data. If you skip the data quality checks and present conclusions based on data that turns out to be wrong, you will often lose the trust of the business forever. Do not assume that all the information is in your data (often relevant context information needs to be considered to draw the right conclusions). Do not draw forced conclusions based on incomplete data (if your questions cannot be answered based on the available data, say so) and do not present anything that cannot be supported by facts.
Because of all these challenges you can sometimes lose track of the great possibilities that process mining provides. But don’t despair and look forward to an exciting journey!
With process mining it is possible to look at your processes at a much more detailed level. You connect to the real processes and you analyze them based on facts. And after each process change, the analysis can be repeated quickly and easily.
But what exactly could be the outcome of such a process mining analysis?
On a high level, there are four main outcomes of a process mining analysis (see also picture above). For any process mining project, a combination of these outcomes can apply.
Sometimes, the outcome is just an answer. For example, imagine you are the manager of a process and have received complaints that this process is taking too long. There is an internal Service Level Agreement (SLA) and you want to know whether the complaints are justified (and if so, how often it happens that the SLA is not met). Getting an answer to this question is the primary goal of the process mining analysis.
Another example would be a data science team that supports a customer journey project, where the customer experience is completely re-designed. To make sure that the new system supports the customers in the best way, the data scientists have been asked to analyze what the most common interaction scenarios are.
Finally, think of an auditor who assesses the compliance of a process. The audit report with the summary of their findings will be the main outcome of the process mining analysis.
2. Process change
In many situations, the outcome will be a process change. For example, a particular process step may be automated. There might be organizational changes to address the high workload and shortage of resources in a certain group. An update to the FAQ or website of the company could be made to prevent unnecessary customer calls. Based on the assessment of the audit team, a new control could be implemented in the IT system to reduce the risk of fraud. Or based on the analysis of an outsourced service process at an electronics manufacturer, the contracts with the outsourcing partners will be renegotiated in the next year.
Typically, the analysis will be repeated after some time to see whether the change was as effective as one had hoped. It is easy to repeat a process mining analysis with fresh data to investigate these effects. The outcome of the follow-up analysis can then again be just an answer or result into more process changes.
Sometimes, you can also discover a new KPI that was not known before. For example, imagine you are analyzing a payment process where the company can get 2% discount from their suppliers if they pay within 10 days. You realize that there are two main phases in this process: (1) the posting of the invoice to the system and (2) several approval steps, before the payment can be run on two fixed days in the week. You implement an additional reminder to the approvers in the financial system (a process change), which reminds the managers who need to approve the invoice to do so more quickly. But now the late posting of the invoices is the main problem. You realize that if they are not posted within 3 days, there is almost no chance to get the payment through on time. And you want to monitor this new KPI in an automated way.
Like the process change, this will be outside of the process mining tool. But after understanding the process and the data (to know where the measure points for the KPI need to be placed) it is typically easy to add such a new KPI to your existing dashboard or BI system.
4. Optimization and further analysis
Finally, sometimes further analysis is needed after the process mining analysis has been completed. For example, let’s say you analyze the fall-out from a sales process, which means that you are looking at those customers who were interested in your products but for whichever reason never completed the ordering process (their revenue has been lost). You want to follow up with them and be pro-active offering help before it is too late. However, you only want to follow-up with the customers who are most likely to buy.
This would be a scenario, where a data science team sets up and trains a prediction algorithm in one the available data mining or machine learning frameworks. It will be a custom application that is targeted at one very specific problem (predicting which customers you should call). The prediction algorithm gets better over time, learning from the historical data, but to set it up in the first place it helps to understand the process and possible process patterns that might have an influence and, therefore, could be a good parameter in the model.
In addition, there are many scenarios where process miners will perform further analyses in other, complementary tools. For example, a Lean Six Sigma practitioner will want to perform additional statistical analyses in Minitab, data scientists might use data mining tools to discover correlations between the process variants and other attributes in the data, process improvement experts might want to run alternative what-if scenarios in a simulation software, and auditors might take some of the findings from their explorative analysis in Disco to their regular audit tools to include them in the standard check procedures.
All of these tools are specializing in different areas and can be used together. Process mining provides important input for these follow-up analyses by providing a process perspective on the data.
So, what outcomes can you expect from process mining for your own work?
To find out, first start learning more about process mining to fully understand how it works and what it can do. Download the process mining software Disco and contact us for an extended evaluation license to explore some of your own data sets.
Although it is not strictly necessary to understand the algorithms behind process mining for using a process mining tool, it will greatly enhance your view of the process mining field and we highly recommend to sign up for the MOOC and give it a try. This is a university-level process mining course of excellent quality, given by Prof. Wil van der Aalst himself. You can read an interview with Wil about the MOOC here.
Over 100.000 people have registered for earlier versions of the course in the last two years. If you have not participated yet, don’t wait and register now!