All About Requirements

Interview with Bill Brantley On Adaptive Case Management and Process Mining

 

 

Bill Brantley – LinkedIn: http://www.linkedin.com/in/billbrantley/en

 

Bill Brantley is a Doctor in Public Policy and Management with an MBA in Project Management. He teaches project management communication at the University of Maryland and project management in human resources at George Mason University. He is also the Branch Chief of the Human Resources Information Systems unit for the Rural Development agency in the U.S. Department of Agriculture.

 

 

 

Enter Bill:

 

 

John: First, thank you very much, Bill for taking the time to do this interview.

 

 

Bill: You’re welcome.

 

 

John: Before we get started can you tell me a little about yourself and your professional occupation?

 

 

Bill: I’m a long-time practitioner-scholar specializing in IT policy; data science; strategic management; change management; policy analysis; training and development; and project management. I have led several professional and volunteer organizations with my most recent position as the Knowledge Management Lead for the PMI Government Project Management Community of Practice. I am also a skilled application developer with a specialty in open-source software and databases.

 

 

 

John: You have just published a book chapter. Can you tell a little about why you wrote the book chapter and why it is important?

 

 

Bill: I was approached by the Workforce Management Coalition (http://www.wfmc.org/) to be a judge for their 2014 Adaptive Case Management Global Excellence Awards. In reviewing the entries for the awards, I was struck by how many of the organizations were using adaptive case management (ACM) to improve their known business processes. I had just recently read about process mining and wondered if ACM could be improved by using process mining techniques to discover and improve hidden processes. I suggested this as the topic for a book chapter

 

 

 

John: What exactly is ACM?

 

 

Bill: You can find some great extended definitions of adaptive case management (ACM) at http://adaptivecasemanagement.org/AboutACM.html. But I like to summarize ACM as the natural way knowledge workers work when dealing with an issue or problem (“ the case”). Each case is different, and the knowledge worker chooses the best processes, templates, and other work methods to handle that particular case.

 

For example, when I was a paralegal in a public defender’s office, I would help attorneys with many individual cases. Each case was the same in that a crime had been alleged and the defendant would proceed through a well-defined process (“judicial process”).What was different was the particular aspects of the case from which the attorney would choose different legal processes and techniques to handle that particular case. As the attorney handled similar cases, he or she would evolve a library of legal templates, procedures, and other methods to help in dealing with the cases more effectively.

 

 

 

John: When I think of Adaptive Case Management I mostly think of unstructured processes performed by knowledge workers. For example how doctors operate or how project managers perform planning. Usually it is hard to document those processes because the performing (in details) differs from case to case. Do you find that the usage of the processes, templates and other methods are usually recorded in IT systems making it possible to apply process mining?

 

 

Bill: Yes, there are IT systems that can support ACM. For example, I have seen Microsoft’s SharePoint application being used for ACM. I used an open source application, Open Atrium (http://openatrium.com/#!/), to build an IT system to support ACM. All that is needed is central repository to store the templates, social networking tools to facilitate communication between the knowledge workers, and a task management system to handle the processes. There are some great examples of ACM IT applications in the book that has my process mining chapter.

 

 

 

John: Your chapter is titled "Using Process Mining to Improve Adaptive Case Management Processes". How do you see process mining play a role in improving adaptive case management processes?

 

 

Bill: In three ways. First, process mining can help discover hidden processes that the ACM practitioner doesn’t realize are in his or her particular use of ACM. The practitioner can better refine his or her entire particular ACM method, by discovering and improving these hidden processes.

 

Second, process mining can be used to make sure that refined processes supporting an ACM method are in conformance with how the practitioner refined those processes. Third, and I admit I am still working on this; process mining can help in directing the evolution of the practitioner’s particular version of ACM. 

 

 

 

John: What do you mean when talking about hidden processes? Is it processes documented and used by some of the knowledge workers, but being unknown to other knowledge workers?

 

 

Bill:  That is one way that processes can be hidden. Ideally, knowledge workers should share processes so that other knowledge workers can benefit from the accumulated process knowledge. But, a knowledge worker may not feel a process is ready to be revealed or that the process is idiosyncratic to how that knowledge worker manages cases.

 

Also, having worked in IT for a while, I realize how difficult it can be to have developers formally document their processes. This is also true for other areas that I have worked in such as human resources or performing legal investigations. That is why a process that isn’t documented might as well be hidden, in my opinion.

 

 

 

John: How do you see the usage of process mining evolve in the future?

 

 

Bill: In project management. Specifically, how to perform better planning, monitoring, and managing risk in a project. I am working on a paper now to be presented at the University of Maryland’s Project Management Symposium (http://pm.umd.edu/page.php?id=887) that will have some aspects of process mining under the topic of using data science to improve project management.

 

 

 

John: That sounds interesting. To get the best results in analysing planning, monitoring, and managing risks in projects we need data recorded for those unstructured processes. Do you see that as an obstacle? What challenges do you see is approaching this?

 

 

Bill: Yes, I do see obtaining data about the project processes and risks to be a formidable obstacle. I am currently examining common project management planning tools such as the work-breakdown structure, risk-breakdown structures, and the risk register. I think a primary source of information will be from reformatting earned value analysis documents as event logs.

 

 

 

Buy the book on Amazom.com:

http://www.amazon.com/Thriving-Adaptability-Practices-Knowledge-Workers/dp/0986321400/ref=sr_1_1?s=books&ie=UTF8&qid=1424704997&sr=1-1&keywords=thriving+on+adaptability

 

 

 

Interview With Frank van Geffen

 

 

Interview with Frank van Geffen, Process Innovator, Advanced Data Analytics, Rabobank (the Netherlands) and Chairman of the Special Interest Group on Process Mining within the Ngi-Ngn (Dutch society for ICT Professionals).

 

 

 

Frank van Geffen – LinkedIn: http://nl.linkedin.com/in/frvangeffen

 

 

 

Frank van Geffen is a process mining expert and one of the leading innovative business consultants and works for Rabobank in the Netherlands. Frank has introduced process mining in Rabobank and completed numerous process mining projects, resulting in millions of Euros in cost savings. It is a pleasure to have Frank here for a chat.

 

 

 

Enter Frank:

 

 

 

John: First, thank you very much Frank for being willing to do this interview and spending time with me on this exciting subject, Process Mining.

 

 

Frank: You’re welcome. I am very happy to share my thoughts and experiences.

 

 

John: Frank, can you tell me a little about your role at Rabobank?

 

 

Frank: I am a Business Consultant in the department for Advanced Data Analytics within Rabobank, specialising in process mining. The department specialises in advanced analytics spanning from data analysis, advanced analysis, machine learning, text mining, and process mining - all under the same umbrella; Big Data.

 

 

 

John: What do you use process mining analysis for in Rabobank?

 

 

Frank: Process mining is one of the tools used in the Advanced Data Analytics department, and we use it for a lot of things. We explore process mining tools as a new way of looking at processes. We found that process mining was leading us to results way faster than the usual approaches, such as machine learning or manual process analysis. In addition, process mining gave us many new insights we did not have easily at hand previously, e.g. overview of ineffective processes, bottlenecks in processes and costs.

 

 

 

John: That sounds interesting. What is your main driver when initiating a new process mining project?

 

 

Frank: When we started out doing advanced analysis and using process mining in the organisation we had a very firm success factor: Go out and create some real value. We were not interested in applying these new tools and techniques and waiting five years to see results. So, we gathered together with managers and identified their urgent challenges and from that we discussed what the main driver should be. It turned out that the focus was on cost reduction. Keep in mind that we started out in the middle of the financial crisis, so cost reduction was a very natural element.

 

 

John: That certainly makes sense. Is cost reduction still a main driver when doing process mining projects in Rabobank?

 

 

Frank: It is, but another urgent challenge not directly related to cost reduction is also taking advantage of process mining techniques, for example working together with LEAN Six Sigma projects in order to apply facts based improvement or simply getting a grip on a primary process.

 

 

John: Getting a grip on a process and really understand what customers are doing, can be very valuable insights, even though it is maybe not directly related to cost reduction. Can you give an example of a case?

 

 

Frank: Yes, for example working with our mortgage process. Here we used process mining to really understand the process and especially understand what the customers are doing in the process. We had known KPIs attached, but during process mining analysis we found that process mining was able to acknowledge/proof some of the KPIs - but the analysis also led to adjusting some of the KPIs.

 

 

John: Interesting. You mentioned that you were working closely with LEAN Six Sigma projects. Can you elaborate on that?

 

 

Frank: Sure. A very interesting lesson learned is that there is a high degree of benefit when using process mining in LEAN projects, because the improvement initiatives become facts based. We also found that different profiles are needed when applying process mining to LEAN projects - to put it in other words; in our experience process mining consultants should not do LEAN projects and LEAN consultants should not do process mining, the best results emerges when the projects involve both LEAN consultants and process mining consultants in a joint effort. We find that LEAN consultants and process mining consultants is a very powerful combination.

 

 

John: That is a very interesting point. I would like to go a step back and ask you, how you got started and how you managed “selling” the idea of process mining to Rabobank.

 

 

Frank: Well, when I introduced process mining some years ago, process mining was a very new technique. I introduced process mining bottom-up trying to present it for people involved in projects or processes that could benefit from it. As mentioned before things got going when I presented it for managers with urgent challenges, and made some proof of concepts for some of the cost reduction projects. Another important aspect was that about the same time of introducing process mining in the organisation, an initiative around Big Data evolved in the organisation. Back then (and also today) Big Data was a huge trend. A lot of attention was on Big Data and how we could improve our business taking advantage of Big Data. That led to Rabobank establishing a Big Data proof of concept initiative, and indirectly that also led to establishing the Advanced Data Analytics department, where I am still working.  

 

 

John: Why do you think there was such a huge attention towards Big Data?

 

 

Frank: First of all it is a huge trend, and the financial sector is indeed entering the internet age with a shift of business towards digital business. We need to stay ahead of competition, so one direction was to see if all of our data could give us new insights; thus, new competitive advantages.

 

John: How did you approach performing the Big Data proof of concept?

 

 

Frank: We formed a small group of people. All enthusiasts. Then we established a set of success factors. The goal was to explore these new techniques and tools, and see what advantages and insights they could bring. The proof of concept was executed in around a year. Process mining turned out to be very valuable, bringing new ways of looking at processes.

 

 

John: I like the idea of forming a group of enthusiasts that get time in the organisation to prove that data and process analysis is a way forward. What is the most important advice to others, who would like to do the same?

 

 

Frank: Well, one good advice is to start out small. Focus on areas or processes you can get a grip on and where the data is accessible. Don’t start analysing a process where the process data is scattered over multiple systems. Also, don’t focus on technology. Technology should never be the main driver and get all your attention. Instead focus on what value you can bring.

 

Another advice is to keep going. You will definitely face obstacles from time to time - but always keep going, keep pushing and benefits and rewards will emerge.

 

 

John: What findings did you identify in the process?

 

 

Frank: One thing was that the process is explorative and as you go you will discover, that you will need more information than first anticipated, for example. Another finding was that having good enthusiastic evangelists inside in the team is very important. In addition, I found, as mentioned before, that LEAN people together with process mining people is a very powerful combination.

 

 

John: So, you are suggesting forming a team, that should lead the Big Data / process mining initiatives?

 

 

Frank: Yes, absolutely. Both that but also forming teams when approaching projects. We formed a group of enthusiastic evangelists with a passion for Big Data, process mining and data drives business in general. That turned out to be powerful. Create a small team that can perform, and be aware that it takes time to get things up and running. You need to create a movement.

 

 

John: I definitely agree. We have already talked about how to approach management and “sell” the idea / open up their eyes for the importance of process mining, but what obstacles were you facing when performing the actual process mining projects?

 

 

Frank: There are many obstacles to overcome, but a major one is of course on how to get the data. Even though most process steps are already stored in the IT systems, it can still be challenging. One thing is the extraction of the data, but sometimes the data exist in various formats and sometimes some of the attributes are missing. Some good advice, when trying to extract meaningful process data from the systems, is to start identifying the case-ids; unique keys identifying the cases for which you would like to analyse the process, and then as a next step try finding the corresponding activities, timestamps, and attributes.

 

 

John: That is clear. You mention a typical process mining obstacle, namely not having enough data or not having data in a sufficient quality. Our Process Mining Manifest addresses that as a need for on-going data improvement - treating your data as first class citizens. Do you have such initiatives as well in order to continuously improve the effect of process mining analysis?

 

 

Frank: We do, and it is certainly another obstacle. We try to address this by lobbying inside our organisation for more data and better quality. Hesitation from IT is sometimes, that adding logging will have a cost on performance and storage, but frankly, nowadays storage costs so little and enterprise architecture can easily overcome our logging needs.

 

 

John: Sounds like the way forward. What tools are you using for process mining?

 

 

Frank: We use a lot of different tools. For process mining, Disco is our favourite tool because of its usability. We also use ProM, but that is primarily for explorative use, as it is more cumbersome and less user-friendly.

 

 

John: I agree. Some BPMS suites incorporate process mining functionality, whereas Disco is a stand-alone process mining tool. Do you favour approaching process mining analysis with stand-alone tools?

 

 

Frank: At the moment we favour stand-alone tools. We try to integrate process mining analysis in ways of working and, as mentioned earlier, we try not to focus on technology, but to focus on business value.

 

 

John: That makes sense. Frank, it has been a pleasure talking with you and hearing more about how Rabobank got success with process mining. Thank you very much!

 

 

Frank: You are welcome!

 

Process Mining Mini Interview With Anne Rozinat

Anne Rozinat is co-founder of Fluxicon and one of the leading voices in Process Mining. We at ProcessMining.dk are proud business partners with Fluxicon delivering services on the award winning process mining tools.

Prior to this years Process Mining Camp in Eindhoven I have talked to Anne and summarises highlights in s short mini interview.

 

Enter Anne:

John: "Nice talking to you again Anne, and thanks for some great process mining sessions in Eindhoven. Speaking of Eindhoven, you moved to Eindhoven in order to work in the center of process mining. Can you tell me what is going on in process mining business landscape today - is Eindhoven still the place to be?"



Anne: "Absolutely. Process mining was born in Eindhoven 15 years ago, and the process mining group at Eindhoven University of Technology ist still the biggest and most active research group in the whole process mining field. Although we ourselves are not actively doing research anymore, we are still collaborating with the group in many ways. For example, at the moment we are helping them prepare this year's BPI Challenge, which is an annual process mining competition based on real-life data.

At the same time, process mining has really gone global. There are 170 universities all over the world active in our academic initiative for process mining research and education. And also our customers are located on all continents. So, from that perspective, the location does not matter so much.

We are also organizing Process Mining Camp again in Eindhoven this year on 18 June. Process Mining Camp is a one-day conference strictly focusing on the practical application of process mining and people are actually coming in from overseas to attend. If you take a look at http://processminingcamp.com you can see that very different use cases are covered by process mining in practice. "


John: "What do you see will happen in the Process Mining business in the near future?"


Anne: "I think that process mining will continue to gain more and more momentum. It is still a relatively new discipline and largely unknown. However, most people who do see it are immediately fascinated. It's such a great fit for everyone who needs to understand how their processes are running in reality. And anyone who has tried analyzing process data with Excel knows how hard this is. Process mining makes it easy to take these existing data and see how the processes really are.

It's great that you and your blog help increase the awareness for process mining in Denmark. Thank you for that!"

 

Infographic - Process Mining And Health Care

 

 

 

 

 

What Is The Differencebetween Process Mining And Data Mining

 

Data mining and process mining are some of the concepts that are very hot when talking about big data.

 

Big data is data that exists everywhere and it is the analysis of these data, that have named  a range of techniques - including Data Mining and Process Mining.

 

Both Data Mining and Process Mining goes under the concept, called Business Intelligence. Business intelligence refers to techniques and tools that are used to analyse large amounts of digital data and retrieve valuable business knowledge out of them. And that is true for data mining techniques as well as process mining techniques - albeit with different perspective on the analysis and the results they produce.

 

 

Let's look at some of the similarities between the two :

 

• Both techniques are used to analyse large amounts of data, that it would be impossible to analyse manually
• Both techniques produce information that can be used for making business decisions
• Both techniques use the "mining" techniques where algorithms traverse through large volumes of data, looking for patterns and relationships

 

 

Of course there are some similarities, as both techniques can be categorised as Business Intelligence. But, as mentioned before, the two techniques have different perspectives and goals.

 

 

Differences between Data Mining and Process Mining:

 

 

• Data mining techniques are using multi-dimensional views (cubes ) on data which can be drilled up and down (in different aggregated levels levels). For example, a sale of a product could have the related dimensions: price, product category , customer , region, country , area, day, month, quarter , year, and so on and it is then possible to slice and look at the cube of data and aggregated data in various ways.
• Data mining techniques are primarily used to find patterns in large data sets. With data mining techniques it may be possible to find that certain categories of customers demand a certain product, or to find that the customers who most frequently buy product A are also the ones most often buying product B , or that the products placed on a specific location in the shop while running an advertising campaign, are also the ones that sell the best. I remember an English department store which, through data mining techniques, found out that the customers who shopped the most were also those most often buyingt a special Italian cheese that otherwise was not often sold. Traditionally retailers would try to remove products with very low turnover rates and replace them with products with better sales - the problem is that the removal of goods according to the principle could lead to the best customers having to look somewhere else (for the special Italian cheese).
• The input to data mining are tables with data
• Process mining is not used to find relationship data patterns, but rather to find process relationships in the data. Finding process relationships that provide an overview of processes and activities in the process, and deviations and process performance such as throughput , bottlenecks and discrepancies.
• Process mining's perspective is not on patterns in the data but in the processes the data represents.
• The goal of process mining is to find information about the business processes
• The input to the process mining analysis are event logs , audit trails , and data and events stamped in the IT systems.

 

 

Process Mining bridging data mining and big data, and business process management

 

 

 

 

 

 

 

Process mining is the " missing link" between data mining and traditional BPM ( Business Process Management ). Data mining provides valuable insights through analysis of data, but is generally not concerned about processes. This is where process mining comes into the picture and gives the opportunity to get the same benefits of data mining ,when working with processes and process improvements.

 

Process mapping can be done with mining techniques instead of brown -paper workshops and interviews. And the process performance analysis can be made on existing data mining techniques without first collecting data through work studies.

 

 

The prerequisite for Process Improvement Is No Longer That We Have Diagrammed The Current Process

 

I came to think of a dialogue from Alice in Wonderland where Alice asks which way she should go . She does not know where she's going and the answer is ,that if she doesn't know where she's going , it doesn't matter which way she goes.

  

I have seen the quote sometimes associated with discussions about process improvements.

 

The thesis is that it makes no sense to improve a process , if we do not know the process we are currently having. For example , a company might realise that a given process is obviously not effective and sensible.

 

This could for example be the sales process , where you can see on various KPIs and metrics that we apparently are not effective; and perhaps some of the people involved even give examples of instances in the process where they see are discrepancies .

 

It is so tempting to adopt a new process. The problem is that if we do not have any idea what the current process looks like, it is impossible to improve it - at least impossible to know whether it is a real improvement or not.

 

It corresponds somewhat to say that the way we drive to work is not the most optimal way, and simply decide on a new way to drive to work. If the way chosen to drive to work are often varies, and it is not known for sure how long time it takes - how can we know that the new way to work is a better choice ? It's the same with process improvements.

 

In order to improve a process one needs to know the current process, in order to build on it and improve it where it is inappropriate. Unknown current processes can not be improved.

 

There may be adopted a different / new process , but an improvement of the existing process , it is not - it's just another process - which may not be better than the current process.

 

Typical start of process improvement work

 

That's why process improvement work typically starts with determining what the current process is. This is called the As-Is process. The road to an As-Is process in sufficient quality to make process improvement, can vary a lot in terms of time used. The more complex and the more ways to carry out the current process , the longer it will take to get the process outlined.

 

Typically, designing As-Is processes involves facilitating a series of workshops . These workshops are facilitated often by interviews, followed by questions to key process steps, on what happens in the process. What happens prior to a process step , what happens after a process step , does it always works like this etc.

 

The traditional approach to outlining the As-Is process is illustrated below:

 

 

 

 

 

 

The output of the As-Is process is first a lot of notes and draft drawings, which are drawn into a modelling tool on a computer, using correct notation and drawing styles (step 3 in the illustration above). It could look like this:

 

 

 

 

 

 

There are disadvantages associated with the traditional approach

 

Firstly, it can be a slow process. The business analysts who establish the As-Is diagrams, in a proper structure, will need to know the process in detail. This is done, as mentioned before, by facilitating a series of workshops. And it takes time before the process is fully understood and fully detailed .

 

Besides being a slow and time consuming approach, it also puts work load on the operating organization of the company. You will have to engage on key employees for workshops, follow-up questions and verification.

  

 

With process mining it is different

 

With process mining is no longer necessary to prepare As-Is process maps before you can begin optimising the process or begin analysing how the process performs. The trick is that process mining can generate the As-Is process map automatically from the data that already exists in your IT systems.

 

The vast majority of processes is now supported by IT.

 

Think for example of a sales process. When creating an Order, it is registered in IT systems. It is registered in IT systems when the customer is billed. It is registered in IT systems when payment is received. It is registered in IT systems when the goods are shipped. All these process steps are already stored in IT systems.

 

When the data is pulled out of IT systems, process mining tools can automatically draw the As-Is process diagrams without involving the business key employees .

 

 

Illustratively, process mining approach to outlining the As-Is process looks like this:

 

 

 

 

 

 

 

Process Mining is one of the most innovative new elements of Business Process Management. For a detailed description of Process Mining in Danish see Den Danske Blog om Process Mining or see the English site www.processmining.org

 

 

Documentation Quality Evaluation - Free Tool

Download Documentation Quality Determination

Tool for evaluation the quality of the documentation.

Evaluating and Scoring Documentation Quality

Documentation is important. Most of us believe this is true - at least to some degree.

Most people would agree with me that documenting business rules and core functionality is important. The reason is that it makes it easier to update and migrate the solution - and more important it enables agility in a world of ever changing business rules.

However, one valid question is: When is the documentation good enough? not an easy question to answer because it depends on your need for details and how critical the functionality is.

 

Questions you could ask yourself could be:

 

Do we have documentation describing how the (functionality of the) solution works?

 

Can we at any time read from documentation how the functionality is implemented and which business rules are addressed?

 

If not, is that a problem and what is the impact?

Answers to questions like those gives you a hint on how to approach your documentation. Not all your documentation artefacts need the same detail. After creating the documentation or even better after receiving documentation from a vendor it needs to be reviewed and you need to determine if it is good enough - if it meets your quality expectations.

 

The problem is though, that there are as many opinions about the documentation quality as people looking at it. To cope with that challenge I have created a small tool for evaluating documentation quality. The tool consists of a number of parameters evaluated from a objective perspective and the result is illustrated in a kind of a heat map as shown below.

 

 

 

The scoring of documentation quality is based on taking each documentation element (e.g. functional requirements, use cases, business rules, domain model, etc.) and rate each of them according to criticality and quality. 

You will end up with a score for each documentation element as shown below and a corresponding heat-map as shown above.

 

 

 

 

The Critical Rate as well as the Quality Rate is meant to be as objective as possible. 

Therefore the criticality (determining how critical a documentation element is for your solution is determined by a rate from 1-5 based on the criteria shown below:

 

 

 

 

The Quality rate (Evaluation score) is also determined by a rate from 1-5 and is based on the criteria shown below:

 

 

 

Pay attention to the criteria for rating 5. Only if the question can be answered with a "Yes" AND if it has been verified, rate 5 can be given. So, it's not enough to know that a certain question can be answered with a "Yes", you also need to verify with your own eyes that it is in fact true.

Now that you know the criteria, the first thing to do is to score each of the documentation elements.

Need more Documentation Elements to score? Just add them to the list of elements, add a sheet with questions determining the best practice of that particular element. That way you will improve your documentation evaluation as an on-going process.

Have you become better at documenting a particular element and need that as a new best practice and new benchmark? Simply update the questions needing answers in the elements sheet.

Let's have a look at the scoring sheets for Business Rules, Functionality, and Detailed Functionality.

For scoring Business Rules I have suggested the criteria shown below. Feel free to add your own criteria.

The important thing is to construct the questions so that you can determine what you think constitutes a good documentation quality.

In addition, make sure to use the same questions for every project, so that they are comparable.

 

  

 

 For the scoring of the Functional Requirements I have suggested the criteria shown below.

 

The last element included in my template (make sure to add a sheet for each element) is the Detailed Functional Requirements, shown below.

 

 

For the Detailed Functional Requirements I have suggested a slightly different scoring approach, using the COSMIC Function Process approach to determine the level of documentation.

The COSMIC Function Process approach says that the size of a piece of software is determined by the total number of data movements (Create, Exit, Read, Update and Write) summed over all functional processes.  Each data movement is counted as a COSMIC Function Point (CFP). 

Using the COSMIC Function Process approach you'll be using a standard approach which is easy to apply.

Below you can see how each documentation quality for each data movement is rated according to the COSMIC Function Point approach.

 

 

If you want to read more about the COSMIC Function Process approach you can check these links:

http://en.wikipedia.org/wiki/COSMIC_software_sizing

http://www.cosmicon.com

Download Documentation Quality Determination - tool for FREE DOWNLOAD

 

 

See mine and the other contributions for the International Business Process Intelligence Challenge 2013 in Beijing:

http://www.win.tue.nl/bpi2013/doku.php?id=challenge&#results

Inputs from Danish Project Management Symposium 2013

 

 

Just arrived back home from Danish Project Management Symposium 2013 where I was speaker in one of the seminars.

Here is a short heads-up on some of the interesting inputs from the project management world with relevans for Business Analysts:

The organisational value of the project will in the future be something that will be owned, managed, and controlled by the business and it will evolve throughout the entire project life cycle. Today, in many project management methodologies, the value of the project is determined at project start, fixed in business case, project charter and the like - and is rarely updated. That is about to change. It is difficult to identify all project benefits up front, and the business must continuously evaluate and monitor if the project benefits are the desired ones and if the project is on track in order to reach those benefits. For the business analyst it means that the project value/benefits will be changing, and that it is ok to identify benefits as the project evolves. It also means that the benefits identified by the business analyst can now be addressed directly to the business and not risk simply being ignored by a project manager more keen on just delivering the deliverables identified at project start.

 

The managing effort will get more attention in the future than the focus on methodologies and models. It will have more relevance to get the right think done, involve the right people, get the most value out of your team than following and mastering a specific methodology.

 

The projects are getting more and more complex, and the projects are continuously under time pressure and budget pressure. Therefore the transaction costs must be lowered. There are no longer time and energy left to micro-plan, micro-manage and follow methodologies just of the sake of the methodology. You need ways to produce outputs fast. You need to produce outputs without having to be managed in details by a manager - you have to be able to think and work on your own.