What’s new in the world of projects? #agile #pmot

We are updating our project management experiences and need your input!  We are trying to understand the relationship between the roles involved in a project and project success.

After such great success in 2017 collecting field experience through a survey, we want to repeat the experience.  We have a survey running at the following link that will take you less than 10 minutes.


Also, we are looking for a few projects to interview 4-5 project members including, the sponsor, project manager, agile coach/scrum master, product owner, and a few team members. Each interview with take about 15 minutes.

Our plan is update our book “Going Agile Project Management Practices Second Edition”.  For your support, we can download an ePub or Kindle of the current version of the  book. The link will be provided at the end of the survey.

In addition, for those that contribute before 11-January-2019 we will add you to our list for a free download copy of the next book edition when it publishes (note your email to receive the study results).

Seeking feedback! #BI #Bigdata #Analytics #PMOT

Based on our study from 2017, we built a classification model for Business Intelligence and Big Data Analytics projects. We would be interested in how well the model classifies your project, and if the reporting information is relevant for project management. You can select “generate report” and provide your project specifications at the following site: http://bit.ly/2qRdzG2 .

Let us know your feedback.

In addition, on the following dates, we will discuss the study results in our office in Heidelberg, Germany. Please contact us if you wish to join.

  • Success Criteria for BI and Big Data Projects: Friday, 30.11.2018
  • Stakeholder Influence on System Use and Success for BI and Big Data Projects: Friday, 25.01.2019

2. PM Stammtisch: Impact of Multidisciplinary Teams and Data Scientist on Project Success

In the Project Management Stammtisch on the 26th of October, we covered “Impact of Multidisciplinary Teams and Data Scientist on Project Success Project Management Stammtisch. ”

The research in this area offered an intriguing story about teams and individuals. The profiles of the participants were of mixed: An Artificial Intelligence specialist managing international projects, an Agile Coach, Scientific Journal Editor…Nevertheless, the group came to a similar conclusion: having specialist in the team is important for learning, building a well-functioning team with mixed profiles is important to success.

We used the session as an opportunity to announce the availability of a system to generate a custom target project planning report. https://www.pmxtra.com/dspcsf/Index.php.

DSP Entry Screen
DSP Entry Screen

1. PM Stammtisch : Project Success Factors for BI and Big Data

In the Project Management Stammtisch on 28-September, we covered the topic “Project Success Factors for Business Intelligence (BI) and Big Data.”

The discussion was on a comparative analysis of Big Data Analytic and Business Intelligence projects from our project success study. In short, the study compared 52 demographic and project attributes. None of the organizational demographic (e.g., industry, organization size) or project demographic or efficiency factors (e.g., team size, budget, duration) items were significantly different. Also, business strategy, top management support, and client acceptance were not significantly different. Of the 39 remaining items, 18 were significantly different all in favor of Big Data Analytics. This information reflected the discussion of the session participants. Especially interesting was the role of Senior Managers in the success of the projects. The participants reflected that Senior Managers could act as a buffer between the project and top management to ensure the project maintains an agreed course of action until a successful outcome is reached.

The following diagram reflects the differences in the project complexity, pace, technology uncertainty, and product novel between Big Data Analytic and Business Intelligence projects. Complexity and product novelty were significantly different. The diagram is based upon the Diamond model for project success from Shenhar, A., & Dvir, D. (2007).

Figure 1: Project Attribute Comparison

You can find a conference paper on the study comparison at the following location: https://annals-csis.org/proceedings/2018/drp/pdf/125.pdf.


Shenhar, A., & Dvir, D. (2007). Reinventing project management: the diamond approach to successful growth and innovation. Boston, Mass.: Harvard Business School Press.

Miller, G. J. (2018). Comparative Analysis of Big Data and BI Projects. Paper presented at the Proceedings of the 2018 Federated Conference on Computer Science and Information Systems. http://dx.doi.org/10.15439/2018F125

Benchmark : BI, BigData, & Analytic Project Success

Based upon our Decision Support Project Survey, we have created a template for a benchmark comparison that can be used to share best practices or identify areas of improvement.  The survey analyzed 78 projects for critical success criteria and factors and created a classification model. The classification model has been documented and peer reviewed by members of the Computer Science and Information Systems community. Based on the model, the classification of the project is given and comparisons are made to other respondents from the survey

Figure 1: Decision Support Projects Benchmark Comparison

In our PM Stammtisch, we will present the results of the study and discuss how the results can be used in practice.

  • Success Factors for Business Intelligence (BI) and Big Data Projects: Friday, 28.09.2018
  • Impact of Multidisciplinary Teams and Data Scientists on Project Success: Friday, 26.10.2018
  • Success Criteria for BI and Big Data Projects: Friday, 30.11.2018
  • Stakeholder Influence on System Use and Success for BI and Big Data Projects: Friday, 25.01.2019

Survey results: BI, BigData, & Analytic Project Success

The aim of the research was to understand the success criteria for decision support projects and what influences the performance of those projects. “Decision support projects are implementation projects that deliver data, analytical models, analytical competence, or all three, for unstructured decision-making and problem-solving. They include subspecialties such as big data, advanced analytics, business intelligence, or artificial intelligence” (Miller,2018).  This report summarizes the survey inputs from and analysis from 78 projects. The first section provides descriptive statistics for the data that was collected as part of the survey. The second section provides summary of the analysis that was performed with the survey data.


The majority of the projects were undertaken as internal projects by large organizations, with big teams and networks of involved organizations. They were diverse in terms of complexity, pace, novelty, and team structure. The participants were from 22 countries with 73% being based in Europe.

Project Classifications

Analytic competency and building analytical models and algorithms are characteristics that differentiate the decision support project types.

Critical Success Factors

System quality and information quality are critical success factors that influence system usage and system usage influences project success. Project schedule and budget performance are not correlated with the other success measures so they are not critical success factors in most cases.

Figure 1: Interactive chord diagram of variable correlations

Stakeholder Contribution

Business user, senior manager, top management, and data scientist participation in project activities such as requirements and model building is a benefit. It increases the chances of achieving organizational benefits months or years after the project has been completed.


The recommendation is to actively engage business users and senior managers in hands-on project work such as building models and to focus on providing sufficient system and information quality.  As a consequent, the project should deliver long term organizational benefits.

Next Steps

On the following dates, we will discuss the study results in our office in Heidelberg, Germany. Please contact us if you wish to join.

  • Success Factors for Business Intelligence (BI) and Big Data Projects: Friday, 28.09.2018
  • Impact of Multidisciplinary Teams and Data Scientists on Project Success: Friday, 26.10.2018
  • Success Criteria for BI and Big Data Projects: Friday, 30.11.2018
  • Stakeholder Influence on System Use and Success for BI and Big Data Projects: Friday, 25.01.2019

Free “Going Agile” ePub for PM / BigData /Analytic / BI survey input by 1-Oct http://bit.ly/2jRUhzx

I am doing some research to investigate success from a project perspective of the different types of decision support projects and their contribution to organizational performance. Join the effort and get a free “Going Agile Project Management Practices Second Edition” ePub or Kindle book (worth 32 USD on amazon).

Project managers, agile coaches, project team members, and sponsors that participated in a big data, business intelligence, or analytics project since 2002 can to take the survey. http://bit.ly/2jRUhzx

So far, people from 14 countries have contributed. Regards, Gloria

Building a Team with Collaborative Events

The agile coach has the responsibility to build the team into a self-organizing unit that can make its own decisions and resolve its own conflicts as well as collaborate as a team to deliver the product. Building a team is much more than building a working group. ‘Going Agile – Project Management Practices’ says the agile coach has to

  1. form the team and develop it through different stages,
  2. build collaboration amongst the team members,
  3. set the framework for group decision-making, and
  4. facilitate conflict resolution.

He or she has to take a newly formed group of individuals from individuals working together, which would be a ‘working group’, to a team. Teams go through stages of development and they tend to reach their best performance after having been established for a while. Bruce W. Tuckman identified four phases of development concerning interpersonal relationships and task performance:

To ensure the team members are clear about the project goals, the agile coach should use a collaborative event such as the chartering session, and create an environment that allows the team to collaborate in the creation of the product. Collaboration is two or more people or organizations working together to realize shared goals.

Collaboration event – Planning session

The diagram is for a project planning and estimation session. It was developed together with project team members and the client. It was used in the forming stage of the project. It supported alignment of team members towards the project goals and with one another, and a cross understanding of project tasks that needed to be completed. In the opinion of the project manager, it reduced the duration of the storming phase.

At the top of the diagram are hand drawn boxes that would represent phases in the project. Going from an initial deliverable in the first column and to building successive components until the final product on the right side. Wide-band estimation according to Fibonacci numbers suggested by Planning Poker ® were used.

The components of the diagram are:

  • The sticky note (yellow and pink) represents the tasks that have to be completed.
  • The color represents if the tasks could be completed by the project team (yellow) or if it was sourced from a third-party.
  • The tabs represent the estimates for the task.
  • In the lower left is the legend for what colors represent what levels of effort.
  • Each post-it® received an estimate if possible.
  • If it was not possible to estimate the effort, the task was placed in the box in the lower right corner.

According to the visualization recommendations, the design of this visualization fits the recommendations.

  1. Make it action-oriented:  Created during the session; retained as reference during the project
  2. Make it messy: the order developed during the session and its ugliness was retained
  3. Make it specific to the situation — It was for a specific project.
  4. Use different dimensions — it uses different…
  • Position:  represents the evolution of the product over time
  • Colours: pink, yellow, green
  • Shapes: oblong post-its are tasks; tabs are estimates
  • Numbers: are used as estimates

5. Make it interesting – the team was motivated and engaged.