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

2013…What a year! maxmetrics successes and challenges

Once upon a time.. I was very naive to think I could spend a few days or weeks to write a book: “Going Agile Project Management Practices”. I just wanted to document some of my experience about project management specifically being agile. Well, in the end the book project took six months and involved about 35 people. During this time, I was overwhelmed by the number of people that volunteered experience, input, feedback, and time to write, rewrite, review, etc. Just to think about the support I received makes me quite emotional.

Now it is one year later, and we have sold 500 copies of the book. That is amazing, because we did not expect to sell any. Ok, we thought we would sell a few, but mostly we wanted to use it for our consulting and workshops. We wanted people to know that we have an experience and a voice on project management. In addition, to the book success, we have been involved in some of the most interesting and innovation projects in the Business Intelligence space. We have had engagements around Grid, Social Media, Visual Analytics, Cloud Computing, Competency Centers, etc. Therefore, in the professional sense 2013 was a success.

On the other hand, our personal lives were a bit more strained. We have lost some good colleagues and friends. For this we are deeply sadden and have a great sense of lost. But, the people we lost were such a powerful influences in our personal and professional lives that they are with us almost every day. I often find myself still making decisions based upon their influence, trying to show as much passion and professionalism as they did for their tasks, and finding the kind of joy and beauty in people, places and activates that they did.

2013…What a year. Now, I would just like to thank you for your support, your thoughts, and your business. We look forward to 2014 with much anticipation. To follow our activities, like our facebook fan page and follow us on twitter @maxmetrics.

Best regards, Gloria  & Victoria

Business Analytics Marktführer SAS stellt Führungsposition unter Beweis

1      SAS Forum Deutschland 2013 mit Rekorden

foto-in-sys-office-2010SAS Institute zeigte sich auf dem SAS Forum Deutschland 2013 vom 11.-12.09.2013 in Höchstform: Besucher-Rekord, nagelneues Software-Release SAS 9.4, erfolgreiche Kundenprojekte und viele hochinteressante Präsentationen und Diskussionen. Erfreulich ist die Kehrtwende weg von Marketing-lastigen und Technik-entleerten Vorträgen hin zu konkreten Lösungs- und Projektvorträgen, Hands-On-Möglichkeiten, Live-Vorführungen und sogar einem Programmers’ Corner, wo echte SAS-Entwickler sich austauschen konnten.

SAS stellt mit seinen neusten Software-Entwicklungen seine führende Rolle auf dem Business Intelligence Markt unter Beweis.

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