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.

http://bit.ly/2LOUWft

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).

3. PM Stammtisch: Success Criteria for BI and Big Data.

In the Project Management Stammtisch on the 30th of November, we covered “Success Criteria for BI and Big Data.”

There were two different types of criteria for Big Data Analytics projects: one area has six success criteria and the second had only one. The Business Intelligence projects also had one success criteria, which was different from that for Big Data Analytic projects. These criteria were not the project efficiency criteria of on-time and in-budget delivery. In fact in the project efficiency area, we could identify meeting requirements as the most appropriate success criteria. All of this provided for a very interesting discussion during the session.

The next session focuses again on success factors specifically “Stakeholder Influence on System Use and Success for BI and Big Data Projects” on Friday, 25.01.2019.

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.

References:

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.

Demographics

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.

Bottomline

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

Can we Motivate you to Share your Knowledge?

Knowledge Management (KM) implies creating, using, sharing and managing the knowledge of people from an organization. Some of the benefits of supporting the processes of KM are clear and well-known. The PMBOK(R) Guide 6th edition adds the chapter 4.4.2. “Manage Project Knowledge: Tools and Techniques” in recognition of KM importance.

KM is about people, processes and technology so it should support people and make their work-life easier. It is important for:

  • avoiding reinventing the wheel

  • reusing documents and ideas

  • taking advantage of experienced employees and their expertise

  • avoiding making the same mistake twice

  • rewarding through a nice recognition system

About How Technology Changed People and Organizations

In the past, when you knew how to do or make something you would share your know-how with family, friends and neighbors in a natural and easy way. It could be how to paint a wall, how to cook a certain dish, how to repair a shoe or anything you can imagine of. Elderly people had more experience and expertise and were happy to share it with the younger generations. It was their purpose in life.

When did this purpose of life change? Was it when the Information Technology started to dominate the world? It probably was. Nowadays elderly generations are often no experts in new technologies. It is rather the younger ones who teach the older ones how the new technologies work. Is this generally applicable to the employees of an organization? Don’t people from previous generations have more experience that could be also useful? How can we make sure that the technology works for all the people and not vice-versa?

Using IT-driven KM can be a double-edged sword. Technology connects those who have the expertise with those who need it when they need. It opens doors for communication and information exchange across organizational and geographical boundaries. But, does it also facilitate exchange of knowledge? Technology can also close doors when it comes to sharing knowledge. If the technology is too complex or requires too much time and effort, then it is more a barrier than a help.

About Creating a Knowledge Sharing Culture

Many organizations struggle to run effective KM processes. ‘Knowledge’ on its own is related to a person and his/her ‘know-how’, his/her ability doing something or his/her expertise on a certain topic gives him/her a certain power.

It is crucial to create a culture that supports knowledge sharing and re-use. Making people understand that knowledge sharing can benefit them personally through examples of how they can improve their performance is the base of this culture. Putting the own experience into words or writing it down is extremely valuable. The face-to-face exchange of experience can happen in a formal way such as in a meeting or a workshop or less formal such as at a coffee corner.

In order to create such a knowledge sharing culture, this should be supported by all the departments and be part of the job requirements. In my experience as a former administrator of a KM system, colleagues often said they had no time to complete any KM process, they were too busy. Normally they mean it is not a priority. Including it in job descriptions and using it in appraisal programs would support KM culture to a large extent, and would make everyone spend time on sharing their knowledge.

It is popular to share best practices, but not to share ‘worst practices’. Even though you would probably learn more from what-went-wrong than from what-went-well. These ‘lessons learned’ would provide some essential learning, no matter what generation you belong to. We should celebrate mistakes in order to avoid making the same mistake twice.

About the Process of Motivating People

Many people think they become dispensable when they share what they know, what they learned while managing a project. How can we motivate employees to share their knowledge and expertise? Somebody with wide project management experience said: “You cannot motivate them, you have to force them”. I agree only partially. I have seen that colleagues shared their knowledge for three reasons:

  1. If they were forced as it was part of their performance plan (e.g. bonus),
  2. If they received positive feedback from their contribution
  3. If they felt they were recompensed. Recompense does not have to be monetary, but moreover in form of recognition and value: Public recognition, acknowledgment, a symbolic prize, or a voucher.

You only become dispensable when you stop adding value to the company, when you stop learning, when you don’t feel being part of something.

Sharing knowledge is still a challenge for many organizations. It is the human nature to love having power. Since knowledge is power, it remains a challenge to motivate employees and teams to share this power.