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

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

2015 trends

In March 2014, I had the pleasure to spend the day at CeBIT, the world’s largest and most international Information, communications, and technology conference. I viewed a few exhibition stands and attended a few conference presentations.

I walked away with two impressions: (1) the digitization affect every aspect of our lives and (2) the number one concern of business and governments is cyber security. At that time it was thanks to Snowden (and more recently it is thanks to North Korea).

My digitization conclusion started when I saw the NAS servers for the home with backup options to the cloud. I can envision it now–a wash room with the washer and dryer for my cloths and a data center with all my servers, fiber connections, and various monitoring equipment. Further digitizations options extended to the car (VW), to online shopping (zoolando), to medical treatment options, to educational systems, and to luggage with monitoring options.

Regarding cybersecuity, the buzz was related to European laws that data should not be stored on servers in the United States. There were several technology and services solutions presented in this regards. Further debates related to having or not having national internets, including the benefits and drawbacks, the advantages and disadvantage, and the political and business implications.

I end the year with the view that the key trends in 2015 will be:

  • big data, including analyzing and using all that sensor data
  • cloud computing for business and home,
  • digitization in every aspect of life, and
  • cyber security.