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

Socialmedia use and monitoring in the workplace

In his January 2014 Computing article “Corporate Risks from Social Media”, Brian M Gaff highlighted several risks related to the use of Social Media in the workplace. While Brian encouraged organizations to leverage the benefits of social media, he suggested that they should be vigilant in establishing  internal controls – policies and policing.

His explanation of the legal issues or penalties I could face if my social media site was hacked and customer data was compromised was of great concern to me. Therefore, I looked into the topic  a little more and I summarized my findings:

  • as an inforgraphic on “Social media use and monitoring in the workplace”  (Figure 1) and
  • as a table on social media monitoring tools (Table 1 & Figure 2).

My conclusion after the investigation is

  1. social media use is accepted in the workplace,
  2. the negative consequences (for me) are marginal, and
  3. there are enough monitoring tools available to track and manage the risks.


Gaff, B. M. (2014). Computing and the Law: Corporate Risks from Social Media. Computing Now.

Institute, P. (2011). Global Survey on Social Media Risks: Suvey of IT & IT Security Practitioners.

Figure 1:  Social media use and monitoring in the workplace

SocialMedia Risks v6-01

Figure 2: Treemap of Social media monitoring tools

social media tools v2-01

Table 1: Social media monitoring tools

Topics Products
1 Finding people to follow Commun.it
2 Get alerts when you are mentioned on the web GoogleAlerts
3 Conversation Tracker Conversion suite (Google Analytics)
Social Revenue (Argyle Social)
4 Identify influence Google Analytics (Social Reports)
Google+ Ripples
5 Schedule tweet/automaion Buffer
6 See incoming posts in a workflow Attensity
Sprout CRM
Conversation Center (SAS)
7 See all social media accounts Angorapulse
8 Tracking clicks on links that you sent Backtweets
9 Get information on how posts travel Google Analytics
Hub Spot
Map my followers
Tweet Reach
10 Controlling outbound tweeks Crowd control
11 All (or many) in one Beevolve
Google Analytics
Social Media Analytics (SAS)

Trademarks are property of their respective companies.

Federico Bellani on “Adoption and evolution of agile practices over time”

Today, Federico Bellani released his research on the Adoption and evolution of agile method practices.  The research is an extensive litature review supported by survey results from 194 worldwide participants and extensive survey analysis.  Some of his findings on agile practices fall inline with the results from the VersionOne annual survey on the state of agile.  Specifically, Continue reading “Federico Bellani on “Adoption and evolution of agile practices over time””