Remove Governance Remove Presentation Remove Process Remove Software Engineering
article thumbnail

Implications of Artificial Intelligence on Project Management

The IIL Blog

AI technology will automate repetitive processes, generate insightful program reports, and highlight potential problems before they arise. The future of AI in project management will depend on how we implement, use, and govern it! Eugene Bounds is presenting at this year’s #IPMDay2023! Check out his presentation details here.

article thumbnail

Project Delivery through the Definition of Done

Project Pulse Journal

Ready to transform your project delivery process? Learn how to collaborate effectively, detail deliverables, set standards, adapt to feedback, and continually refine processes. This definition ensures transparency and quality control as features pass the development process. What is the Definition of Done?

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Project Management Maturity Models: A Basis for Reaching Your Organization’s Business Success

Epicflow Blog

The main purpose of determining project management maturity is to assess the actual state of the project management process in an organization as well as detect areas and direction for its improvement. . Assessment presents methods, processes, and procedures that a company can use to self-assess its maturity. .

article thumbnail

12 Must-Have Skills for a Data Analyst You Should Test

Teamweek

The real difference is that a data scientist is usually more experienced and works with more complex data processes. And keep your candidates engaged with an efficient screening and assessment process. Data processing and transformation — making the data more accurate and ready for analysis. Hire the right person for any role.

article thumbnail

Reading List for the Cone of Uncertainty

Herding Cats

Project Management Institute (PMI) [8] presents similar results about uncertainty, except that their results introduce an asymmetric cone where initial estimates vary between +75% and –25%, budgetary estimate between +25% and -10%, and the final estimate between +10% and -5%. 37–48, 2007. Quantifying IT Forecast Quality,” J. Eveleens and C.

2012 48
article thumbnail

Misunderstanding Making Decisions in the Presence of Uncertainty

Herding Cats

This quote demonstrates a lack of understanding of making decisions in the presence of uncertainty and the processes and events that create uncertainty. There is naturally occurring variability from uncontrolled processes. Aleatory uncertainty is expressed as a process variability. First, let's establish a principle.

2003 46
article thumbnail

Risk Management Resources

Herding Cats

Taxonomy-Based Risk Identification,” Marvin Carr, Suresh Konda, Ira Monarch, Carlo Ulrich, and Clay Walker, Technical Report, CMU/SEI-93-TR-6, Software Engineering Institute, June 1993. IEEE Transactions on Software Engineering , Vol. Norton, The Systems Engineering Process Office, MITRE Corporation, 6 June 1999.