Competency E
Work collaboratively in teams and use project management practices effectively to solve user-centric information and data problems;
Personal Definition and Importance
The focus of this competency is around project management and collaboration in the service of informatics goals. While data management and governance are ongoing operations, project management is about creating a specific product by a specific date to solve specific problems. Informatics is not only about project management, differentiating informatics project management from ongoing informatics operations is crucial. It is also important to understand and tailor to different types of projects. Collaboration should not only consider commercial end-users or patients, but also the different stakeholders within and outside of an organization. I believe all informatics projects are geared towards connecting users with information and data in a responsible manner, these are factors to be considered in the scope and goal of any project.
Supporting Informatics Courses
The SJSU Informatics course that focuses primarily on project management is INFM 205 Informatics: Project Management. This course was centered around the Project Management Body of Knowledge (PMBOK) framework, however we were allowed and encouraged to explore many different principles and tools. I did explore working collaboratively in other courses and have presented evidence in Competency A about working in a group to redesign a website. I also explored Agile project management in evidence presented in Competency D. However, it is INFM 205 that exclusively addressed and explored project management. I also learned a highly specific data science framework for data science projects in INFM 203 Big Data Analytics and Management.
Evidence
Evidence 1: INFM 205 Term Paper: Portfolio Management
I selected this paper because I think it important to specialize in projects that are about informatics. The paper explores portfolio management, where project management is agnostic and can be applied to construction projects and IT projects alike. Portfolio management is filtering and specializing in specific types of projects. I argue that as an informaticist it is vital to specialize in IT project management. I explore classifications of projects in IT. Two that I touch upon are data governance implementation and cybersecurity projects: which are finite and limited in scope at the project level. Of course data governance and cybersecurity are ongoing operations, but it is necessary to know when an activity is a finite project and when it is a normal, everyday operation.
Evidence 2: INFM 205 Project Management Strategy
This paper highlights what I believe to be the 3 most important points to formulate and manage in any project: Scope, metrics, and risk management. I propose that by focusing on those three aspects of a project a specialist in certain types of projects can build a reusable framework that only needs to be tweaked on a project by project basis. A scope needs to be clearly defined in order to prevent scope creep, but also to fundamentally understand the project and identify metrics and risks. The paper explains that a scope can be looked at from a high level as having 3 important processes: planning, controlling, and closing. The metrics are needed to measure the success of any project and alerts for risk mitigation. Metrics can be categorized as productivity, qualitative, or quantitative. Risk management will help to have a mitigation plan in place and to keep a project on time. While everybody involved in a project should be alert for risks, there should be assigned sentinels who deeply understand the factors and can recognize threats as part of risk management.
Evidence 3: INFM 203 Mini-Project
I selected this paper because it shows a specialized project management process around data science. Data science projects designed to gain actionable insight from data to give value to both businesses and customers. This is different from creating a final product for end-users, but more for extracting value from data for future projects. A data science project is focused on data and the cycle has phases which are the prepare phase, the plan phase, and the finish phase. The prepare phase is where questions are formulated and data wrangled. The plan phase is where the data is used to build and gain insight. The finish phase is where the results are finalized and delivered for use. Within each phase are many steps where iteration occurs back and forth, and the same thing between phases. Iteration happens between each phase, sometimes having to go back to the beginning. Eventually however there is an end, it is not an ongoing operation but a project with a finite ending.
Professional Application Value of Skill
The evidence above shows my ability to differentiate project management from ongoing operations. As an informaticist I can identify and classify types of IT projects. I can identify important markers and mitigation strategies in a project, governed primarily by defining and enforcing project scope. I can also evaluate and identify tools in order for teams to be able to use in the aid of simplifying cooperation on a project. There are many different project frameworks from PMBOK to Agile to specialized data science frameworks. Project management frameworks aid in formulating and completing projects, and knowing many different frameworks and how to apply them to specific types of informatics projects is something that I appreciate. I feel like I could provide valuable feedback and constructive criticism on a project management team.