Keeping the company’s IT projects running in an efficient manner remains the goal of your technology managers. Part of this approach involves understanding common project failures and what steps need to be taken to avoid them. Ultimately, a proactive approach ensures a successful result, under budget and ahead of schedule.
So let’s take a closer look at a few common technical project failures and how to prevent them. Leverage these insights to ensure your project team works effectively while managing the expectations of business stakeholders. In short, don’t let these kinds of issues lead to a failed effort.
In nearly all technical undertakings project managers serve as a conduit, ensuring communication flows freely. This role becomes even more critical when using an iterative methodology, like Agile or Lean Startup. Business stakeholders need to ensure their expectations are communicated effectively to the project team.
Poor communication remains an important risk to the success of any project. Leveraging Slack or another collaboration tool helps improve the interaction on a project team. Still, managers must be proactive to ensure the right messaging gets delivered to everyone on the project.
Companies typically start technology projects to fix a pressing business problem or take advantage of a newfound opportunity. The project team in tandem with business stakeholders crafts detailed requirements based on that underlying issue. If these requirements lack detail or poorly explain the work to be performed, expect a difficult project. Again, the project manager holds the responsibility to ensure the business problem and technical solution are clearly defined.
Any technology initiative requires a detailed project plan and schedule. As noted earlier, this is especially the case with modern methodologies, like Agile or Kanban. A poor project plan lacks the necessary detail to schedule the programmers’ work with accuracy. Don’t be surprised if the project misses its deadline.
Sometimes, companies simply build the wrong team for a project. For example, a project crafting a machine learning model that lacks anyone with experience deploying an ML model into production. So before the project begins, ensure the entire team boasts relevant experience and technical skills.
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