What do you do if your data science project is falling behind schedule?

??Identifying the root cause of a slipping schedule is crucial for getting your data science project back on track. ??Visualize Your Workflow Utilize project management tools like Gantt charts or Kanban boards to visualize task progress and dependencies. This helps pinpoint bottlenecks and areas requiring adjustments. ??Revisit Scope & Goals Ensure project scope, objectives, and deliverables remain aligned with stakeholder needs. If not, consider renegotiating the scope or prioritizing critical features. This keeps everyone focused on achieving the most impactful results. ??Taking a step back to diagnose the situation empowers you to implement effective recovery strategies and deliver a successful project.

If a data science project is behind schedule, assess reasons, communicate with team, prioritize tasks, streamline processes, adjust timeline, and inform stakeholders. Take proactive steps, allocate resources efficiently, and foster collaboration to expedite progress and ensure successful completion.

Just look at the bottlenecks and avoid bandaid solutions, the below solutions assumes team has resources that respect their jobs Bottleneck commonly observed 1. Data Flow Pipeline - Upgrade and Invest in better techniques and technologies 2. Data Compution Throttle - Upgrade your computation limit or develop more optimal codes to execute code faster 3. Domain Knowledge of the Developer - Invest time in teaching developers your vision, it will add to their growth and they will perform better 4. Ineffecient Coding Practices - Use ChatGPT to optimize your code, use shared space as your home garden and not public park 5. Listen to experienced resources and take their advice they might have implemented more projects than you

If a data science project is falling behind schedule: 1. Assess: Identify bottlenecks and reasons for delays. 2. Reallocate Resources: Adjust team workload or add resources if possible. 3. Prioritize Tasks: Focus on critical components to catch up. 4. Communicate: Update stakeholders on revised timeline and reasons for delay. 5. Streamline: Simplify processes and automate where feasible. 6. Collaborate: Seek team input for solutions and distribute workload effectively. 7. Stay Agile: Adapt project plan as needed to meet revised deadlines. By swiftly addressing these steps, I ensure the project gets back on track and meets its objectives effectively.

-->Here are the steps to follow if your data science project is falling behind schedule: -->Assess the situation: Identify the specific areas or tasks causing delays and evaluate their impact on the overall timeline. -->Prioritize tasks: Reassess task priorities based on criticality and dependencies to focus on essential components that can bring the project back on track. -->Allocate resources: Consider reallocating resources, such as team members or computing resources, to expedite critical tasks or resolve bottlenecks. -->Communicate with stakeholders: Keep stakeholders informed about the challenges.

Always apprising the status honestly will help in long term stakeholder satisfaction. Be open to ask for help if needed from your stakeholders and don’t shy away. Do not point fingers on people instead do take up individual performance issue privately. Always take accountability for the entire team and accordingly manage your communication style.

Getting everyone on the same page is key. Objectively present the current progress and offer alternatives eg the MAPE target is not reached (target 10%) , we need 2 more days to experiment with FB Prophet.

Communicate openly with stakeholders about the project status, reasons for delay, and actions being taken to resolve issues. Seek feedback to align with stakeholder expectations, fostering trust and collaboration. Effective communication is key to managing expectations and avoiding misunderstandings.

Look for opportunities to simplify your approach. If your predictive model or any model that you are using is overly complex then consider taking a step back and constructing a simpler model..sometimes less is more. Elegance in data science often lies in its simplicity not complexity. Also reconsider the scale of your current project scope. Is it necessary to deliver everything at once? Could a phased approach work better? Simplifying doesn't always mean sacrificing quality. Often breaking projects down into manageable chunks creates quick wins for the business, keeps the team motivated and generates momentum and lays groundwork for the next step in the process.

Simplify your data science approach by focusing on essential steps and techniques to deliver maximum value. Consider simpler models, fewer features, or streamlined data preprocessing. Utilize existing tools and frameworks to accelerate workflow and minimize errors, reducing project delays.

Delegate or outsource tasks that fall outside your core competency or consume excessive time. Assign data collection, cleaning, or visualization to team members with expertise or availability. Alternatively, consider outsourcing tasks to external experts or services for quality results within a shorter timeframe.

If a lack of resources is impeding the project's progress, think about assigning team members jobs or contracting out specific project components to outside specialists or contractors. Determine which areas require more assistance or knowledge, then assign tasks appropriately to meet deadlines and advance work faster.

I have found agile methods of scrums, sprints, sprint reviews and retrospectives simple but effective ways to manage work and keep transparency. Additionally, retrospectives are important ways to reflect on work. That being said, my experience has been in data science projects that when falling behind, it tends to be problems with 1) data, staging data takes longer than anticipated, 2) infrastructure availability/stability and 3) technical debt. From an implementation standpoint, I see integration as the other long pole. Legacy system integration can be challenging. Often moving from modeling data sets to live data can be challenging. Building unit tests, regression tests and tight collaboration with technology is critical.

Make the most of the setback as a chance to grow and learn. Examine the underlying reasons for the delays and determine any lessons that can be used for upcoming projects. To avoid similar problems in future projects, think about putting process improvements, risk mitigation measures, or better resource planning procedures into practice.

Over-communicate to your stakeholders and manager about the situation as soon as you start falling behind on milestones. A lot of problems that occur from falling behind are solved when you give them a heads up. This may mean they adjust other projects to give you more resources, or it's helping cut unnecessary side tasks.

Apart from the previously mentioned measures, take into account elements like reassessing project schedules, modifying the distribution of resources, pursuing mentorship or advice from seasoned experts, and utilizing automation or technological solutions to optimize processes and enhance productivity. Continue to be proactive and flexible while handling obstacles to guarantee the data science project is completed successfully.