How can you prioritize multiple deadlines in Data Analytics?

From my perspective, the categorization of projects in Data Analytics should look beyond a standard risk/urgency matrix. Think about several aspects that are relevant to your stakeholders, and run these by them - ideally, sign them off. What is the impact, regulatory importance, and qualitative benefits of delivering X before Y? If your team works agile, reiterate as priorities will shift. This signed-off approach will create a consensus on what the driving factors are behind priority.

I have found that the following indicators can help with prioritizing multiple deadlines. Determine the data analytics project that: 1) Reduces loss or risk to customer (internal or external) 2) Addresses regulatory or critical policy requirements/concerns 3) Improves performance of critical businesses activities 4) Aligns with strategic objectives

Aligning multiple deadlines in data analytics with the overall strategy of the organization is crucial. Here's how to integrate this aspect into the prioritization process: - *Understand Company Goals:* Grasp the overarching strategies and objectives of the organization. - *Connect Deadlines to Objectives:* Identify how each deadline contributes to these strategies. - *Prioritize High-Impact Tasks:* Give precedence to deadlines closely tied to core company strategies. - Communicate Alignment: Ensure your team comprehends how meeting these deadlines supports the company's mission. - Measure and Refine: Track the impact of meeting these deadlines on company goals, using this data to continually fine-tune approaches for future deadlines.

Don't wait for people to ask you what your plans are - anticipate the needs. PLANNING: 1. Do not leave space for (mis)interpretation. Create a document that is easily accessible to your stakeholder 2. Break down the projects into tasks 3. assign each one of them to a resource 4. provide a time estimate 5. the type of effort required (1 to 5) 6. the blockers (green/yellow/red) 7. the priority level (low/mid/high) 8. the status for the current week (picked/not picked) COMMUNICATION attack plan: 1. Set clear and realistic expectations 2. Make sure you wrote down strong hypotheses to support your timeline, goals, and milestones 3. Communicate the plan on time to your stakeholders. They will challenge it, so be prepared and be open minded.

I've found setting clear expectations through effective communication from the start to be invaluable in managing multiple deadlines. Breaking down projects into manageable steps with clear milestones is crucial, but equally important is maintaining an open dialogue with all stakeholders. Collaboration tools like Slack and Google Meet are helpful, yet regular, structured communication including progress updates and periodic reviews is key. This not only keeps clients informed but also allows adaptability to unforeseen events, ensuring priorities align with the evolving needs of the project.

As part of business intelligence team, the team setup and working environment is vital in determining the scope of each task given to us. When the flow of communication is clear, then only each task can be planned out and communication flow can be determine (vertical/horizontal, top/bottom) The expectations from the management is also vital in planning multiple tasks in one go, and in data analytics world, the agile method will speed up this process and each task can be split up to individual MVP and thus setting milestones that will eventually funnel into one single main outcome

Focus is the key. When under pressure, it's easy to waste time trying to multitask too many things at once. Focus on one thing at a time, work in 2-3-hour blocks, and don't let anyone or anything (yourself included) distract you. While it's difficult at first, after some practice in organisation skills and learning how to say 'no' it becomes a great way to get stuff done.

Maintaining focus and prioritizing tasks effectively is important, as is recognizing that your priorities may not always align with those of others, leading to potential conflicts when managing workload. Identifying and reconciling these conflicting priorities is a skill that can significantly impact success.

Avoid scope creep! Pick your top meaningful KPIs and stick with them. Get the overarching barometer readers going first, then dig into supporting details.

Every week priorities can change, and that's ok! Don't get frustrated and rather be prepared: setup a recurring checkpoint (e.g. weekly) to update the status of all your ongoing tasks and re-discuss their priorities. Bonus: it's important to make sure you have a document that is flexible TO YOU: if you prefer Google Sheets, or Excel, over Tableau, go for it. This process should be completely hassle-free and remove uncertainties. It should not create more problems, frustrations, or blockers.

Ideally, when starting out a project map out the different pathways it can take as best practice keep flexibility when gathering and transforming data so you can change course with relative ease later on. Also, trust your instincts and if you realize that tweaks need to be made do not overestimate your initial plan. Be open to suggestions and change as more information/demands come to the fore.

In managing multiple deadlines in Data Analytics, a key strategy is to regularly review and adjust priorities. This involves periodically assessing the progress of ongoing projects against their deadlines and the overall impact on business objectives. It’s important to remain adaptable, as priorities can shift due to changes in business needs, unexpected data findings, or resource availability. Regularly checking in with stakeholders and team members for updates and feedback helps in identifying any potential issues or changes in scope that may affect timelines.

What happened last week? Did I have any clash? What made my work smoother? Did I receive any shout-out? Any extra credit? Here is your attack plan: 1. Take a moment to reflect on your recent experience. 2. Write down your learnings. 3. Apply them to the next stream. 4. One week later, if they are confirmed, you will have one more data point confirming that your hypothesis was right. 5. Congrats, you improved!

The optimization and process improvement period after project completion is one of the most crucial factors of long-term success. I found it helpful to have quick retros to discuss -what went well -what went wrong -what could have been better Using the experience and knowledge gained from the current project as a foundation for the next project will help streamline and strengthen work processes. It will also make sure individual team members’ voices are heard.

Learning and improvement are essential aspects of managing multiple deadlines in data analytics. Reflect on completed projects to identify successful strategies and areas for refinement, gathering feedback to understand stakeholder satisfaction and areas of concern. Apply insights from this reflection to optimize workflows and decision-making processes for future projects. Continuous professional development through courses and certifications ensures your analytical skills remain sharp and your methodologies current.

Give kudos to your peers and your team, but don't forget to celebrate your own success, too! You don't need to give up on being modest; take credits for what you did and celebrate with something small, but meaningful to you. Do you like running? Go for it, maybe during a time when it is unusual for you. Do you like cooking? Save some time and setup a nice dinner, bake something special :)

Celebration and reward are vital for maintaining high team morale and motivation, particularly in the demanding field of data analytics. From my experience, small victories often lead to significant achievements. Utilizing platforms like Kudos and Bonusly for instant recognition encourages a culture of continuous appreciation beyond traditional rewards. This not only enhances job satisfaction but also spurs ongoing innovation and engagement. Additionally, facilitating downtime and relaxation with tools like Headspace or through subscriptions to services like Netflix or Spotify can help teams to disconnect and return more focused and creative, thereby boosting well-being and productivity.

In Reality, this is something that seems to be forgotten with most of the companies, they usually say thanks for the hard work and the achievement you got but more often than not that will be the end of it no compensation added. Then that is where employees think that they are being undervalued and underappreciated, since their hard work doesn't equate to the salary and compensation that they get. Companies need to stop prioritising giving more money to the new hires and start giving it to the high performing tenured employee to have them more motivated by validating their hard work with an actual compensation and not with just more work.

Keep a gantt chart ready 🚦 On gantt chart, keep 3 level of priorities. Low middle high. 🚨 Move every task forwards when the task might impact on the timing of other tasks 🧩

Break the tasks into a modular approach and proceed in steps. 1) Ideation: codify the SLA / Hypothesis / Problem statement. 2) Collection: based on step 1, plan your data sources, APIs, and storage. 3) Descriptive Statistics: perform EDA to understand the distribution of data. 4) Imputation: Based on step 2, you may look into data cleaning as its "garbage in and garbage out". 5) Transform: Scaling the data as deemed appropriate; address outliers; if required, write custom scalers. 6) Model: Select the model based on interpretability and flexibility trade-off; address the bias-variance. 7) Evaluate: Validate your model and evaluate using a quasi-real world scenario by backtesting; deploy to production on satisfactory results.

The importance of cognitive flexibility: the ability to adapt thinking strategies to new, unfamiliar situations. When juggling multiple deadlines, analysts must be ready to quickly switch focus between tasks, which can be a significant challenge. Mindfulness techniques and stress management training are valuable for improving this adaptability. Moreover, fostering a continuous learning environment, where mistakes are seen as growth opportunities rather than just failures, can help teams better navigate unpredictability and shifting priorities. Finally, supporting cross-functional collaboration facilitates a more holistic view of work, where insights from different areas can enrich decision-making and task prioritization.