How do you manage and prioritize multiple analytics projects and requests?

Clearly define the scope of each project or request, including the goals, objectives, and deliverables. This will help understand the level of effort required and the resources needed to complete each project. Determine the value of each project or request to the organization, including the potential benefits and impact on business outcomes. This will help prioritize projects based on their strategic importance and potential return on investment. Once have assessed the scope and value of each project and evaluated resource availability, prioritize projects based on their strategic importance and potential impact on business outcomes. This will help you allocate resources effectively and ensure that the most important are completed first.

Another aspect beyond scope and value to consider is the dependency of future initatives on the request. This may not be immediately apparent and sometimes what looks like a low impact project today may become a huge dependency for larger initatives.

Following the principles of agile delivery have helped me as a developer of analytics projects and eventually in managing them. By aligning with stakeholders on priorities, we then split the project into phases (sprints) with a regular cadence to showcase the output. During the showcase, we get feedback and align on next steps so the development process will be streamlined through recurring checkpoints. Furthermore, understanding the requirements to be able to deliver a Minimum Viable Product (MVP) is another best practice when managing multiple projects. By focusing on delivering the MVP first, you can already showcase an output that is useful to the stakeholder and continue building on top of it in next iterations.

Managing a flood of data reporting requests is common in data analysis. To prioritize effectively, I use a strategy similar to the 80/20 rule. I assess which projects offer the greatest value to the business while demanding reasonable time and effort. By focusing on these, I ensure our time and resources are used where they have the most positive impact.

As I like to work on an agile way, a well curated backlog is essential in delivering value to the organization: 1) The impact of each request should be clearly assessed and quantified by the relevant stakeholders. 2) The technical dependencies should also be noted, as they could guide 'must-have' priorities. 3) Implementation effort should be estimated by the entire team using e.g. planning poker. I've discovered that even when an SME estimates effort, the joint exercise of discussing and estimating by the team proves to be more fruitful and accurate. Frequent discussions should take place between the analytics team and the business stakeholders, to ensure the team is constantly focused to the priorities that drive value.

Continuously monitor project progress and adjust priorities as needed based on changing business needs and resource availability. This will help ensure that projects remain aligned with organizational goals and objectives. Once have assessed the scope and value of each project and evaluated resource availability, prioritize projects based on their strategic importance and potential impact on business outcomes. By assessing the scope and value of each project or request, identifying project value, evaluating resource availability, prioritizing projects, and monitoring and adjusting priorities, organizations can effectively manage and prioritize multiple analytics projects to drive business success.

Once we decide to go ahead with a project based on project prioritization, we create a project plan which includes the key milestones. We include the dependencies in the project plan. We then review the project plan with our stakeholders to ensure they are aligned with the project plan including the timelines. We also decide meeting cadence with the stakeholders and within the analytics team. Analytics project is a joint effort between stakeholders and the analytics team. Through regular meetings between stakeholders and analytics team, the solution being built gets validated, the business insights of stakeholders gets incorporated in the solution, data validation happens from business standpoint etc.

Defining timelines and milestones is crucial, but it's equally important to consider the flexibility needed. While structure is essential, allowing room for adaptation acknowledges the dynamic nature of analytics work and fosters agility in responding to unforeseen challenges or new insights along the way.

It’s critical to layer in dependencies into your project timeline. Understanding tasks and milestones at hand is important, but knowing what you cannot accomplish without a preexisting dependency being completed first helps to prioritize and time box appropriately

Set expectations with your technical lead, the client and the project manager on when a certain task will be delivered. This will give visibility to your peers about what are you working on.

I believe it depends heavily on the analytical maturity of the company/department. If the maturity level is low, in my past experience, a good strategy was delivering self service platforms, with which 90% of the users' need are met (e.g. a business intelligence solution). For the remaining 10%, setting a monthly committee gathering all stakeholders and analyzing the company/department key objectives tend to bring more transparency and prioritization when allocating the available resources.

We focus on onboarding resources to the project with the right skill mix. Also leaders and professionals from stakeholder organisation is part of the project team. We have subject matter experts from stakeholder team who provide business knowledge. Change manager is from stakeholder organization who is responsible for driving adoption. Finally we have the sponsor to the project, who is the senior leader from stakeholder organization who owns the adoption of the project.

Allocating resources involves not just assigning names to tasks but understanding the strengths and passions of team members. Leveraging their skills and interests can often result in surprising innovations and efficiency improvements.

- Assess Project Scope and Urgency: Evaluate each project's requirements, deadlines, and business impact. - Rank by Business Value: Prioritize projects that align with strategic goals and deliver high ROI. - Resource Allocation: Assign skilled team members based on expertise and availability. - Tool Utilization: Match projects with appropriate analytical tools and technologies for efficiency. - Timeline Management: Create detailed timelines, ensuring overlapping tasks are synchronized to avoid bottlenecks. - Regular Check-ins: Conduct frequent progress reviews and adjust resource allocation as needed. - Stakeholder Communication: Maintain transparent communication with stakeholders on priorities and status updates.

We also need to check future efficiency and efforts needed to make project valuable in future. As data changes everyday ,what are the minimum requirements we need to sustain the project

Be sure to draw a distinction between monitoring items that fall into the category of "data observability" and the actual performance of core KPIs and metrics. Your data observability metrics pertain to things such as pipeline runs, refresh intervals, and the quality and completeness of underlying source data. Actual performance of core KPIs, metrics and outcomes are best served to be gauged against snapshots in time to view positive or negative trends over time.

Working on multiple analytics projects, often at the same time calls for clear and concise task management. For this I recommend using the P.A.C.E model for documentation and tracking since it takes into consideration both execution and monitoring aspects of analytics projects. - Plan: Define objectives to ensure alignment with business goals - Analyze: Apply analytics processes on data to discover trends and derive actionable insights - Construct: Build ML models or Visualization Dashboards to clearly communicate findings - Execute: Implement insights to influence business strategies, ensuring real-world impact. This phase is pivotal as it translates data-driven insights into real-world impact, elevating organizational performance!

Execution and monitoring, for me, also involve fostering a culture of continuous learning. Encouraging team members to share their learnings and insights creates a collaborative environment where everyone benefits from each other's experiences.

Keeping track of multiple ongoing projects can lead to delays and missed deadlines if not monitored effectively. Let us take Slack for example - Slack had issues with executing and monitoring multiple analytics projects simultaneously. They adopted agile project management techniques and implemented tools like JIRA for better tracking and collaboration. By using these tools and methodologies, Slack improved visibility into project statuses, enabling timely interventions and ensuring projects stayed on track and were completed efficiently.

Analytics projects execution and monitoring requires data assessment and processing at all levels, i.e. data collection and storing, data cleaning and transforming, data analysis and visualization, data reporting and dashboarding, deep dives and supporting information collection, insight creation and impact assessment, and finally presentation of findings and implementation of actionable pointers. The impact of implementation through pre-post KPI report is also part of analytics project impact monitoring.

As a BI Analyst, gathering report requirements, modeling data and designing the report is crucial, but the real impact comes from proper presentation of the insights. Here are some things to consider based on your audience: - For Executives: Focus on clear, high-level insights and visuals. - For Middle Management: Provide a balanced view, offering detailed insights for effective team management while aligning with broader business goals - For Operational Team: Ensure relevancy and focus on how insights will help with day-to-day activities. Understanding your audience ensures your hard work pays off, making the data insightful and actionable.

By following the below elements and performance criteria,effectively communicate and deliver each project or request to manage and prioritize multiple analytics projects and requests. It is important to establish clear communication channels, develop a comprehensive delivery plan, and proactively manage program risks to ensure successful project outcomes. Performance criteria:Develops and manages the communication plan across program components for both internal and external stakeholders Expectation: A well-formatted and documented communication plan with current and accurate updates, stakeholders are informed of program status and changes in a timely manner Examples:Regular program status meetings, stakeholder surveys, program newsletters

You should also consider data governance around the project. Data can become obsolete and unusable fairly quickly without refactoring for downstream changes.

Communication and delivery are where the human touch truly shines. Crafting a narrative around the insights, connecting them to the stakeholders' objectives, and presenting data in a compelling, relatable manner can be as crucial as the analytics itself. It's about not just delivering numbers but conveying a story that prompts action and fosters understanding.

In situations where you're operating on a sprint based project plan, consider facilitating an end-of-sprint recap to thoughtfully review progress made towards milestones and deliverables. Having a thorough readout of what has been accomplished, and whats left to tackle, helps to align expectations on overall project delivery and pace.

As an analytics practitioner for two decades, I have experienced that the most critical component in analytics project prioritization is aligning analytics resources with the organization's overarching strategy. Some of the most skilled analytics talent spends their time doing work that is necessary but not necessarily the work that will drive competitive differentiation and breakthrough insights. A study published by Harvard Business Review shows that 80% of analysts’ time is spent simply discovering and preparing data. I believe that the ideal percentage of time that analysts should spend on analysis should be closer to 50%. Thoughts?

Maximize automation wherever possible. Whether it involves data requests or requirements, provide users with an uncomplicated method to request analytics and visibility to monitor progress. Streamlining these processes not only enhances efficiency but also empowers users with a straightforward and accessible approach to obtaining the information they need.

While working on tasks requiring more bandwidth than is available and no direct fix, one can explore the following options - 1. Releasing tasks in a phased manner - for tasks where not all deliverables are required immediately, one can prioritize certain parts first and then work on other ones later so as to increase the number of tasks completed at a time 2. Pulling resources from adjacent teams 3. Reprioritizing non-essential deliverables in favor of mission-critical ones 4. Automating repetitive tasks to reduce workload overhead

Producing agile Analytics Proof of Concepts (A-POCs) that demystify project risks is crucial. These A-POCs offer rapid insights, allowing quick adaptation and informed decision-making. They showcase the project's value, securing support, and provide a clear roadmap for subsequent phases, ensuring alignment with evolving organizational needs. In today's data-driven world, A-POCs serve as essential catalysts for successful analytics initiatives, helping organizations mitigate risks, harness value, and stay competitive.