How to Save a Failing Project: Top Data Management Strategies

Data management strategies are crucial for life science commercial operations teams, who are regularly pressured to develop new, actionable insights while streamlining data activities to reduce expenses. However, well-intentioned, new data management projects initiated to pursue these goals sometimes veer off course, floundering for traction and momentum.

Wasted time and misspent money are two apparent consequences, but frustrated stakeholders’ delays in reaching or implementing critical decisions are equally costly. Often, these projects are too important to sideline, forcing leaders to act quickly to regain traction and confidence.

This blog will discuss the actions needed to regain momentum for a data management project that lurched forward or, perhaps, completely stalled. We concentrate on six general steps, which can serve as a valuable framework for assessing the project’s delay.

1. Identify the Root Causes

The first step in rescuing a failing project is understanding why it is failing. Implementing effective data management strategies requires a thorough analysis of the project’s current state and the factors contributing to its stagnation.

Is it due to inadequate resources, unclear objectives, lack of skilled personnel, or poor project management? In our experience with delayed projects, it is not uncommon for each issue to play a part in the interruption.

When new technologies are involved, such as advanced analytics, the resources and skills required may exceed those currently deployed on present analytics efforts, compromising further progress while reducing resources for current demands. This misalignment often stems from a lack of proper data lifecycle management planning, which is crucial for successful project execution.

Teams can often discover blurred or misaligned objectives in this step. For example, a simple objective is to streamline the number of dashboards needed to adequately examine the underlying actions required at the data, calculation, and visualization layers. However, this goal may not align with the broader data management best practices or the organization’s long-term data strategy.

A thorough review, however painful, conducted with key stakeholders and project team members is essential. 

This review should encompass all aspects of the project, including:

1. Resource allocation and utilization
2. Skill gaps within the team
3. Project management methodologies
4. Alignment with overall data management strategies
5. Adherence to data lifecycle management principles
6. Implementation of data management best practices

Sometimes, an external audit can provide a fresh perspective on the underlying issues. External experts can offer unbiased insights into your data management strategies and identify areas where industry best practices are not being followed.

It’s important to note that root causes may be interconnected. For instance, a lack of skilled personnel might lead to poor project management, which in turn results in unclear objectives. By thoroughly examining each potential factor and its relationships to others, you can develop a comprehensive understanding of the project’s challenges.

Once the root causes are identified, you can prioritize them based on their impact on the project and the effort required to address them. This prioritization will guide your next steps in developing an action plan to rescue your floundering data management project and align it with your organization’s overall data management strategies.

2. Realign Goals and Expectations

Let’s break the effort into smaller, linked segments. For example, a project that involves the preparation of an existing data environment to support a new product launch can be divided into phases such as data cleaning, integration, and validation.

Similarly, revisiting the mastering process as more data sources are onboarded ensures accuracy in downstream reporting and analytics. This phased approach makes the project more manageable and allows for continuous assessment and adjustment.

Business, data, and governance strategies should be developed jointly by business and IT units. This collaborative approach ensures that all stakeholders are aligned and working towards common objectives.

Cascade these strategies through well-organized planning sessions, adopting well-developed goals articulated using business frameworks such as SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound). This method ensures that goals are clear, attainable, and aligned with the overall business strategy.

During these planning sessions, it’s important to emphasize why lifecycle management is important. Proper data lifecycle management ensures that data is handled efficiently from creation to disposal, reducing risks and improving data quality. This is particularly crucial in data warehousing projects, where large volumes of data need to be managed effectively to support business intelligence and analytics.

Leaving ample time for planning, executing, and clarifications is vital. Rushed projects often lead to overlooked details and misaligned objectives. By taking the time to thoroughly plan and communicate, teams can ensure that everyone is on the same page and that the project is set up for success.

3. Reboot with Strengthened Project Governance

Effective project management and governance are crucial in revitalizing a struggling project. Begin by evaluating the current project management’s experience, level, and authority to determine if any enhancements or adjustments are necessary. It’s vital to identify key stakeholders from various areas, such as business, IT, and contracted vendors, to ensure comprehensive oversight and collaboration.

Aligning the project’s objectives, agreeing on the minimal viable product (MVP) or critical paths, and assessing risks and dependencies are essential steps before initiating the execution phase. These steps ensure that everyone involved understands the project’s goals and the necessary steps to achieve them. It’s important to avoid the complications arising from each workstream or vendor using disparate tools like Microsoft Project, Smartsheet, and Excel without a cohesive strategy.

Instead, focus on appointing a dedicated project manager whose primary responsibility is to gather information from all workstreams and vendors. This individual should objectively present weekly project status updates and lead monthly executive steering committee meetings. The weekly meetings aim for tactical alignment across workstreams, highlighting potential delays and shifting timelines. This approach ensures that all parties are informed and can adjust their efforts accordingly.

Meanwhile, the executive steering committee prioritizes tasks amid changing timelines, ensures financial oversight, deprioritizes when necessary, and facilitates the achievement of the minimal viable product. This committee plays a critical role in maintaining the project’s momentum and ensuring that resources are allocated efficiently.

Incorporating robust data management strategies into your governance framework is essential.

These strategies should include:

1. Data Asset Management: Ensuring that all data assets are properly managed, secured, and utilized effectively. This involves cataloging data assets, monitoring their usage, and implementing policies for data governance.
2. Life Science Solutions: Leveraging specialized life science solutions to address industry-specific challenges. This could include tools and platforms designed to handle large datasets, ensure compliance with regulations, and support advanced analytics.

4. Be Tough on Communication and Collaboration

Poor communication often contributes to project failures. Establishing regular updates, transparent communication channels, and collaborative platforms can significantly improve project progress and outcomes.

However, a strong emphasis on defined agendas, notes capture, and distributed action items is as significant as frequency. This structured approach ensures that all team members are on the same page and that there is a clear record of decisions and responsibilities.

For effective data management strategies, it is crucial to ensure that remote employees, contractors, and vendors are aligned while respecting competing time zones to maintain motivation and engagement. This alignment can be achieved through the use of collaborative tools such as Slack, Microsoft Teams, or Asana, which facilitate real-time communication and task management.

Leaders must demand steady participation from a core team to ensure that the information developed and passed is understood and that the team can effortlessly bring in new members. High churn among your rebooted team risks stalling the project again. Therefore, maintaining a stable team is essential for continuity and progress.

To enhance communication and collaboration, consider the following best practices:

1. Regular Updates: Schedule regular check-ins and updates to keep everyone informed about the project’s status and any changes. This can be done through daily stand-ups, weekly progress meetings, or bi-weekly sprint reviews.
2. Transparent Communication Channels: Utilize platforms that allow for open and transparent communication. This includes email, instant messaging apps, and project management tools where all team members can access relevant information.
3. Collaborative Platforms: Implement collaborative platforms that enable team members to work together seamlessly. Tools like Google Workspace, SharePoint, and Confluence can help in sharing documents, tracking changes, and collaborating on tasks.
4. Defined Agendas and Action Items: Ensure that every meeting has a clear agenda and that action items are documented and assigned to specific individuals. This helps in maintaining focus and accountability.
5. Respecting Time Zones: When working with remote teams, be mindful of different time zones. Schedule meetings at times that are convenient for all participants and use asynchronous communication methods when necessary.
6. Core Team Participation: Encourage consistent participation from a core team to maintain continuity. This core team should be responsible for onboarding new members and ensuring that they are up to speed with the project’s progress.
7. Sales and Marketing Operations Alignment: Ensure that the sales and marketing operations teams are aligned with the project’s goals. This alignment is crucial for the successful implementation of data management strategies, as it ensures that the data collected and analyzed is relevant and actionable.
8. Outsourced Managed Services: Consider leveraging outsourced managed services to handle specific aspects of the project. This can help in managing workload, accessing specialized skills, and maintaining focus on core activities.

5. Assess Honestly Investment in Required Skills Development​

New data management outcomes sought by commercial operations teams often rely on mastering new technologies, data methods, and new production and distribution requirements demands. Even experienced data experts may need to pay more attention to the scope of learning and practice required for these new technologies.

Implementing effective data management strategies often necessitates a significant investment in skills development to ensure that the team is equipped to handle the complexities of modern data environments.

In a project reboot, this is an excellent time to reassess the real needs and outline the necessary skills and related training needed to repoint your project and ensure it will transition into operations smoothly. This evaluation should be thorough and honest, identifying gaps in the current team’s capabilities and the specific skills required to achieve the project’s objectives.

Consider the following steps to assess and invest in required skills development:

1. Conduct a Skills Gap Analysis: Evaluate the current skill levels of your team members and identify areas where additional training or expertise is needed. This analysis should be aligned with the project’s goals and the technologies being implemented.
2. Identify Training Needs: Based on the skills gap analysis, outline the specific training programs or certifications that team members need to undertake. This could include training on new data management strategies, advanced analytics, or specific tools and platforms.
3. Leverage Cloud-Based Learning Systems: Utilize cloud-based learning systems to provide flexible and accessible training options for your team. These platforms offer a wide range of courses and resources that can be accessed anytime, anywhere, making it easier for team members to upskill without disrupting their work schedules.
4. Engage External Experts: Consider bringing in consultants or external experts with the necessary expertise to provide targeted training and support. These professionals can offer valuable insights and hands-on guidance to help your team master new technologies and methodologies.
5. Implement Continuous Learning Programs: Establish a culture of continuous learning within your organization. Encourage team members to stay updated with the latest trends and advancements in data management strategies by participating in ongoing training programs, attending industry conferences, and engaging in professional development activities.
6. Monitor and Evaluate Progress: Regularly assess the effectiveness of the training programs and the progress of your team members. Use performance metrics and feedback to identify areas for improvement and adjust the training plans as needed.

Investing in skills development is crucial for the success of your data management project. By ensuring that your team has the necessary knowledge and expertise, you can enhance their ability to implement effective data management strategies and achieve the desired outcomes.

This investment will not only help in the successful completion of the current project but also prepare your team for future challenges and opportunities in the ever-evolving data landscape.

6. Consider a Highly Empowered Champion to Support Change

Efforts required to produce new changes in a commercial operation may also require sunsetting or eliminating current practices, SOPs, and work instructions. These disruptions are a form of loss, and the effect of that loss should be rectified among existing employees, contractors, and vendors (yes, even vendors). These changes may benefit from empowered senior-level leaders regularly discussing the project’s goals and progress.

Appointing a highly empowered champion—a senior leader with the authority and influence to drive change—can be pivotal in navigating these transitions. This champion should be someone who understands the intricacies of data management strategies and can advocate for the necessary changes across all levels of the organization.

Their role involves supporting the project team and communicating the vision and benefits of the changes to all stakeholders, thereby mitigating resistance and fostering buy-in.

Here are some key responsibilities and actions for the empowered champion:

1. Communicate the Vision: Clearly articulate the goals and benefits of the data management project to all stakeholders. This includes explaining how the changes will improve processes, enhance data quality, and support overall business objectives.
2. Engage Stakeholders: Regularly engage with stakeholders, including employees, contractors, and vendors, to address concerns and gather feedback. This engagement helps in understanding the impact of changes and in making necessary adjustments to the project plan.
3. Support Change Management: Lead change management efforts by developing and implementing strategies to manage the transition. This includes training programs, updated SOPs, and new work instructions that align with the project’s goals.
4. Monitor Progress: Keep a close eye on the project’s progress and ensure that it stays on track. Regularly review milestones, identify potential roadblocks, and work with the project team to address any issues promptly.
5. Promote Customer Master Data Management Best Practices: Emphasize the importance of customer master data management best practices. This involves ensuring that data is accurate, consistent, and up-to-date, which is crucial for effective decision-making and operational efficiency.
6. Foster a Culture of Flexibility and Adaptability: Encourage a culture where team members are open to change and willing to adapt to new processes and technologies. This flexibility is essential for overcoming challenges and achieving project success.

Rescuing a floundering data management project is challenging, but achievable with a strategic approach. By identifying the root causes, realigning goals, strengthening project management, enhancing skills and tools, and appointing a highly empowered champion, you can steer your project back on track.

Remember, the key is to remain flexible, adapt to changes, and keep your team motivated and focused on the end goal. With these steps, you can turn a struggling project into a success story.

Conclusion

Rescuing a floundering data management project requires a thoughtful and strategic approach. By identifying the root causes of the issues, realigning goals and expectations, strengthening project governance, enhancing communication and collaboration, investing in necessary skills development, and appointing a highly empowered champion, you can turn your project around.

Effective data management strategies are at the heart of successful data projects. These strategies ensure that your team is aligned, equipped, and focused on delivering valuable outcomes. Remember, flexibility, adaptability, and continuous engagement are key to overcoming challenges and steering your project to success.

Data management projects can indeed be complex and fraught with unexpected hurdles. However, with the right strategies and a committed team, even the most troubled projects can be revitalized and set on a path to success. Keep your focus on the end goal, embrace best practices, and leverage the power of strong leadership and collaboration to transform your data management initiatives.

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