Winning Project Management For Power BI Projects

Project management for Power BI Projects needs to be different. Defining project goals and requirements is crucial for any successful project on data analysis. However, ensuring data quality, relevance, and iterative, agile, step-by-step approaches are essential elements that play a significant role in achieving Power BI project success.

Your data project today has a wide range of deliverables. It is a data-gathering, processing, governance, security, and visualization project all rolled into one.

Why is Defining the Goals and Requirements important?

They say you cannot get to your destination without a MAP. Your goals, requirements, and ability to enforce, adapt and follow them are the keys. With data projects, you usually have far more stakeholders who have knowledge critical to your success. These projects add complexity through the need to Integrate data, gather subject matter experts, systems exports, data models, complex data calculations, and other requirements strewn across the organization. 

For several reasons, defining the goals and requirements while maintaining flexibility is critical for a data project. The following will explain.

Direction and Focus of Everyone’s Efforts

Clear goals and requirements provide a roadmap for the project, guiding the data analysis process and ensuring that resources are utilized effectively. In addition, they help the project team stay focused on what matters most, preventing them from getting lost in irrelevant details or pursuing divergent objectives.

Setting clear goals and requirements provides guidance for data analysis, resource allocation, and decision-making. The team can prioritize tasks and avoid distractions by staying focused on the end goal, resulting in improved workflow and productivity. In addition, this approach ensures timely and cost-effective achievement of the project’s objectives.

Stakeholder Alignment

Establishing well-defined goals and requirements fosters a shared understanding among stakeholders, ensuring everyone is on the same page regarding the project’s purpose, scope, and desired outcomes. This alignment is crucial for efficient collaboration and effective decision-making.

Stakeholder alignment is vital to create a unified vision and drive collaboration among all parties involved. By establishing well-defined goals and requirements, stakeholders comprehensively understand the project’s purpose, scope, and desired outcomes, facilitating efficient cooperation and promoting effective decision-making.

When stakeholders have aligned expectations, they tend to be more dedicated to achieving project success. Two-way team communication develops trust and reduces the likelihood of conflicts or misinterpretations arising from divergent perspectives. By working together, stakeholders can efficiently merge their resources, abilities, and knowledge to navigate challenges and capitalize on opportunities.

Stakeholder alignment can lead to more efficient decision-making. Decisions can be made quickly and confidently when all parties involved share the same goals. This helps speed up the project timeline and ensures that decisions are well-informed and aligned with the project’s objectives.

Ability to Measure Performance

Defining goals and requirements allows for developing specific, measurable success criteria, which helps all parties agree on the project’s performance. This facilitates data-driven decision-making, continuous improvement, and demonstrating the project’s impact to stakeholders.

Being able to make mid-course corrections in a project requires knowing that you are off the rails. Having focused update meetings with key stakeholders, including those doing data preparation and analysis, will result in the discovery of miscommunications and can result in impactful changes.

With Power BI, especially data projects for that matter, testing and proving the results’ validity is paramount. Therefore, successful project management will require detailed testing and balancing routines to prove all is well.

Resource Allocation

Clearly defined goals and requirements help prioritize tasks, allocate resources (time, budget, personnel), and determine the appropriate data analysis techniques. This ensures that resources are invested where they will significantly impact achieving the desired outcomes.

Your team has different skills and strengths. A good plan aligned to the requirements allows you to leverage their strengths into a more productive work effort.

With the various groups involved, such as subject matter experts, not all resources will be required full-time. Most stakeholders will be swamped with their other duties. A good resource plan will be necessary to make sure that these people can plan to be available when the project needs them.

Communication and Transparency

Well-defined goals and requirements are a foundation for transparent and effective communication with stakeholders throughout the project. They help convey the project’s purpose, progress and results in building stakeholder trust and credibility.

Defining goals and requirements is crucial for ensuring that your data project can be shown to be focused, aligned, and efficient. This will ultimately be successful in delivering and showing valuable insights, which will help the project achieve its intended outcomes. 

Security is Job 1 and Needs to be Front and Center

Incorporating security and data security discussions into the project management plan is crucial due to the rising frequency of data breaches and cyber-attacks. Organizations must prioritize security to protect sensitive information and comply with data privacy regulations.

By addressing security concerns proactively, organizations can mitigate risks, safeguard their reputation, and maintain trust with clients, partners, and stakeholders. Including security measures is essential to safeguarding assets, ensuring regulatory compliance, and upholding an organization’s reputation in today’s data-driven world.

Some specific security elements that may be more unique to a data project involve;

• Define user permissions and authentication protocols to restrict unauthorized access to sensitive data and visualization tools.
• Implement data encryption techniques to protect sensitive data during storage and transmission, minimizing the risk of breaches.
• Establish audit trails to track user activities and interactions with data and visualizations, enabling the detection of suspicious behavior.
• Utilize data masking techniques to anonymize sensitive information in visualizations and engineering processes, preserving privacy without compromising analysis.

Integrating security discussions into the project management plan helps organizations identify potential vulnerabilities. This process involves defining data access controls and encryption methods to protect sensitive information. It also encourages collaboration between IT, legal, and operations teams, fostering a security-conscious culture.

… more and more companies are asking How secure is my data in the cloud? and What end-to-end protection is available to prevent my sensitive data from leaking?

A must have resource is the Power BI security white paper available on the Microsoft Power BI whitepaper site.

Defining the Power BI Data Project Plan

Clearly Define Project Goals.

Start by establishing the specific objectives for the data analysis project. Ensure the goals are well-defined, measurable, and aligned with the business or research objectives. This clarity will help guide the project from start to finish and provide a benchmark for evaluating success.

Having a well-defined set of goals also keep a check on scope creep. There is an excellent article by Devan Ciccarelli o which has a great definition;

“scope creep is the uncontrolled expansion to project scope without adjustments to time, cost, and resources.” 

Source: The Definitive Guide to Scope Creep in Project Management (goskills.com)

Identify Key Stakeholders

Determine the individuals or groups with a vested interest in the project’s outcomes, such as decision-makers, data providers, operations teams, and end-users. Engaging these stakeholders early on ensures their input, which can improve project buy-in and the quality of the final deliverables.

Some people may sit back and be nervous about speaking up. It is up to you to make sure voices are heard, as they will speak at the end when it may be too late. Separate meetings may be more beneficial than large groups.

The following table shows the various stakeholders and their essential roles in a Power BI data project.

Stakeholder	Role	Contribution to the Project
Project Manager	Overseeing the project	Planning, resource allocation, timeline management, risk mitigation
Data Analyst	Analyzing and interpreting data	Data manipulation, statistical analysis, insights generation
Data Engineer	Managing data infrastructure	Data collection, storage, integration, and preprocessing
Data Scientist	Developing advanced analytics models	Machine learning, AI, predictive analytics
Data Steward	Responsible for maintaining data quality, governance, and consistency	Owner of a portion of the data. Creating standard definitions and ensuring trust in data resources.
Domain Expert	Providing industry-specific knowledge	Business context, domain-specific insights, best practices
IT Specialist	Ensuring technical support	System maintenance, security, network administration
Software Developer	Building tools and applications	Programming, system integration, application development
UX/UI Designer	Designing user interfaces	User experience, interface design, usability testing
Quality Assurance	Ensuring product quality	Testing, validation, defect identification, and resolution
Senior Management	Setting strategic direction	Decision-making, resource allocation, project prioritization
Database Analyst	Optimizing database performance	Database design, query optimization, performance analysis
Data Modeler	Creating data models	Logical and physical data modeling, schema design, data normalization
Data Visualization Designer	Crafting visual representations	Data visualization design, storytelling, interactive visuals

Stakeholder Engagement

In addition to Identifying the key stakeholders (e.g., data analysts, project managers, subject matter experts) needed throughout the project lifecycle, ensuring their perspectives are considered in defining goals and requirements is just as important.

They must be identified and are active participants, but also they provide sign-off on decisions made during the project.

Discussions of formulas and calculations are a great example of the need for engagement. Different departments, such as HR, Sales, and Executive teams, may have different definitions of revenue. Which one is current and which one you will use needs to be determined. Also, don’t be surprised if all three definitions will be reported on.

Some interactive tools can be used to help with stakeholder engagement. During meetings, this can be essential for several reasons, as they contribute to more engaging, effective, and productive sessions. First, an enhanced engagement helps capture participants’ attention, encourages active involvement, and fosters collaboration and idea generation.

Here are a few interactive tools that can be used during the meeting to help bring out participation.

• Engaging participants in a data project meeting is crucial to ensure productive discussions and effective collaboration.
• Start by setting a clear agenda, outlining the meeting’s objectives and topics to be covered. This helps attendees understand the purpose and come prepared with relevant information.
• Personalize the invitation by highlighting each stakeholder’s importance to the project, fostering a sense of ownership and accountability.
• During the meeting, encourage open dialogue by asking open-ended questions and actively seeking input from those who may be quieter or less assertive.
• Use interactive tools like live polls or collaborative digital whiteboards to foster engagement and stimulate creative thinking.
• Break down complex data into easily digestible visualizations, making it accessible for everyone to grasp critical insights.
• Finally, acknowledge and validate contributions, fostering a positive and inclusive atmosphere where everyone feels valued and motivated to participate.

Resources: 47 useful online tools for workshops and meetings! | SessionLab, for those online meetings, these look interesting, 8 Best Online Meeting Engagement tools [2022] | Facilitator School

Establish Requirements

Define the necessary data, resources, and tools required to achieve the project’s goals. This includes outlining the data sources, data processing, analysis techniques, and any software, hardware, or personnel needed. Clearly listing these requirements helps to set realistic expectations and avoid potential roadblocks during the project.

At this stage, you also get into the timing and availability of those required data, resources, and tools. Some topics that are missed include;

• Choose appropriate analytical methods and techniques (e.g., descriptive, predictive, or prescriptive analysis) that align with the project’s objectives and available data.
• Some items around data security and compliance include data handling and storage protocols to maintain data privacy, security, and compliance with relevant laws and regulations (e.g., GDPR, HIPAA).
• An especially important aspect of development data is that it could be widely shared within internal and external development teams, testers, and quality assurance (QA) teams.
• Any important project timeline and milestones. Consider also including intermediate milestones to confirm timely completion expectations and track progress throughout the project.

The following table is one I like to use as an agenda topic list. During a full-day workshop, various stakeholders would be gathered to take a first cut. After the first set of meetings, breakout sessions happen over time with individual groups for a deeper dive-style discussion. I found it important to have a larger kickoff-style workshop so everyone has an overview of all these requirements.

I always find it amazing what assumptions each department may have about each other. You can get a great deal of history on data and why specific decisions that were may impact you today. But be careful that the meeting does not devolve into a raising of grievance session and political landmines.

Requirement	Explanation / Task
Business Objectives	Define the specific goals the project aims to achieve, aligned with the organization’s strategic vision.
Data Sources & Quality	Identify relevant data sources, assess data quality, and address potential data-related challenges.
Technical Infrastructure	Determine the necessary hardware, software, and tools needed for efficient data processing and analysis.
Analytics & Reporting	Specify the required analytics techniques, KPIs, and reporting formats to derive actionable insights.
Security & Compliance	Establish data security, privacy, and regulatory compliance requirements to protect sensitive information.
Resource Allocation	To ensure smooth adoption, prepare for organizational changes, including training and support.
Stakeholder Communication	Develop a communication plan to keep stakeholders informed and engaged throughout the project.
Project Timeline	Establish a realistic project schedule, including milestones and deadlines, for timely completion.
Risk Management	Identify potential risks and develop mitigation strategies to minimize their impact on the project.
Change Management	Establish a realistic project schedule for timely completion, including milestones and deadlines.
Data Governance	To ensure smooth adoption, prepare for organizational changes, including training and support.
Scalability & Flexibility	Plan for future growth and adaptability, considering potential changes in data volume and business needs.
Integration & Interoperability	Ensure seamless integration with existing systems and compatibility with potential future technologies.
User Experience	Design user-friendly tools and interfaces to enhance usability and adoption across the organization.
Project Evaluation & Review	Define success criteria and establish a review process to measure project performance and outcomes.

Prioritize and Balance Goals

Recognize that multiple goals may be competing for resources and attention. Prioritize these objectives based on their importance and alignment with the overall project or business strategy. It is always a balance in order to meet the goals by allocating resources and time accordingly.

When it comes to managing a Power BI project, one of the most important aspects is recognizing that multiple goals for reporting may also be competing for resources and attention. Having data ready for analysis may be the one priority that may cause the most issues to your timeline. Once you start looking, you may find that the quality and availability may cause objectives to change.

As a project manager, it is important to prioritize objectives based on their importance and alignment with the overall project or business strategy. This will help ensure that scarce resources are allocated in an effective manner and that time is used efficiently.

These projects need to be agile as the timing of data and resources always tends to have the most volatility. You need to make interim deliverables which may cause some goals to be moved to a later phase of development.

“Agility is the ability to adapt and respond to change… agile organizations view change as an opportunity, not a threat.”— Jim Highsmith

Source – 139 Project management quotes (plaky.com)

Regularly Review and Adjust

We must continuously assess the project’s progress against these defined goals and requirements throughout its lifecycle. Be prepared to make and justify adjustments as needed to stay on track and ensure the project remains relevant and valuable to the stakeholders. Agile delivery can sometimes get all over the map; however, you need to adjust to reality. There must be a balance.

It is important to have regular meetings and updates. Regularly communicating these updates helps maintain stakeholder engagement and project momentum. However, don’t let important issues or information that may have a big impact wait around a week or so for the next meetings.  

Don’t drown your stakeholders in paperwork. Long daily email updates will cause them to glaze over.

I have been on many projects where it seemed to be the most important goal was to keep to the points in the plan. I have always been a believer that you learn during project delivery. 

The following 4 reasons why agile delivery may make adjusting priorities in data project development beneficial.

• Adaptability to changing requirements: Agile delivery allows for quick adjustments to evolving business needs, ensuring the project remains relevant and aligned with the organization’s strategic objectives.
• Risk mitigation: By regularly reevaluating and adjusting priorities, agile delivery helps identify and address potential risks early in the project lifecycle, minimizing their impact on project outcomes.
• Optimization of resources: Agile delivery enables efficient resource allocation by prioritizing high-impact tasks and deprioritizing low-value activities, maximizing the return on investment.
• Enhanced stakeholder satisfaction: The ability to adjust priorities in agile delivery allows for continuous improvement and timely incorporation of stakeholder feedback, leading to higher overall satisfaction with the project’s results.

“Setting a goal is not the main thing. It is deciding how you will go about achieving it and staying with that plan.” – Tom Landry 

Source – Setting a goal is not the main thing. It is….

Clear Objectives

Establish well-defined goals and requirements for the project to ensure proper alignment with stakeholders’ expectations and to provide clear direction for the analysis process. Clear objectives are crucial to the project, providing a foundation for all activities and decisions.

Confirming clear project objectives promotes a shared understanding among stakeholders regarding the project’s goals, scope, and intended results. This promotes effective collaboration and reduces the likelihood of conflicts arising from differing perspectives.

Having clear objectives guides the data analysis process by identifying KPIs and appropriate analytical techniques, leading to efficient resource allocation and contributing directly to the project’s success.

Well-defined objectives provide a framework for making informed decisions throughout the project lifecycle. By regularly assessing progress against the project’s goals, the team can identify areas that require adjustments or improvements and make necessary course corrections in a timely manner.

Establishing clear objectives can provide a standard by which to evaluate the project’s progress. Defining specific, quantifiable, and time-bound goals allows the team to assess the project’s achievements and pinpoint opportunities for future enhancements.

Data Quality and Relevance

As a consultant in data analytics for many years, I consider this the most significant point. It is important to ensure that the data used in the analysis is accurate, complete, and relevant to the project’s goals to avoid misleading or incorrect insights. This must be one of the most consequential tasks in defining the project management process for Power BI projects.

Ensure the data used in the analysis is accurate, complete, and relevant to the project’s goals, as poor-quality data can lead to misleading or incorrect insights. Who will determine which data is correct? Who will sign off on whether the data, balances, and results are correct? How do you prove it?

Data quality and relevance are critical factors in the success of any data project. These steps are must-haves to be assigned by project management.

• Determine how to measure data quality. What are the Criteria? Who determines accuracy, completeness, consistency, timeliness, and relevance?
• Identifying the individuals or teams responsible for ensuring data quality, including data validation, cleaning, and enrichment, is important. The Stakeholder table above can serve as a guide.
• Define the data validation process for validating data, such as cross-referencing with other data sources, utilizing data profiling tools, or conducting manual spot-checks. These can be carried over to production as Regular data validation helps identify and address data quality issues before they impact the project’s outcomes.
• The responsibility for items in the plan should be determined through a formal process for data quality sign-off. This includes key stakeholders reviewing and approving the data, balances, and results before they are utilized in the analysis.
• It is important to keep detailed documentation of all data quality efforts, including the validation processes, sign-off approvals, and any identified and resolved data quality issues.
• Consider implementing automation to streamline the operationalization process. Utilize data quality tools and software to automate data validation, cleaning, and monitoring tasks. This will make it easier to get into production.

The implementation of these steps can help guarantee the use of high-quality and relevant data in the analysis, which minimizes the risk of inaccurate insights and enhances the project’s success.

Resources: Data quality – Wikipedia and (26) What I learned from ‘Executing Data Quality Projects’ | LinkedIn

Establish Success Criteria

Establishing measurable success criteria is one of the most important deliverables of any data project. This serves as a clear framework and benchmark for evaluating the project’s effectiveness. As with all goals, they need to be clear, agreed to, and measurable.

Success criteria should align with the project’s goals and requirements to reflect the desired outcomes and deliverables accurately. This alignment provides a consistent focus on achieving the project’s objectives throughout its lifecycle.

It seems like this was drilled into me every year, but these criteria need to be SMART (specific, measurable, achievable, relevant, and time-bound). You need a way to measure where you are in relation to the criteria. This way, you can make alterations or mid-course corrections.

Management likes to measure. Measurable success criteria enable the project team to demonstrate the project’s value and impact.   This becomes measurable evidence of success which can help secure continued support and resources for the project and future initiatives.

I have had a bit of a mantra of Agility in this article. Having success criteria creates a feedback loop for continuous improvement. It gives you a way to compare actual performance against the success criteria. It allows the team to examine the status and refine processes and strategies. This can be fed into the project management process as a lesson learned.

Keep the Iterative Approach

I developed a 7-Stage Data Visualization Framework for Power BI that I have used in project management. It developed over about 20 years, as I was always seeming to see the same things. As the diagram below shows, I always found an iterative approach that worked best for data projects, which became data visualization and Power BI projects.

It is important to continuously improve and adapt project goals and requirements based on new information and feedback from stakeholders, as well as changing circumstances, to maintain the relevance and value of the data project.

Conclusion

A customized approach is essential for successful project management in Power BI projects. Ensuring data quality and relevance and utilizing an iterative and agile methodology are important considerations alongside defining project goals and requirements. Incorporating these essential elements into the project management process can lead to more efficient and successful Power BI projects, resulting in impactful and valuable outcomes.