Project Management for Data Visualization, Analytics & AI | Ensuring Success
Managing data visualization, analytics, and AI projects requires a unique approach. Success hinges on clearly defining project goals, ensuring data quality, and adopting an iterative, agile process. A customized strategy is essential for everything from data collection to security, governance, and visualization.
Key factors include stakeholder alignment, performance measurement, efficient resource allocation, and transparent communication. Security must be front and center to safeguard sensitive information and maintain compliance.
Drive impactful insights with expert data project management.
Project management for Data Visualization and Analytical projects, including AI, needs to be different. Defining project goals and requirements is crucial for any successful data analysis project. However, ensuring data quality and relevance and iterative, agile, step-by-step approaches are essential elements that significantly achieve project success.
A customized approach is essential for successfully managing data projects that include AI components.
Today, your data project 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 with knowledge critical to your success. These projects add complexity by integrating data, gathering subject matter experts, systems exports, data models, complex data calculations, and other requirements across the organization.
Determining the goals and requirements while maintaining flexibility is critical for a data project for several reasons. 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. They also help the project team focus on what matters most, preventing them from getting lost in irrelevant details or pursuing divergent objectives.
Setting clear goals and requirements guides data analysis, resource allocation, and decision-making. By staying focused on the end goal, the team can prioritize tasks and avoid distractions, improving 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. 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. When all parties involved share the same goals, decisions can be made quickly and confidently. 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 and continuous improvement and demonstrates the project’s impact on stakeholders.
Making mid-course corrections in a project requires knowing when you are off the rails. Focused update meetings with key stakeholders, including those doing data preparation and analysis, can uncover miscommunications and result in impactful changes.
With Power BI, especially data projects, 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 in areas 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 ensure that these people are 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 and progress and build stakeholder trust and credibility.
Defining goals and requirements is crucial for ensuring that your data project is focused, aligned, and efficient. This will ultimately result in the successful delivery and showing of 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 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 keeps a check on scope creep. There is an excellent article by Devan Ciccarelli that 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 ensure 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’ essential roles in a 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 |
The various stakeholders that are integral to a data project
Stakeholder Engagement
In addition to identifying the key stakeholders (e.g., data analysts, project managers, and subject matter experts) needed throughout the project lifecycle, it is just as important to ensure their perspectives are considered when defining goals and requirements.
They must be identified as active participants, but they also 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 help with stakeholder engagement. This can be essential during meetings for several reasons, as they contribute to more engaging, effective, and productive sessions. First, enhanced engagement helps capture participants’ attention, encourages active involvement, and fosters collaboration and idea generation.
Having stakeholder engagement helps in good measurement definitions.
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 ensures 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. This will foster 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 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. 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 critical 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 gather for 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 essential to have a larger kickoff-style workshop so everyone has an overview of all these requirements. This also helps to introduce various team members and stakeholders to each other from groups that do not normally work together.
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 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 | Establish a realistic project schedule for timely completion, including milestones and deadlines. |
| 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. |
Full-day workshop agenda.
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 important to maintain a balance in meeting the goals by allocating resources and time accordingly.
One of the most important aspects of managing a data project is recognizing that multiple reporting goals may also be competing for resources and attention. Having data ready for analysis may be the one priority that causes 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 effectively and that time is used efficiently.
These projects need to be agile, as the timing of data and resources always tends to be the most volatile. 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
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 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 must adjust to reality. There must be a balance.
Regular meetings and updates are essential. Consistent communication keeps stakeholders engaged and helps maintain project momentum. However, do not delay sharing critical issues or information that could significantly impact the project until the next meeting, which might be a week or more later.
Don’t drown your stakeholders in paperwork. Long daily email updates will cause them to glaze over.
I have been on many projects where the most important goal seemed to be adhering to the plan. I have always believed that you learn during project delivery and need to adjust the plan to maintain a successful outcome.
The following are four reasons why agile delivery can make adjusting priorities in data project development more 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: Agile delivery helps identify and address potential risks early in the project lifecycle, minimizing their impact on project outcomes, by regularly reevaluating and adjusting priorities.
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
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.
Clear objectives guide the data analysis process by identifying KPIs and appropriate analytical techniques. This leads to efficient resource allocation and contributes 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.
Your objective needs to be clear.
Image by Felix Wolf from Pixabay
Data Quality and Relevance
As a consultant in data analytics for many years, I consider this the most significant point. To avoid misleading or incorrect insights, it is important to ensure that the data used in the analysis is accurate, complete, and relevant to the project’s goals. This must be one of the most consequential tasks in defining the project management process for data 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 data quality sign-off process. This includes key stakeholders reviewing and approving the data, balances, and results before they are used in the analysis.
Maintaining thorough documentation of all data quality efforts is essential, including 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.
Implementing 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 provides 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 accurately reflect the desired outcomes and deliverables. This alignment consistently focuses on achieving the project’s objectives throughout its lifecycle.
It seems like this was drilled into me every year, but these criteria must 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.
Smart Goals are specific, measurable, achievable, relevant, and timed.
Personal goal setting and strategy system.
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 used a bit of an agility mantra 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 data projects that I have used in project management. It evolved over about 20 years, as I always seemed to see the same things. As the diagram below shows, I have always found that an iterative approach works best for data projects, which have become data visualization and Power BI projects.
7 Stage Data Visualization Workflow
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.
Using Your Client’s Terminology
This is a quick example of what you can see in these projects, as each client tends to have different terminology that they like to use. Be open to learning something new.
I had this one dilemma, for example. Is it Productionalize, Productionize, or Productionise? While writing, I often come across words that don’t seem right. The linked article addresses one of my annoyances: “Productionalization.” As I researched, I realized I needed to be open to different variations of English. Could different grammar rules from various countries influence it? For example, Color or Color?
Conclusion
A customized approach is essential for successful management of data projects that include AI components. 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 data projects, resulting in impactful and valuable outcomes.
