Damit ein Data-Projekt erfolgreich ist, muss es gut geplant sein und einen Nutzen erbringen. Ein Project Data Canvas hilft mir dabei, alle wichtigen Punkte eines Projekts klar und übersichtlich darzustellen.
Ein Project Data Canvas beantwortet die wichtigen Fragen: Warum machen wir das Projekt? Wer ist daran beteiligt? Welche Vorteile haben wir? Es hilft, klare Ziele zu setzen und festzulegen, wer was tut. Besonders wichtig ist es, dass alle im Team wissen, was zu tun ist.
Ich benutze das Tool vor allem zu Beginn eines Projekts, aber auch zur Nachverfolgung der Ergebnisse. Besonders inspiriert hat mich das Project Management Handbook von Antonio Nieto-Rodriguez und die 7 W-Fragen Methode aus dem Journalismus. Die Methode hilft Journalisten, alle wichtigen Informationen zu einer Geschichte zu bekommen. Mir hilft sie besser zu verstehen, warum ein Kunde ein Datenprojekt durchführen will und was er damit erreichen will.
Am liebsten erstelle ich den Canvas auf einem klassichen Whiteboard, habe aber auch eine Vorlage für OneNote und Archi. Hier ist der Auszug aus meiner OneNote Canvas Vorlage:
Purpose
- Why we are doing the project?
- What is the value of the project and how will it change the future – and for whom?
- How will improved data management enhance decision-making processes?
- Who will benefit most from better data governance and quality?
- What are the TOP 3 Things that will be better after this project?
- Examples: Improved data accessibility, better data quality, enhanced compliance with data privacy laws.
Benefits
- What benefit and impact will the project generate
- How will improved data governance reduce risks and improve operational efficiency?
- How will we know the project is successful? What are the Top 3 metrics?
- Examples: Reduction in data-related incidents, increased user satisfaction with data systems, compliance with data regulations.
- What really keeps the clients happy in terms of the outcome of the project?
- Reliable and timely access to accurate data.
Investment
- How much will the project cost?
- Estimate costs for software, hardware, consulting, and training.
- What is the budget and how flexible is the financial framework?
Sponsorship
- Who is accountable for the project?
- key sponsors and their roles in supporting the project.
Stakeholders
- Who will benefit from and be affected by the project?
- data users, data stewards, IT teams, and compliance officers.
- Who are the stakeholders, who are the people who start or finish the project, who receive the output? Are there foreseeable conflicts?
- Consider potential conflicts between departments over data ownership and usage.
Team & Resources
- Who will deliver the project and which skills are needed to deliver the project?
- Data architects, data engineers, data governance experts.
- But also – what is needed in terms of work tools, materials and spaces (on-site / virtual)?
- Tools & Licenses, Access to Source Systems, VPCs
Deliverables
- What will the project produce or deliver?
- Data governance framework, data lineage documentation, improved data integration processes, Data APIs
- What is exactly to be delivered to the customer? And what not?
- Clear documentation and training materials for data management practices.
- Implemented data pipelines for extracting, transforming, and loading data into data warehouses or data lakes.
- Provisioned cloud resources (e.g., AWS, Azure) for data storage, processing, and analysis.
Data Sources
- What are our Data Sources and how to get the Data?
Risks and Opportunities
- What uncertain events can hinder or boost the success of a project?
- How are we going to engage stakeholders and manage risks?
- Regular communication plans, stakeholder engagement sessions, risk management strategies.
Plan
- How and when will the work be carried out?
- Detailed project timeline with key milestones.
- How should we achieve the project goals, what is the way of cooperation?
- What milestones would be reasons to celebrate? Are there dates for partial and intermediate results? Dates for measurable successes or important decisions?
- Examples: Completion of data inventory, implementation of data governance policies, successful data quality assessments.
