Space Data & AI for Earth Business Canvas

Why to Use?

Use the Space Data & AI for Earth Business Canvas to map how data sources, data systems, and analytics/AI solutions empower your organization’s broader strategy. The rocket metaphor provides a fun, visually engaging way for teams to see how long-term visions and immediate objectives align, especially in data-driven or AI-powered initiatives. This approach is well-suited to space-themed projects or any multi-stage endeavor requiring clear data strategy planning.

When to Use?

Planning a Data & AI Strategy Workshop

Introduce a simple, compelling format that highlights how data and analytics flow upwards to support business goals.

Aligning Stakeholders

Give teams a “big picture” roadmap before diving deeper into specifics like technical architecture or roles.

Explaining Complex Projects Simply

Whether in aerospace or another long-duration domain, the rocket metaphor illustrates a phased approach where each stage supports the next.

Who to Use?

Form an interdisciplinary team of EO data experts, AI engineers, and business managers and invite an internal or external workshop facilitators for moderation and mediation.

How to Use?

① Destination: North Star (Vision)

What It Is

This field captures your organization’s Vision, the aspirational “north star” that guides your entire strategy. It describes the desired future state of your business and the larger impact you aim to achieve.

Why It Matters

The Vision is the ultimate purpose. It should inspire and unify the team while remaining clear and actionable. A strong vision motivates decision-makers and stakeholders by framing what success looks like in the long term, especially in fields like aerospace where projects often span decades. Everything else on the canvas is derived from this guiding star.

Key Consideration

Ask:

1. What long-term value or change are we trying to create?

2. How will our strategy impact the business, customers, and broader ecosystem?

② Payload: Impact (Objectives & Key Results)

What It Is

The Payload section translates the vision into Objectives and associated Key Results (or KPIs). These are measurable goals that break down the vision into achievable, time-bound targets. Objectives should bridge the high-level vision with specific impacts.

Why It Matters

Objectives make the vision tangible. They provide clarity on what needs to happen for the vision to be realized. The key results attached to each objective ensures teams stay focused on measurable outcomes, aligning resources and actions accordingly.

Key Consideration

Ask:

1. What specific goals must we achieve to move toward the vision?

2. How will success be measured in terms of KPIs or outcomes?

③ Cabin Crew: Processes (Decisions & Actions)

What It Is

This stage focuses on Decisions that must be made and the Actions required to meet the objectives. These are the operational and strategic moves your team will take. It also includes the processes that support these decisions and actions.

Why It Matters

Decisions and actions translate information into impact. They ensure that data insights and analytics outputs lead to tangible steps, such as optimizing processes or allocating resources. Without this stage, even the most advanced analytics pipeline remains theoretical.

Key Consideration

Ask:

1. What decisions must we make to achieve our objectives?

2. What actions must follow to turn decisions into results?

3. What processes will ensure consistency and accountability?

④ Stage #3: Analytics & AI (Information Pipeline)

What It Is

This section defines the analytics and AI tools that transform raw data into actionable information. It maps the Analytics Pipeline stages (Descriptive to Autonomous Analytics) and ensures the right tools are in place for insights at each level.

Why It Matters

Insights inform decisions. The analytics and AI pipeline ensures you extract the maximum value from data, providing everything from basic reporting to advanced predictive and prescriptive insights. This stage clarifies how information is generated and used to inform decisions.

Key Consideration

Ask:

1. What analytics capabilities (descriptive, predictive, prescriptive, etc.) are needed to inform decisions?

2. Do we need advanced AI applications, or are simpler solutions sufficient?

⑤ Stage #2: Data Systems (Data Management)

What It Is

Data Systems are the technologies and organizational structures that handle data ingestion, processing, and integration. Examples include data lakes, warehouses, ETL pipelines, and governance frameworks.

Why It Matters

Robust systems ensure that data is clean, organized, and accessible for analytics. They serve as the backbone of any data strategy. Without reliable systems, analytics pipelines are prone to errors and inefficiencies.

Key Consideration

Ask:

1. What systems do we need to collect, store, and process data effectively?

2. Are current systems scalable for our objectives?

⑥ Stage #1: Data Sources (Earth & Space Data)

What It Is

This stage identifies all relevant data sources feeding into your systems. Sources can include internal systems (e.g., CRM, IoT sensors) and external feeds (e.g., satellite imagery, market data).

Why It Matters

Data sources are the lifeblood of analytics. This stage ensures that all necessary data is available and highlights gaps that may need to be addressed.

Key Consideration

Ask:

1. What data sources do we already have, and what’s missing?

2. Are there quality or accessibility issues with current sources?

⑦ Launch Pad: Data Fuels (Earth Observation Satellites)

What It Is

This section highlights the major data categories or domains driving your strategy, such as "Earth Data" and "Space Data." These categories act as the fuel for the rocket, powering analytics and decision-making.

Why It Matters

Understanding the broader categories of data emphasizes dependencies and highlights opportunities to leverage new types of data (e.g., satellite imagery, IoT sensors). It helps prioritize investments in foundational data streams.

Key Consideration

Ask:

1. What broad data categories drive our strategy?

2. Are there untapped opportunities in these data domains?

Workflow: From Vision to Launch Pad

1. Start at the top with your Vision (North Star). Define the ultimate goal.

2. Move downward to Objectives, identifying specific measurable impacts that bring the vision to life.

3. Break objectives into actionable steps in the Decisions & Actions stage, ensuring clarity on what needs to be done.

4. Identify the Analytics & AI capabilities needed to generate the information for these decisions.

5. Verify that your Data Systems can support analytics and address any system gaps.

6. Map your Data Sources and flag missing or inadequate ones.

7. Conclude with the Launch Pad, highlighting the foundational data domains that fuel everything above.

This top-down approach ensures a clear connection between high-level strategic goals and the practical systems and data that support them, making it ideal for aligning complex data strategies with actionable business insights.