From Data Gaps to Data Assets
Data Landscape Canvas
By using the Data Landscape Canvas you explore new data sources and discover new data suppliers. It helps you to assess your company data and to identify the proper data sources for your utilization scenarios.
What is the Data Landscape Canvas?
The Data Landscape Canvas helps you understand and manage the wide range of data sources that could power your data & AI products. By mapping out where data originates—be it owned internally, earned from customers and partners, purchased from external providers, or publicly available—you gain a clear perspective on what’s accessible, what’s missing, and what may require additional effort to obtain or improve.
This structured view ensures that you’ve considered the necessary data inputs and any associated quality, legal, or privacy factors before investing in analytics or AI solutions. Whether your focus is a specific data/AI product or the organization’s broader data ecosystem, the Data Landscape Canvas guides you in identifying critical data sets, highlighting gaps, and strategizing how to fill them. It encourages a forward-looking approach to data management, helping you discover new sources, refine data quality, and design processes that continuously feed into value-generating analytics and AI applications.
The Data Landscape Canvas is available for free under a Creative Commons license: You may use and modify the canvas as long as you cite Datentreiber in particular as the source.
When to Use the Data Landscape Canvas?
The Data Landscape Canvas is especially useful in scenarios such as:
- Preparing for Analytics or AI Projects:
Before developing a data-driven product or applying advanced analytics, use the canvas to pinpoint which data sets are required, verify their availability and quality, and ensure you can integrate them seamlessly. - Assessing Data Readiness for Use Cases:
If you’ve identified promising analytics or AI use cases, the canvas helps confirm whether the necessary data is accessible and appropriate. It reveals data gaps or issues, guiding you to adjust goals or find new data sources.. - Strategic Data Sourcing and Improvement:
For organizations looking to broaden their data horizon beyond internal systems, the canvas surfaces opportunities to acquire or exchange external data, capture more owned data, or refine current data sets. This ensures you have the right inputs to support long-term data and AI strategies.
By applying the Data Landscape Canvas, you build a strong foundation for effective data utilization and continuous growth in analytics and AI maturity, ensuring that information flows naturally into your products, services, and decision-making frameworks.
How Do I Use the Data Landscape Canvas?
Description is coming soon…
①a Header
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② Data Products
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③ Owned Data
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④ Earned Data
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⑤ Paid Data
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⑥ Public Data
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a Raw Data
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b Derived Data
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c Linked Data
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Where Can I Find More Information?
- Find detailed tutorials and examples on how to use the canvas in the context of designing a fully integrated data & AI strategy in our Data & AI Design Bench.
- Explore your Data Landscape (Blog)
- Data Thinker Group (LinkedIn)
