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.
The Data Landscape Canvas complements the Data Strategy Canvas and delves into the question of data supply by classifying data sources according to their origin:
Furthermore the Data Landscape Canvas differentiates between different kinds of data:
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.
A data landscape gives you an overview of the available, accessible, and required data sources of your company.
Use the data landscape to:
For more information, see Data Strategy Design.
You can use the template in two ways:
You then cycle through the four quadrants – owned ~, earned ~, paid ~ and public data – in clockwise direction and consider which data sources of the respective type are available for the specific application, necessary or at least helpful.
In addition to the four quadrants, the data landscape canvas defines three areas delimited by dashed lines, which describe the granularity and type of data (from outside to inside):
Place your data sources in one of the three areas accordingly. If a data source contains data of different granularity or type, place the appropriate card on the boundary of either area, or create two or more cards and place them in their respective areas.
Use the following colors for the cards (data sources):
Step 1 of 4
Your most valuable data assets are typically owned data (also called “first-party data”). This is data that your company has created or collected itself and for which you have full and exclusive rights of use.
Step 2 of 4
Earned data is usually limited in terms of utilization and you cannot be sure that other companies, especially your competitors, will not have the same data. Earned data comes from your customers and partners (e.g. suppliers, service providers, etc.) and is collected within the context of the existing customer or supplier relationship.
If, on the other hand, the customers or partners sell the data as a standalone service or offer it explicitly in exchange for other services, this is paid data (see next section).
Step 2 of 4
One way to get additional customers or user data is a so-called data trap: you offer your customers or partners a free service or an app. Through this app, you then collect additional data.
Data network effects increase the willingness to provide data on users’ side: imagine a (digital) product that receives data from users and provides them with added value. The more data is available, the higher the added value and the more users use the product and in turn generate more data, the more value is added to the product.
Step 3 of 4
Paid data is data from other companies that you have purchased or exchanged for your own data or your own services (as part of a data exchange). If the other company has created or captured this data, it is called “second-party data”. Data brokers who sell data of other companies offer “third party data”. Another source of paid data is a data marketplace. The data providers usually do not sell the data exclusively to you and usually only for limited purposes.
If an existing customer or partner sells additional data to your business in addition to its existing business, it is paid data. Potentially, the customer or partner is both the source of earned data and a supplier of paid data.
Step 4 of 4
Public data is generally accessible data, for example from public internet sites, social media networks, or statistical offices. The data, at least in its raw form, is accessible to all market participants and accordingly offers little differentiation potential. However, if the data is refined, for example, it can create a unique data source. One example is Google’s PageRank algorithm which uses public data (websites) to create a prioritized search index. The search index is then owned data.
With public data, often the question of licensing is unclear: what can I do with the data if there is no explicit license agreement? To address this issue, there are Open Data: public data that is under an open-source license that governs the use, modification, and disclosure of the data. An example is Wikipedia as well as the canvas templates of Datentreiber which are under a Creative Commons license.
Step 4 of 4
Complete the work on the data landscape by following these steps:
The presentation referred to beside introduces you to the Data Strategy Design method and Analytics Maturity Canvas by means of an example project.
Here you can find further documentation:
Explore your Data Landscape (Blog)
With Design Thinking towards a Data-Driven Marketing (SlideShare)
Data Thinker Group (LinkedIn)
Get to know our Data Strategy Design Method in our practical seminars:
Here you can find further canvas and information concerning Data Strategy Design:
You are free to:
Share — copy and redistribute the canvas in any medium or format
Adapt — remix, transform, and build upon the canvas
for any purpose, even commercially.
Under the following terms:
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Wie das mit der von uns entwickelten Methode des Datenstrategie-Designs funktioniert, verrät Ihnen Martin Szugat im Fachartikel im iX-Magazin.
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