Data Landscape Canvas

How Do I Use the Data Landscape Canvas?

A data landscape gives you an overview of the available, accessible, and required data sources of your company.

Use the data landscape to:

1. Identify gaps in your data landscape and new relevant data sources.
2. Conceive utilization possibilities.
3. Analyze missing links between data sources or legal restrictions of data sets.

For more information, see Data Strategy Design.

Start

You can use the template in two ways:

1. To explore the required and available data sources for a specific use case. Start by naming the use case and placing the appropriate card in the box utilization in the middle of the template. This can be for example a card from the box utilization of the Data Strategy template or from the template Analytics Maturity.
2. To explore the data sources available to your business in general. If you want to narrow the scope, name the area of application and place a respective card in the utilization box. Otherwise, leave the box in the middle empty.

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.

Areas

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):

1. Raw data is unprocessed and unfiltered data such as log files, measured values, (anonymized) customer surveys or transaction data.
2. Derived data has already been refined, for example, by having been cleaned, normalized or aggregated. Examples are website statistics, sales figures or KPI tables.
3. Link data is data used to link data from different sources to each other, for example, by connecting transaction data from an ERP system with customer data from a CRM system via a unique customer identifier.

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.

Colors

Use the following colors for the cards (data sources):

• Green: existing data sources to which you also have access.
• Yellow: data sources that are available, but to which you have no access or for example whose data quality is questionable.
• Red: data sources that are mandatory for a use case, but do not yet exist, are unknown, or where access is denied.

Step 1 of 4

Owned Data

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.

Questions:

• Which data is created by our employees (in the context of key activities)?
• Which data is collected by our technical systems (see key resources)?
• Which data do we receive through our marketing, sales, distribution and service channels (see key channels)?
• What data is collected by our (key) partners on our behalf (whereby the data collection activity is the subject of the contract rather than the data itself)?
• Which data can we capture in addition?

Examples:

• Measurement data from own devices
• Log files of IT systems
• Manual data collection by employees
• Customer surveys by an outsourced service provider

Step 2 of 4

Earned Data

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).

Questions:

• Which data do we receive through our customers (in the context of customer relationships and through our key channels)?
• Which data do our (key) partners provide us with – implicitly or explicitly?
• Which data could we ask for additionally?

Step 2 of 4

Examples:

• Customer data from a CRM system
• User data from websites, mobile apps, social media profiles, etc.
• Data from logistics or purchases through our partners
• Data we receive directly from our partners

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

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.

Questions:

• With which companies have we agreed a mutual exchange of data or would it be worthwhile to conclude such a partnership?
• Which companies offer data which is helpful or necessary?
• Which relevant data do our customers, partners or competitors offer?
• Which marketplaces are available for data, that helps us?

Examples:

• Qualified addresses from data brokers
• Market research data and statistical surveys
• Anonymized user profiles from online advertisement

Step 4 of 4

Public Data

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

Questions:

• Which authorities, universities or associations have relevant data?
• Which open data providers (open data marketplaces or open data websites) are there?
• Which data can we extract from public websites?
• Which relevant data is published on social networks?
• Which companies offer their own open data portals?

Examples:

• German GovData
• Open Data Portal of the EU
• Twitter and Facebook
• Open Data Portal of the Deutsche Bahn

Completion

Complete the work on the data landscape by following these steps:

1. Check the data landscape for completeness with the following questions: “Do we have all the data available to realize the desired use case? Can we connect all data sources via suitable link data? And are there data sources that we do not yet use, but which could possibly be relevant?”
2. Focus your attention on the yellow and red cards and ask yourself: “What are the open questions and critical assumptions? Who do we need to talk to, to gain access to these data sources? How can we complement missing data, for example with data partnerships with other companies or with new or enhanced products for customers? From questions like these, you can directly derive tasks and the next steps. These actions you can, for example, note down on white cards which you position next to the relevant data sources.
3. Combine the data sources into databases and transfer the databases to the box utilization of the parent data strategy and/or the box key resources of a business model.

References:

• Data Strategy: the data landscape is a zoom into the box utilization of a data strategy.
• Business Model / Case: data sources are a key resource for data-driven business models. Key customers, key partners, key activities und key channels are possible data suppliers or sources of relevant data.