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
By the way, you can order a print version (DIN A0) from Stattys.
You can use the template in two ways:
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 exploitation 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.
To generally explore the data sources available to your business. If you want to narrow the scope, name it and place a card in the exploitation box. Otherwise, leave the box in the middle empty.
You then go through the four quadrants - owned ~, earned ~, paid ~ and public data - in a clockwise direction and consider which data sources of the respective origin are available for the specific application or necessary or at least helpful.
Your most valuable data assets are typically owned data (also called "first party data"), which is data that your company has created or collected itself and in which you have full and exclusive rights of use.
Earned data are usually limited in terms of exploitation 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, they are paid data (see next section).
One way to get additional customer or user data, are so-called data traps: you offer your customers or partners a free service or an app and collect through this additional data.
Data network effects increase the willingness to release data on the user side: imagine a (digital) product that receives data from a user and provides him with added value. The more data available, the higher the added value - and the more users will use the product and generate more data, which in turn will add value to the product.
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 the data of other companies offer "third party data". Another source of paid data are data marketplaces. 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. Possibly, the customer or partner is both source of earned data and supplier of paid 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. An 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, the question of licensing is often 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 the Wikipedia - or the canvas templates of Datentreiber, which are under a Creative Commons license.
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 whose data quality for example is questionable.
Red: Data sources that are mandatory for a use case, but do not yet exist, are unknown, or where access is denied.
In addition to the four quadrants, the data landscape template 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.
Complete the work on the data landscape by following these steps:
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?"
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 data partnerships with other companies or with new or enhanced products for customers?" From this, you can directly derive tasks and the next steps and note, for example, on white cards which you position next to the relevant data sources.
Combine the data sources into databases and transfer the databases to the box exploitation the parent data strategy and/or the box key resources of a business model.
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