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About

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 new possibilities of exploitation and utilization.
  3. Analyze missing links between data sources or legal restrictions of data sets.

For more information, see Data Strategy Design

By the way, you can order a print version (DIN A0) from Stattys.


More information

Tutorial

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 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.

  2. 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.

OWNED DATA

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.

Questions:

  • What data are created by our employees (in the context of key activities)?
  • What data are collected by our technical systems (see key resources)?
  • What 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)?
  • What data can we capture in addition?

Examples:

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

EARNED DATA

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).

Questions:

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

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 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

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.

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

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. 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.

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 are published on social networks?
  • Which companies offer their own open data portals?

Examples:

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 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.

AREAS:

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):

  1. Raw data are unprocessed and unfiltered data such as log files, measured values, (anonymized) customer surveys or transaction data.
  2. Derived data have already been refined, for example, by being purged, normalized or aggregated. Examples are website statistics, sales figures or KPI tables.
  3. Link data are linking data from different sources to each other, for example, by connecting transaction data from the ERP system with the customer data from the CRM system via a unique user 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.

END

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 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.

  3. 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.

REFERENCES:

  • Data Strategy: The data landscape is a zoom into the box exploitation of a data strategy.
  • Business Model: 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.



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