Dear hub members,

I currently write my master thesis on “Success factors of data-centric business models” at University of Regensburg (Germany) and in cooperation with Detecon Consulting.

Data-centric business models try to describe emerging business models that are mainly based on leverage data and information exchange with users, communities and partners. As we have observed, companies that adopt a data-centric business model such as Google or Amazon, generate most of their revenues from handling and processing of data or information.

A in depth overview over the methodology and also some examples for data-centric business models such as nike+, Goldcorp, social media aggregation and restaurant reviews can be found in the presentation here.

My current research approach is to develop several case studies based on expert interviews, that are furthermore using the “business model framework/template” (@all thanks for the great and comprehensive work on that) for analyze the overall business model.

Since the focus of my thesis is mainly on information and data-products we have created a framework called “Information Value Chain”(IVC) that is especially targeted at data and information processes of a business model.

I see the main advance of the IVC over the business template in the discussion of the steps of the Information Lifecycle and therefore the representation of the specific characteristics of information.
If you have other comments or think that informational business models can be better described better by the business model template – I look forward to your comments.

In evaluating the IVC as framework analyze data-centric business, we address further questions:
- What are the key success factors of data-centric business models?
- How does the information value chain (IVC) help to derive strategic options related to data-centric business models?-
- What data is marketable and how does it need to be processed and presented?

For a quantitative evaluation of the IVC I’m conducting a online survey. Survey Link
The survey is confidential and will take approximately 5-10 minutes.

It would great if you are able to participate or can give me some feedback, random thoughts and comments on the information value chain approach. Also forwarding to some experts, colleagues etc would be great.

Your benefit to participate is that each participant gets a copy of the aggregated results and final findings of my thesis.

Furthermore, if your company is marketing any informational or data-products I would be thrilled to ask you some questions in a short 15-20 minute telephone interview. Please leave your contact details in the survey or send it to me via email: Lukas.feuerstein@gmail.com.

Thank you al lot.
Kind regards and looking forward to the “business model generation” book
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Here some the short characteristics of the several steps of the Information Value Chain (IVC)

1. Data generation/acquisition describes the generation/source of data.
- Is data generated externally through users?
- Is data acquired from partners or users?
- Is data generated internally?
- Structured vs. unstructured data?

2. Storage and representation
- Is the data stored persistently and in a computer oriented way?
- Does the company that stores the data originally own the data (in contrary to pure integration services like Salesforce)?

3. Aggregation and processing
This information value chain steps tries to aggregate and consolidate theData. Furthermore it focuses on the creation of semantic links between data (categories, hierarchies etc.). Also, data is transformed and restructured.

4. Information generation and integration (Mashup)
- Data from different types and sources (internal or externally, structured and unstructured) are mapped, combined and mashed up.
- Integration of value offer of 3rd parties in order to enrich the own data and expand the customer value.
GoogleMaps for examples integrate address data from the GoogleSearch Index with map data and therefore improve the user interface and add value.

5. Analytics and application.
- Evaluation and analysis of the information (e.g Search)
- Adaption into concrete situations (Recommendations, context sensitive ads).
- Personalzation (users adaptive evaluation of data)

6. Presentation and provisioning
- Presentation of the information to the customer and provisioning of the created data and products
- device adapted display of content

Thanks for your comments and also the great work all of you have done for the "Business Model Generation" Book.
Survey Link