how to build a successful business model with big data

WUR business innovation For building better business

How to build a successful business model with big data platforms?

A product lifecycle perspective

i-KNOW Graz Austria, October 11th 2017 Jos Verstegen and Bart Doorneweert


Big data platform initiatives are numerous... Opportunities in the market: once you have large data

sets you can make money with it (“data is the new oil”) Threats of getting disrupted: if other companies do a

better job in ‘binding’ users to their platform your company may become vulnerable, or even worse, irrelevant So ... let’s build our own platform and acquire data

(sources) But wait ... this is a time-consuming, costly, activity Help, we need a business model!

Why research on platform business models?


Please pick your business model...

3


Outline of paper (and presentation)

Describing the developmental stages of several current big data platforms,

● to see if we can find certain patterns in it,

● that can inspire others attempting to develop a platform

Discussing the implications of our findings for current big data platform initiatives

Elaborating on alternative roles and business models in big data


Academic literature Industry (grey) publications, business case descriptions Interviews held with founding team members of platform

businesses (Glooko, Fishingbooker) Our own experiences in action research projects on big

data platform development in Dutch agriculture

Data


DATA-FAIR: Open Software

Ecosystem Stakeholders

Platforms Apps + services

Knowledge models

Governance Business models

Data sharing

DATA-FAIR – value creation by data sharing in agri-food business

Farmer

Open Architecture & Infrastructure Event-driven, Configurable, Customizable

Standards & Open Datasets

Real-time data sharing

IoT layer


Vernon, Raymond (1966). “International Investment and International Trade in the Product Cycle,” Quarterly Journal of Economics, 80, 190-207

New product – the origin of big data platforms Maturing product – the growth and monetization of big

data platforms Standardised product – all set for further expansion by

fanning out over multiple industry verticals

Methodology


Emerging platforms (Glooko) ● Opportunities arise from the user base acquired in a

pipeline business model (value proposition evolves) ● Fall-back option

Intentional platforms (Fishingbooker) ● Difficult to get multiple value propositions right ● Many efforts needed to get two participant groups on

board: “chicken-or-egg problem” (Airbnb) ● Possible way out: example of the DataHub

cooperative in Dutch Agriculture

New product – the origin of big data platforms


Feed (95%)Zuivel / Vlees bedr.

Kringloopwijzer

ELNI, Belastingdienst, I&R, CBS, enz.

Banken

Accountants

E-CONNECT-Framework

Producenten / Ondernemers

Authorisatie

Machtigingen

Authenticatie

Veehouder

Mac

htig

ing

Grond+kuilmonsters

Agritrust

Source: VAA ICT Consultancy

Processors

Government

Farmers and growers

Accountants Analists

Suppliers

Banks


Challenge is to grow the user base and (herewith) ensure “stickiness” of users to your platform Innovation in your value proposition is needed (to get

revenues and to stay ahead of copycats) but this is also tricky careful experimentation needed

Maybe use multiple platforms if you want to retain young people “digital companies should delay profitability for as long

as they can” (Wessel et al. 2017)

Maturing product – the growth and monetization of big data platforms


Fanning out is also referred to as “platform envelopment” Examples are Windows Media Player (Real), Google

Shopping (eBay, Amazon), and Amazon Web Services Offering web services to affiliated companies and

developers becomes a more vital element of the big data platform business model than the revenues from actually selling own commodities Amazon is referred to as “the tax collector”

Standardised product – all set for further expansion by fanning out over multiple industry verticals


Learn from the development patterns of the pioneers Has the world market been divided among Amazon,

Google, Facebook and WeChat? Answer is “no”. ● There is always room for new entrants offering a

distinctive value proposition ● Bundling and unbundling of companies

(AoL/Google, Craigslist/Airbnb) is an ongoing process

● Transaction cost theory is still valid ● Competition authorities and social resistance limit

the growth of conglomerates

Implications for current big data platform initiatives


Size is no guarantee for survival (look at 7m30)

15

https://www.youtube.com/watch?v=7dZGhB7rv1g

https://www.youtube.com/watch?v=7dZGhB7rv1g


generate the resources to build your own platform? Answer: there are many other roles related to big data

platforms that can provide interesting business models If big data is the “new oil” then there is opportunities for

companies in e.g., capturing and refinery

What if you don’t want or cannot ...



Thank you! Any questions?

www.WURbiz.nl T (070) 33 58 201 / 330 @WUR_bia This paper presentation is part of the research projects: - Data Fair NL TKI-AF-16101 - Personalised Nutrition and

Health TKI-AF-15262

Dr. Jos A.A.M. Verstegen T +31 (0) 6 10 97 88 26 @josverstegen

Working together

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& sustainable business!

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In 2019

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