How To Develop A Business Model For Big Data Driven Innovation

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The Fifth “V“

Prof. Dr. Barbara Dinter

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TDWI 2015, München Prof. Dr. Barbara Dinter · The Fifth “V“ – Big Data Driven Innovation Slide 2

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Introduction

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Data driven business models

 Terminology and a framework

 Business models in the data economy

 Two frameworks for data driven business models

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Innovation (processes) on big data

 State of the Art

 Open Innovation / Crowd-sourced Analytics

 Open Data

 Challenges

Agenda

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Big Data – the “new oil“ (or gold) (?)

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At least: Big data as a (new) organizational resource for value creation which enables gaining competitive advantages

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Huge opportunity to develop new business models based on provided data and/or new analytics options

(Big) data driven development of business models

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Methods and technologies for highly scalable capturing, storing, and analyzing of

polystructured data

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Charakterized by “the three / four / five V‘s“:

 Volume: high data volume (no precise definition of “high“)

 Variety: including unstructured (text, video, …) and

semi-structured data (XML, …)

 Velocity: up to continuous data streams;

data capturing and analysis in neartime or realtime

 Varacity: biases, noise, and abnormality in data

 Value

Understanding of big data

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New business models due to big data

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Well-known examples:

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These pioneers have relied for quite a while and indeed successfully on new business models which are based on big data

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However, not only the big players on the market – on the contrary: The innovative usage of big data is a great chance for smaller

organizations to gain competitive advantages which would be hardly to achieve otherwise

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… how?

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Abstract representation of an organization, … be it conceptual, textual, and/or graphical, ...

of all core interrelated architectural, co-operational, and financial arrangements

... designed and developed by an organization presently and in the future, as well as all core products and/or services the organization offers, or will offer, based on these arrangements … that are needed

to achieve its strategic goals and objectives (Al-Debei and Avison, 2008)

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Frequently distinction of several partial models, such as:

 Value proposition (WHAT)

 Key activities (HOW)

 Revenue model (BY WHAT)

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A popular framework:

Business model Canvas by Osterwalder and Pigneur

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Special case: digital business models

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One example: DDBM by Hartmann et al., 2014 (cf. following slides)

The concept of business models

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Business models in the data economy –

a categorization according to Bitkom

Source: BITKOM, 2013: Management von Big-Data-Projekten. p. 17

Existing Data New Data New Business Monetarization Breakthrough

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A popular method to illustrate the innovation potential of big data

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Different versions available

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But … no “complete“ business model …

Value chain in the data economy

Source: BITKOM, 2013: Management von Big-Data-Projekten. p. 15

Data aggregation/ data marketplace Data integration/ data quality management Data products/ data services Data visualization/ data interpretation Data collection/ digitalization

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Data Driven Business Model

Framework (DDBM) by

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Source:

BITKOM,

2015:

Big Data und

Geschäftsmodell-Innovationen

in der

Praxis. p. Sub- model

Compo-nent Potential values

Service offererings/ market

adressing

Customer

segment B2B B2G B2C

Offer Data Information / Knowledge Tangible products (non-virtual offer) Business benefits Decisions / Risk assessment Process

optimization Profitability Pricing

Customer orientation/ exploitation of potential Service provision Key

resources Data Technology Know how Partner network Key activities Data generation Data acquisition Processing Aggre-gation Analytics Visuali-zation Distribution Key partner-ship

Data partners Technology and know how

partners Partners for customer access

Revenue model

Revenue model

Asset

selling “Leasing“ License

Usage fees Sub-scription Broker fees Adver-tisment

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Most contributions (scientific and practitioner-oriented) focus so far on:

 Establishment of a big data infrastructure and organization

 Innovation potential of big data in general and for specific cases

 “Results“, i.e. concrete use cases

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But not …

 How have the use cases / innovations been identified?

 Accidentially? Systematic innovation processes?

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Data based innovation processes (1)

Big Data

Products

Services

Ideation Feasability

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Adaptation of generic innovation processes seems to be a good option

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However, new methods (and maybe technical solutions) are needed

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First, rather unsystematic solutions in practice, e.g. in big data labs

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Big data labs

 Especially in large organizations (energy, telco, retail, automobile industry, etc.)

 Dedicated big data infrastructure (generic concept is adviced) and dedicated staff

 Various settings and various goals – among other to keep pace with the market

 So far rather optimization of existing use cases than true innovation

 Vendors offer meanwhile support; also universities run labs for different research purposes

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Lively start up culture – including venture capital firms for big data

Data based innovation processes (2)

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Starting point: data first vs. business first

Source: Vanauer/Böhle/Hellingrath: Guiding the Introduction of Big Data in Organizations: A Methodology with Business- and Data Driven Ideation and Enterprise Architecture Management-Based Implementation, 2015. p. 911

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„Open“ might help

Open

Innovation

Open

Data

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Innovation across organizational boundaries

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Collaboration between organizations, external experts, and customers,

focusing on value creation activities in the innovation process and aiming at the development of new products

(Reichwald & Piller, 2009).

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Distinction to «crowd-sourced analytics» not that clear

Open Innovation

New market

Market Ideas

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Data collections which are provided without any limitations for free use, distribution, and reuse for the public and societal benefit

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Examples: education material, geograhic data, statistics, traffic data, healthcare research results, etc

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Data market places:

 Consolidation and provision of data and data-related services for analytics

 Frequently integration of data from public sources (WWW, UNO, governments, …) and from exclusice, non-public sources (e.g. organizational data such as from ERP systems)

 Often infrastructure for data processing is provided as well

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Examples for usage of external / open data: John Deere, Vestas, Coca Cola (orange juice)

Open Data

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„The road to data-driven innovation is not paved.“

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Every beginning is hard …

Which big data, how to access it, …?

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Technical challenges

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Tempation to “only“ experiment with existing and known use cases

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Know how in the team (data scientists = innovators?)

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Typical problems:

 Privacy (even more relevant in the case of business models / innovation processes crossing organizational boundaries)

 Data quality

 Mid- and long-term funding

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Established methods for product and service design (design thinking!) – however not (yet) for data

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(Big) data driven innovation is different to the business intelligence domain

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Despite the variety of “colourful“ and impressive business models there is still limited methodological and technical support for the (systematic) development of business models

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As long as there is a shortage of best practices and/or research results: methods of “traditional“ business model developlement might help

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Appropriate team structure (data scientists!) should be considered

Conclusion

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