How To Develop A Business Model For Big Data Driven Innovation

Transcription

1 Fakultät für Wirtschaftswissenschaften The Fifth V How Big Data Can Create Value By Data Driven Innovation Prof. Dr. Barbara Dinter Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 1

2 Agenda Introduction Data driven business models Terminology and a framework Business models in the data economy Two frameworks for data driven business models Innovation (processes) on big data State of the Art Open Innovation / Crowd-sourced Analytics Open Data Challenges Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 2

3 Motivation Big Data the new oil (or gold) (?) At least: Big data as a (new) organizational resource for value creation which enables gaining competitive advantages Huge opportunity to develop new business models based on provided data and/or new analytics options (Big) data driven development of business models Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 3

4 Understanding of big data Methods and technologies for highly scalable capturing, storing, and analyzing of polystructured data 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 Source: Zkopoulos et al., 2012 Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 4

5 New business models due to big data Well-known examples: These pioneers have relied for quite a while and indeed successfully on new business models which are based on big data 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 Many startups emerge that way! Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 5

6 But... how? Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 6

7 The concept of business models 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) Frequently distinction of several partial models, such as: Value proposition (WHAT) Key activities (HOW) Revenue model (BY WHAT) A popular framework: Business model Canvas by Osterwalder and Pigneur Special case: digital business models One example: DDBM by Hartmann et al., 2014 (cf. following slides) Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 7

8 The Canvas model (Osterwalder & Pigneur, 2010) Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 8

9 Business models in the data economy a categorization according to Bitkom Existing Data New Data New Business Monetarization Breakthrough Existing Business Optimization Enhancement Source: BITKOM, 2013: Management von Big-Data-Projekten. p. 17 Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 9

10 Value chain in the data economy Data collection/ digitalization Data integration/ data quality management Data aggregation/ data marketplace Data products/ data services Data visualization/ data interpretation Big data technologies and infrastructure Source: BITKOM, 2013: Management von Big-Data-Projekten. p. 15 A popular method to illustrate the innovation potential of big data Different versions available But no complete business model Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 10

11 Data Driven Business Model Framework (DDBM) by Hartmann et al., 2014 Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 11

12 Source: BITKOM, 2015: Big Data und Geschäftsmodell-Innovationen in der Praxis. p. 16 Data driven business models according to BITKOM Service offererings/ market adressing Service provision Revenue model Customer segment Potential values B2B B2G B2C Offer Data Information / Knowledge Business benefits Key resources Key activities Revenue model Decisions / Risk assessment Data generation Asset selling Process optimization Profitability Pricing Tangible products (non-virtual offer) Customer orientation/ exploitation of potential Data Technology Know how Partner network Data acquisition Data partners Leasing Processing License Technology and know how partners Usage fees Analytics Submodel Component Aggregation Visualization Distribution Partners for customer access Broker fees Key partnership Subscription Advertisment Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 12

13 The goal is now known but how to reach it? 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 But not How have the use cases / innovations been identified? Accidentially? Systematic innovation processes? Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 13

14 Data based innovation processes (1) Products Services Big Data Ideation Feasability study Prototyping Decision... Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 14

15 Data based innovation processes (2) Adaptation of generic innovation processes seems to be a good option However, new methods (and maybe technical solutions) are needed First, rather unsystematic solutions in practice, e.g. in big data labs 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 Lively start up culture including venture capital firms for big data Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 15

16 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, p. 911 Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 16

17 Open might help Open Innovation Open Data Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 17

18 Open Innovation Innovation across organizational boundaries 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). New market Ideas Market Distinction to «crowd-sourced analytics» not that clear Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 18

19 Examples Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 19

20 Open Data Data collections which are provided without any limitations for free use, distribution, and reuse for the public and societal benefit Examples: education material, geograhic data, statistics, traffic data, healthcare research results, etc 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 Examples for usage of external / open data: John Deere, Vestas, Coca Cola (orange juice) Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 20

21 Challenges The road to data-driven innovation is not paved. Every beginning is hard Which big data, how to access it,? Technical challenges Tempation to only experiment with existing and known use cases Know how in the team (data scientists = innovators?) Typical problems: Privacy (even more relevant in the case of business models / innovation processes crossing organizational boundaries) Data quality Mid- and long-term funding Established methods for product and service design (design thinking!) however not (yet) for data Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 21

22 Conclusion (Big) data driven innovation is different to the business intelligence domain Despite the variety of colourful and impressive business models there is still limited methodological and technical support for the (systematic) development of business models As long as there is a shortage of best practices and/or research results: methods of traditional business model developlement might help Appropriate team structure (data scientists!) should be considered Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 22

23 Contact Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide 23