From Smart Meters to Smart Products: Reviewing Big Data driven Product Innovation in the European Electricity Retail Market

Accounting and Information Systems

INFORMATIK 2015WORKSHOP: „BIG DATA, SMART DATA AND SEMANTIC TECHNOLOGIES“

Nicolai Krüger, [email protected] Teuteberg, frank.teuteberg@uni-osnabrueck.de

From Smart Meters to Smart Products: Reviewing Big Data driven Product Innovation in

the European Electricity Retail Market

© 2015 | Nicolai Krüger | Frank Teuteberg 2

Agenda

Background1

Research Methodology2

Review Results3

Best Practices / Selected Cases4

Implications5


Overall Research Approach / PhD Program

Economical perspective

Organizational/Psychological perspective

IT perspective

Integrated IS view on big data

Integrated research at the chair for Accounting and Information Systems @Osnabrück University

Bridge to workshop goals: „We believe that using Big Data (...) will result in more sustainable and efficient processes and systems.“ (FZI;; Workshop aims and scope, https://goo.gl/9S02Lo)



Europe’s Electricity Market 1/3

Generator Reseller

End User

Wholesale Retail

KW€

European Energy Exchange

€ MW€MW

(EEX) stock pricebased on supply and demand

§Power plants§Solar energy§Etc.

§RWE§ENBW§Etc.

Simplified scheme of the electricity market in Europe



Generator Reseller

End User

Wholesale Retail

KW


€ MW€MW



§RWE§ENBW§Etc.

Smart meters & smart home devices§ Google Nest§ ENBW smart meter§ Etc.

DataIndividual predictions

Simplified scheme of the electricity market in Europeincluding the European Union’s directive [Eu06] on energy efficiency (à smart meters)

€Individual offer


KW€


Generator

End User

Wholesale Retail


€ MW€MW



Smart meters & smart home devices§ Google Nest§ ENBW smart meter§ Etc.

DataIndividual predictions

Individual offer



IT perspective

Integrated IS view on big

data

Reseller§RWE§ENBW§Etc.

Why an integrated view is neededto maximize the value of data


Research Scope and Background

Linking big data, change, product innovation and smart metering

¡ RQ1: How can change and product innovation management enable the effectiveness (e.g. in terms of cost-savings or additional turnover) of big data initiatives?

¡ RQ2: Which change and product innovation management enablers (e.g. communication, transformation strategy and so forth) can be applied for smart metering within EI?



IT perspective

Integrated IS view on big

data


Artefact 2:Practical implications & best practices

Artefact 3:Theoretical implications & research agenda

Artefact 1:Open problems

Applied research methods of analyzed papers

Research Methodology

23 journals,3 conferences

Database access via EBSCO-

Host, Science-

Direct, etc.119 papers

Journal Selection

Results of Keyword Search

Title, keyword

andabstractfiltering

Initial Data Set

80 papers

Full text-analysis

+forward/

backwardsearch

Final Data Set Within

Research Scope

66 papers

(-) out of scope(+) forward/backward

results

(-) out of scope

Research perspective of analyzed papers

Quantitative and chronological result analysis

Research focus of analyzed papers

Systemic literature review (LR) [Br09]

Analysis and conclusion

Meta Analysis


Meta Analysis

012345678

2006 2008 2009 2010 2011 2012 2013 2014 2015

Technological/Mathematical Economical Organizational/Social General Science

Research perspective of analyzed articles over time (up to 04/15)Articles

Overlapping and solitary research focuses found

§ Sum of articles published in 2015 so far, already reached the

amount of total publications on our topic in 2012

§ Small amount of combined perspectives

§ Latest research tend to provide a cross-disciplinary approach


Content Analysis of Core Articles

From broad to narrow research outcomes:

¡ Future research scope of Energy Informatics [Go14b];; [Re14]

¡ First ideas how to utilize and monetize smart metering [WBN15];; [AZ14]

¡ Demand Response System / Smart Grid implementation [BF14]

¡ Integrating electric vehicles into the Smart Grid [BFN13] and building intelligent pricing systems and business models with both [BWN12]

¡ Achieving disruptive innovation in the automobile market through mobile apps for electronic vehicles [Ha15b]


Unsolved Problems

• Implementation of data-driven thinking into the different disciplines (EI/IS)•Further discussion of moral and ethical questions like data privacy necessary

General science discipline

•Potential of matching smart grids, smart meters, electric vehicles and other decentralized sources•Future data availability and effectiveness for steering of smart grids

•Value creating implementation of smart metering into energy retail organizations •Future business models for data aggregators, collecting and using data from smart meters and offering their infrastructure for data processing to electricity retailers

•Upcoming behavioral risks through big data •Reduction of implementation costs of big data initiatives •Handling decision processes in organizations, which become more complex through big data

Techno-/Mathe-matical

Economical

Organi-zational/Social

[Bi13],[Bu13b], [Wo14]

[BF14], [SK12], [Kr15],[KW15],[KBK12]

[AZ14],[BWN12],[KBK12],[BF14]

[Ol14],[BH08],[By10],[Bu13a],


Best Practices / Selected Cases

How to handle the transition

from the classical energy- to

a modern service-provider?

1. Business model change

2. Organizational change

3. Transforming core

processes of the business

4. Integration of technology

5. Setting up data protection

and -security

6. Legal adjustment of the

business

PWCKey steps for transition

Understanding the data

problem behind the

business transformation

1. Wind forecasting is a big

data exercise

2. Implementation of

weather data from internal

and external sources

è Intraday electricity trading

optimized

è Data-driven investment

strategies for new

turbines

Vestas WindForecasting competences

Sensor data is a

cornerstone for big data -

driven business

• Citizens marked potholes

in Boston via App

• Boston’s government was

unable to handle the data

è (Electricity) Retailers must

prepare themselves for

handling volume and

velocity

è Business processes have

to integrate data

Boston CitySensor-based predictions

[PW13] [IB11] [KB14]


Research Agenda

Empirical study

Case study

Literature review

Business maturity model

Reference model

Action research

Res

earc

h m

etho

d

1. Scope and structure:Identifying gaps, methods applied and results of former research

2. Conceptual construct:Development of theories, standards and best practices

3. Validation and evaluation:Empirical validation of developed concepts

4. Continuous improvement:Regular reflection-and implementation-process

Research maturity

x

x

x

x

x

x x x

x

Developm

ent of a reference and a business maturity

model for big data -im

plementation w

ith focus on change &

innovation

x x x

¡ Development of a reference and a business maturity model for big data - implementation with focus on change & innovation

¡ Literature Review reflected status quo of the topic¡ Case Study paper applied (9/15)


Summary

¡ Take Aways§ Growing interest and need for Energy Informatics to conduct cross-disciplinary research to include non-technical aspects

§ Electricity Retailers have to implement data-driven thinking into their strategy and processes, new approaches, skills and people might be needed for this

¡ Limitations§ Searching for cross-disciplinary papers only might put single-domain papers at a disadvantage

§ Best Practices / Cases pass the line of a classical systemic LR

¡ Future Research§ Iterative research with growing maturity and empirical reliability

§ Targeting on reference / business maturity model

Accounting andInformation Systems


Prof. Dr. Frank Teutebergfrank.feuteberg@uni-osnabrueck.de

Nicolai Krü[email protected]

www.uwi.uni-osnabrueck.de


References 1/2The complete reference list, including the 66 articles analyzed in our literature review, is permanently available on: http://goo.gl/AZdSIs[Bi13] BITKOM: Management von Big-Data-Projekten. Leitfaden. Available on

https://www.bitkom.org/files/documents/LF_big_data2013_web.pdf, downloaded on the 31st of March 2015.

[BKP09] Becker, J.;; Knackstedt, R.;; Poppelbuß, J.: Developing Maturity Models for IT Management. Business & Information Systems Engineering, 1(3), P. 213–222, 2009.

[Br09] Von Brocke, J. et.al.: Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process. ECIS-Proceedings, Verona 2009.

[Ch13] Christensen, C.M.: The innovator’s dilemma. When new technologies cause great firms to fail. Boston, 2013.

[En10] Energiewirtschaftsgesetz – EnWG §21c: Gesetz uber die Elektrizitats- und Gasversorgung. Einbau von Messsystemen, 2010.

[Eu06] European Union: Directive 2006/32/EC of the European Parliament and of the council of 5 April 2006 on energy end-use efficiency and energy services and repealing Council Directive 93/76/EEC, 2006.

[Fli11] Flick, U.: Triangulation. Eine Einfuhrung. Wiesbaden, 3rd Edition, 2011. [IB11] IBM: Vestas. Turning climate into capital with big data. Available on http://www-

01.ibm.com/common/ssi/cgi-bin/ssialias?infotype=PM&subtype= AB&htmlfid=IMC14702USEN#loaded, downloaded on the 6th of April 2015.


References 2/2[Ko12] Kotter, J.P.: Leading Change. Massachusetts, 2012. [MF09] Martens, B.;; Teuteberg, F.: Why Risk Management Matters in IT Outsourcing - A Systematic

Literature Review and Elements of a Research Agenda. ECIS- Proceedings, Verona 2009. [OL13] O’Leary: Exploiting big data from mobile device sensor-based apps: Challenges and benefits.

MIS Quarterly Executive, 12(4), P. 179–187, 2013. [PW13] Smart Metering. Intelligente Messsysteme fur die Energienetze von morgen. Available on

http://blogs.pwc.de/auf-ein-watt/files/2013/08/Smart_Metering-Intelligente_Messsysteme_f%C3%BCr_die_Energienetze_von_morgen.pdf, downloaded on the 31st of March 2015.

[TS13] T-Systems International GmbH: Energie durch Big Data. Sind Europas Energieversorger fur das Datenzeitalter gerustet? 2013.

[VB09] Von Brocke, J.;; Simons, A.;; Niehaves, B. et.al.: Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process. Retrieved February 25, 2015.

[WH07] Wilde, T.;; Hess, T. (2007). Forschungsmethoden der Wirtschaftsinformatik – Eine empirischeUntersuchung. Wirtschaftsinformatik, 49 (4), P. 280-287, 2007.

[WK08] WKWI: WI-Orientierungslisten. Wirtschaftsinformatik 50 (2), P. 155-163, 2008.

Keyvisual Title Page: flickr.com/Todd Smith, https://goo.gl/PS3onP

From Smart Meters to Smart Products: Reviewing Big Data driven Product Innovation in the European Electricity Retail Market

Technology