My Presentation at the Informatik 2015 conference about a paper by Prof. Frank Teuteberg and me: From Smart Meters to Smart Products: Reviewing Big Data driven Product Innovation in the European Electricity Retail Market As soon as the final publication of the paper is available, I will share the link here as well.

1. Accounting
and
Information
Systems
INFORMATIK 2015
WORKSHOP: „BIG DATA, SMART DATA AND SEMANTIC TECHNOLOGIES“
Nicolai Krüger, an@nicolaikrueger.de
Frank Teuteberg, frank.teuteberg@uni-­osnabrueck.de
From Smart Meters to Smart Products:
Reviewing Big Data driven Product Innovation in
the European Electricity Retail Market

2. © 2015 | Nicolai Krüger | Frank Teuteberg 2
Agenda
Background
1
Research Methodology
2
Review Results
3
Best Practices / Selected Cases
4
Implications
5

3. © 2015 | Nicolai Krüger | Frank Teuteberg 3
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)

4. © 2015 | Nicolai Krüger | Frank Teuteberg 4

5. © 2015 | Nicolai Krüger | Frank Teuteberg 5
Europe’s Electricity Market 1/3
Generator Reseller
End User
Wholesale Retail
KW
€
European Energy Exchange
€
MW
€
MW
(EEX) stock price
based on supply and demand
§ Power plants
§ Solar energy
§ Etc.
§ RWE
§ ENBW
§ Etc.
Simplified scheme of the electricity market in Europe

6. © 2015 | Nicolai Krüger | Frank Teuteberg 6
Europe’s Electricity Market 2/3
Generator Reseller
End User
Wholesale Retail
KW
European Energy Exchange
€
MW
€
MW
(EEX) stock price
based on supply and demand
§ Power plants
§ Solar energy
§ Etc.
§ RWE
§ ENBW
§ Etc.
Smart meters &
smart home devices
§ Google Nest
§ ENBW smart meter
§ Etc.
DataIndividual
predictions
Simplified scheme of the electricity market in Europe
including the European Union’s directive [Eu06]
on energy efficiency (à smart meters)
€
Individual offer

7. © 2015 | Nicolai Krüger | Frank Teuteberg 7
KW
€
Europe’s Electricity Market 3/3
Generator
End User
Wholesale Retail
European Energy Exchange
€
MW
€
MW
(EEX) stock price
based on supply and demand
§ Power plants
§ Solar energy
§ Etc.
Smart meters &
smart home devices
§ Google Nest
§ ENBW smart meter
§ Etc.
DataIndividual
predictions
Individual offer
Economical
perspective
Organizational/
Psychological
perspective
IT perspective
Integrated IS
view on big
data
Reseller
§ RWE
§ ENBW
§ Etc.
Why an integrated view is needed
to maximize the value of data

8. © 2015 | Nicolai Krüger | Frank Teuteberg 8
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?
Economical
perspective
Organizational/
Psychological
perspective
IT perspective
Integrated IS
view on big
data

9. © 2015 | Nicolai Krüger | Frank Teuteberg 9
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
and
abstract
filtering
Initial
Data Set
80 papers
Full text-
analysis
+
forward/
backward
search
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

10. © 2015 | Nicolai Krüger | Frank Teuteberg 10
Meta Analysis
0
1
2
3
4
5
6
7
8
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

11. © 2015 | Nicolai Krüger | Frank Teuteberg 11
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]

12. © 2015 | Nicolai Krüger | Frank Teuteberg 12
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],

13. © 2015 | Nicolai Krüger | Frank Teuteberg 13
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
PWC
Key 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 Wind
Forecasting 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 City
Sensor-­based predictions
[PW13] [IB11] [KB14]

14. © 2015 | Nicolai Krüger | Frank Teuteberg 14
Research Agenda
Empirical study
Case study
Literature review
Business maturity model
Reference model
Action research
Researchmethod
1. Scope and structure:
Identifying gaps,
methods applied and
results of former
research
2. Conceptual
construct:
Development of
theories, standards
and best practices
3. Validationand
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
Developmentofareferenceandabusinessmaturity
modelforbigdata-implementationwithfocuson
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)

15. © 2015 | Nicolai Krüger | Frank Teuteberg 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

16. Accounting and
Information
Systems
© 2015 | Nicolai Krüger | Frank Teuteberg 16
Prof. Dr.
Frank Teuteberg
frank.feuteberg@
uni-­osnabrueck.de
Nicolai Krüger
an@nicolaikrueger.de
www.uwi.uni-­osnabrueck.de

17. © 2015 | Nicolai Krüger | Frank Teuteberg 17
References 1/2
The 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.;; Pöppelbuß, 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 über die Elektrizitäts-­ 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 Einführung. 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.

18. © 2015 | Nicolai Krüger | Frank Teuteberg 18
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 für 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 für das
Datenzeitalter gerüstet? 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 empirische
Untersuchung. 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