from smart meters to smart products: reviewing big data driven product innovation in the european...
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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
© 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)
© 2015 | Nicolai Krüger | Frank Teuteberg 4
© 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 pricebased on supply and demand
§Power plants§Solar energy§Etc.
§RWE§ENBW§Etc.
Simplified scheme of the electricity market in Europe
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Europe’s Electricity Market 2/3
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.
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
© 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 pricebased 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 neededto maximize the value of data
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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
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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
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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
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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]
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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],
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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]
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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)
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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
© 2015 | Nicolai Krüger | Frank Teuteberg 16
Prof. Dr. Frank Teutebergfrank.feuteberg@uni-osnabrueck.de
Nicolai Krü[email protected]
www.uwi.uni-osnabrueck.de
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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.
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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