Increasing analytics maturity by establishing analytics networks and spreading the use of Lean Six

148 Pages·2016·1.97 MB·English

by  Svantesson Romanov

Increasing analytics maturity by establishing
analytics networks and spreading the use of
Lean Six Sigma:

A case study of a global B2B company

IDA GULLQVIST
VIKTOR SVANTESSON ROMANOV

Master of Science Thesis
Stockholm, Sweden 2016
Att genom upprättning av nätverk och
spridning av Lean Six Sigma öka
mognadsgraden inom analytics:

En fallstudie på ett globalt B2B-företag

IDA GULLQVIST
VIKTOR SVANTESSON ROMANOV

Examensarbete
Stockholm, Sverige 2016
Increasing analytics maturity by establishing
analytics networks and spreading the use of
Lean Six Sigma:

A case study of a global B2B company

Ida Gullqvist
Viktor Svantesson Romanov

Examensarbete INDEK 2016:91
KTH Industriell teknik och management

Industriell ekonomi och organisation
SE-100 44 STOCKHOLM
Att genom upprättning av nätverk och
spridning av Lean Six Sigma öka
mognadsgraden inom analytics:

En fallstudie på ett globalt B2B-företag

Ida Gullqvist
Viktor Svantesson Romanov

Examensarbete INDEK 2016:91
KTH Industriell teknik och management

Industriell ekonomi och organisation
SE-100 44 STOCKHOLM
Examensarbete INDEK 2016:91

Att genom upprättning av nätverk och spridning
av Lean Six Sigma öka mognadsgraden inom
analytics:
En fallstudie på ett globalt B2B-företag

Ida Gullqvist
Viktor Svantesson Romanov
Godkänt Examinator Handledare
2016-06-08 Bo Karlson Jannis Angelis
Uppdragsgivare Kontaktperson
N/A N/A

Sammanfattning

Organisationer med högpresterande data- och analytics-kapacitet är mer framgångsrika än
företag med lägre data- och analytics-kapacitet. Det är därför viktigt att företag bedömer sin
mognad och företagets behov inom analytics för att identifiera och utvärdera områden för för-
bättring. Syftet med denna fallstudie var således att skapa en förståelse för hur organisationer
kan öka sin mognad inom analytics och den utfördes i en geografiskt avgränsad region av ett
globalt B2B-företag med en central analytics-funktion på huvudkontoret. Regionens önskan
var att integrera analytics i fler processer samt att analytiska färdigheter och resurser används
så effektivt som möjligt.

För att uppfylla studiens syfte samlades empirisk data in genom kvalitativa intervjuer med
anställda på huvudkontoret, mer kvantitativa intervjuer med regionanställda samt en enkätun-
dersökning inom den studerade regionen. Detta kompletterades med en grundlig litteraturstudie
där de mognadsmodeller för analytics som styrt identifieringen av organisationens nuvarande
kapaciteter på en övergripande nivå studerades, tillika analyticsstrukturer, Lean Six Sigma, och
Knowledge Management. Resultaten visar en relativt låg analytics-mognad p.g.a. bland annat
otillräcklig support från management, otydlig ansvarsfördelning för analytics, felaktigt använd
och otillräcklig efterfrågan på data samt olika problem med kompetens, verktyg och källor.

Studien bidrar till forskningsområdet genom att identifiera att de mognadsmodeller för analy-
tics som finns tillgängliga utan kostnad lämpar sig bra för inspiration men att de inte är fullstän-
diga då de saknar strategi för att just öka mognaden inom analytics. Vidare visar studien på att
svårigheter uppstår när en central analytics-funktion har låg analytics-mognad och inte något
tydligt mandat för att driva utvecklingen av analytics samtidigt som andra delar av företaget
vill gå fram inom analytics. Det största bidraget från studien är således att etableringen av
analytics networks kan möjliggöra för företag att höja sin mognadsgrad inom analytics. Genom
litteratur och empiri visas att nätverk inom organisationen ökar transparensen inom den globala
organisationen; anställda knyts samman och kan utnyttja varandras kunskap inom analytics och
en integrering av Lean Six Sigma i nätverket medför en naturlig länk mellan analytics och
verksamheten, varför dess mognad inom analytics kan ökas. Nätverken gör att problem lättare
lyfts till rätt nivå, lösningar hittas snabbare, dubbelarbete minskas samt att befintliga resurser
och kunskap används samtidigt som utvecklingsarbetet inom analytics effektiviseras.

Nyckelord: Data and Analytics mognadsmodeller, centralisering/decentralisering av analytics,
analytics strukturer, analytics nätverk, kunskapshantering och samarbete, Lean Six Sigma
Master of Science Thesis INDEK 2016:91

Increasing analytics maturity by establishing
analytics networks and spreading the use of
Lean Six Sigma:
A case study of a global B2B company

Ida Gullqvist
Viktor Svantesson Romanov
Approved Examiner Supervisor
2016-06-08 Bo Karlson Jannis Angelis
Commissioner Contact person
N/A N/A

Abstract

Organisations with high-performing data and analytics capabilities are more successful than
organisations with lower analytics maturity. It is therefore necessary for organisations to assess
their analytics capabilities and needs in order to identify and evaluate areas of improvement
that need to be addressed. This was the purpose of this case study conducted on a region in a
global B2B organisation, which has a centrally established analytics function on corporate
level, wanting the use of analytics to be integrated in more of the region’s processes and
analytical capabilities and resources being used as efficient as possible.

To fulfil the thesis purpose, empirical data was collected through qualitative interviews with
employees on corporate level, more quantitative interviews with regional employees and a
questionnaire issued to regional employees. This was complemented with a thorough literature
study which provided the analytics maturity models used for identifying the current capabilities
on a holistic level of the region, as well as analytics setups, Lean Six Sigma and Knowledge
Management. Results show a relatively low analytics maturity due to e.g. insufficient support
from management, unclear responsibility of analytics, data not being used correctly or
requested enough and various issues with competence, tools and sources.

This study contributes to analytics research by identifying that analytics maturity models
available free of charge only are good for inspiration and not full use when used in a large
company. Furthermore, the study shows that complexities arise when having a central analytics
function with low analytics maturity while other parts of the company face analytics problems
but no indications are given on who and what to proceed on or not. This study therefore results
in contributing with a proposition for companies wanting to increase its analytics maturity that
this could be facilitated by establishing networks for analytics. Combining literature and
empirics show that networks enable investigation of the analytics situation while at the same
time enabling increased sharing, collaboration, innovation, coordination and dissemination. By
making Lean Six Sigma a central part of the network analytics will be used more and better
while at the same time increasing the success-rate of change and improvements projects.

Key-words: Data and Analytics maturity models, centralisation/decentralisation of analytics,
analytics setups, analytics networks, knowledge management and collaboration, Lean Six
Sigma
Foreword
This study was conducted as a Master Thesis (degree project) in Industrial Engineering and
Management at KTH Royal Institute of Technology in Stockholm, Sweden. The thesis course was 30
credits and was conducted from January to June 2016 in Stockholm, Sweden.
Acknowledgements
During the five months this study was carried out, the authors have received great support from the
investigated company, from KTH Royal Institute of Technology and from industry experts. All who
have participated in the study one way or another have all contributed to the result of this study and
assured the quality of this research and report.
First, sincere gratitude is expressed to our supervisor at KTH, Associate Professor Dr. Jannis Angelis
for his great support and engagement in our project. Dr. Angelis has helped the authors to structure the
thesis and given feedback in order to clarify and improve the project. The authors would also like to
thank seminar leaders Assistant Professor Andreas Feldmann and Lecturer Bo Karlsson for their ever
challenging discussions which has greatly contributed to the final version of the thesis.
Without the support of the investigated company the thesis could not have been conducted as easily.
The company supervisors together with the rest of the employees at the investigated company have
provided incredible support and showed great interest for the project, thank you.
Finally, the authors would like to thank family and friends for their support and encouragement in
executing this project.

Stockholm, Sweden
June 2016
Ida Gullqvist & Viktor Svantesson Romanov

TABLE OF CONTENTS

1. INTRODUCTION ......................................................................................................................... 1
1.1. BACKGROUND .................................................................................................................... 1
1.2. CASE COMPANY ................................................................................................................. 2
1.3. PROBLEM FORMULATION ................................................................................................ 3
1.4. PURPOSE ............................................................................................................................... 3
1.5. RESEARCH QUESTIONS..................................................................................................... 4
1.6. DELIMITATIONS ................................................................................................................. 4
1.7. DISPOSITION ........................................................................................................................ 5
2. LITERATURE STUDY ................................................................................................................ 7
2.1. ASSESSMENT OF DATA & ANALYTICS MATURITY ................................................... 7
2.1.1. Data & Business Analytics Maturity............................................................................... 7
2.1.2. Cosic et al. Business Analytics Capability Framework .................................................. 8
2.1.3. Other Data & Analytics Maturity Models ..................................................................... 10
2.2. ANALYTICS SETUPS ........................................................................................................ 14
2.2.1. What Analytical Setup is Preferable? ........................................................................... 14
2.2.2. Weighing Centralisation versus Decentralisation ......................................................... 15
2.2.3. Reaching the Wanted Outcome of a CoE ..................................................................... 17
2.3. PROCESSES METHODOLOGIES INFLUENCEING ANALYTICS ................................ 18
2.3.1. The Underlying Methodologies of Lean Six Sigma ..................................................... 18
2.3.2. The Essence of Lean Six Sigma .................................................................................... 18
2.3.3. Using Lean Six Sigma in Combination with Large Datasets and Analytics ................. 20
2.3.4. Spreading the Use of Lean Six Sigma ........................................................................... 21
2.4. KNOWLEDGE MANAGEMENT ....................................................................................... 21
2.4.1. Relationship Between Data, Information & Knowledge .............................................. 21
2.4.2. Perspectives on Knowledge Management .................................................................... 23
2.4.3. Global Knowledge Management................................................................................... 24
2.4.4. The Importance of Transferring and Sharing Knowledge ............................................ 24
2.4.5. Using Networks for Increased Collaboration and Dissemination of Knowledge ......... 27
2.5. SUMMARY OF LITERATURE STUDY ............................................................................ 29
2.5.1. Data & Analytics Maturity Models ............................................................................... 29
2.5.2. Analytics Setups ............................................................................................................ 29
2.5.3. Lean Six Sigma ............................................................................................................. 29
2.5.4. Knowledge Management .............................................................................................. 30
3. METHOD .................................................................................................................................... 31
3.1. METHODOLOGICAL APPROACH ................................................................................... 31
3.2. RESEARCH DESIGN .......................................................................................................... 32
3.2.1. Avoiding Data Overload and Structuring ..................................................................... 33
3.3. METHODS USED FOR ANSWERING RESEARCH QUESTIONS ................................. 34
3.3.1. Methods Used for Answering SQ1 ............................................................................... 34
3.3.2. Methods Used for Answering SQ2 ............................................................................... 35
3.3.3. Methods Used for Answering SQ3 ............................................................................... 35
3.3.4. Methods used for answering MRQ ............................................................................... 36
3.4. LITERATURE STUDY ........................................................................................................ 36
3.5. INTERVIEWS ...................................................................................................................... 37
3.5.1. Methods Used for Analysing Interviews....................................................................... 39
3.6. QUESTIONNAIRE .............................................................................................................. 39
3.6.1. Methods Used for Analysing the Questionnaire ........................................................... 40
3.7. QUALITY OF RESEARCH ................................................................................................. 41
3.7.1. Internal Validity ............................................................................................................ 41
3.7.2. Construct Validity ......................................................................................................... 41
3.7.3. External Validity ........................................................................................................... 42
3.7.4. Reliability ...................................................................................................................... 42
4. RESULTS & ANALYSIS ........................................................................................................... 44
4.1. RESULTS FROM FINAL INTERVIEWS WITH EMPLOYEES AT HEADQUARTERS 44
4.1.1. Interviews with Employees from the Centrally Established Analytics Function .......... 44
4.1.2. Interview with an Employee at Central Business Excellence ....................................... 49
4.1.3. Interview with a Corporate Knowledge Management & Collaboration Employee ...... 51
4.1.4. Interview with a Corporate Business Finance Employee.............................................. 52
4.2. RESULTS FROM FINAL INTERVIEWS WITH REGIONAL EMPLOYEES .................. 53
4.2.1. Interview Outline .......................................................................................................... 53
4.2.2. Analytical Activities, Sources and Tools ...................................................................... 54
4.2.3. Problems and Structuring of Data and Analytics .......................................................... 54
4.2.4. Collaboration, Sharing and Networks ........................................................................... 55
4.2.5. Lean Six Sigma and Willingness to Change ................................................................. 56
4.2.6. Specific Data and Analytics Maturity Questions .......................................................... 57
4.3. RESULTS FROM THE ISSUED QUESTIONNAIRE ........................................................ 58
4.3.1. Questionnaire Outline ................................................................................................... 58
4.3.2. General Data and Analytics Questions ......................................................................... 58
4.3.3. Networks ....................................................................................................................... 59
4.3.4. Analytical Activities, Sources and Tools ...................................................................... 59
4.3.5. Analytics Pain Points .................................................................................................... 60
4.3.6. Support and Definition of Analytics Objectives ........................................................... 61
4.3.7. Additional Comments ................................................................................................... 61
4.4. ANALYSIS OF RESULTS .................................................................................................. 61
4.4.1. Comparison of Results from Region Interviews and Questionnaire ............................. 61
4.4.2. Results Applied to the Chosen Analytics Maturity Model ........................................... 63
4.4.3. Major Problem Areas .................................................................................................... 65
5. DISCUSSION .............................................................................................................................. 68
5.1. BALANCING CENTRALISATION AND DECENTRALISATION OF ANALYTICS .... 68
5.2. FOCUSING ON KNOWLEDGE MANAGEMENT AND NETWORKS ........................... 70
5.3. USING LSS FOR DRIVING CHANGE IN ANALYTICS ................................................. 73
5.4. ACTIONS APPLIED TO COSIC ET AL.’S FOUR CAPABILITIES ................................. 75
6. CONCLUSIONS ......................................................................................................................... 77
6.1. ANSWERING THE RESEARCH QUESTIONS ................................................................. 77
6.2. CONCEPTUAL CONTRIBUTION ..................................................................................... 80
6.2.1. Dysfunctional Maturity Models .................................................................................... 80
6.2.2. Difficult to Balance Centralised Analytics with Pressing Needs .................................. 80
6.2.3. Establishing Analytics Networks .................................................................................. 81
6.3. MANAGERIAL CONTRIBUTION ..................................................................................... 83
6.3.1. Short Term .................................................................................................................... 83
6.3.2. Long Term .................................................................................................................... 84
6.3.3. Action Points for Companies Establishing Analytics Networks ................................... 86
6.4. SUSTAINABILITY .............................................................................................................. 86
6.5. LIMITATIONS & FUTURE RESEARCH .......................................................................... 87
BIBLIOGRAPHY ............................................................................................................................... 89