This presentation was held by Professor Christine Legner (HEC Lausanne) at the Swiss Day on November 8, 2017, in Lausanne, Switzerland. It addresses the need for organisations to think about data and its management in new ways, as many corporations engage in the digital and data-driven transformation of their business. It concludes with three recommendations: 1) assess data's business value and impact, 2) measure and improve data quality, and 3) democratize data and support data citizenship.

1. Christine Legner
Professor of
Information Systems &
Academic Director CC CDQ
HEC Lausanne
Managing Data as a Strategic Resource –
Foundation of the Digital and Data-Driven
Enterprise
Swiss Data Day – November 8, 2017

2. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 2
The Competence Center Corporate Data Quality (CC CDQ)
is an expert community and research consortium
2006
Foundation
+30
Members
+50
CC CDQ
Workshops
+1500
Contacts within
CDQ community
+100
Bilateral Projects
Consortium research is being conducted in association between research institutions and companies
NB: Overview comprises both current and former partner companies

3. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 3
Effective data management –
foundation of the digital and data-driven enterprise
GOALS ENABLERS
DATA
STRATEGY
PEOPLE, ROLES &
RESPONSIBILITIES
PROCESSES &
METHODS
DATA
LIFECYCLE
DATA
APPLICATIONS
DATA
ARCHITECTURE
PERFORMANCE
MANAGEMENT
BUSINESS
CAPABILITIES
DATA
MANAGEMENT
CAPABILITIES
RESULTS
BUSINESS
VALUE
DATA
EXCELLENCE
The CDQ Data Excellence Model https://cc-cdq.ch/data-excellence-model

4. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 4
Agenda
1. The changing role of data – data as a strategic resource
2. Real-world challenges in the digital and data-driven enterprise
3. Conclusion

5. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 5
Data is a valuable resource – not only for tech giants!
http://www.economist.com/news/leaders/
21721656-data-economy-demands-new-
approach-antitrust-rules-worlds-most-valuable-
resource
“Data is becoming the new raw
material of business: an economic
input almost on par with capital and
labor. Every day I wake up and ask
how can I flow data better, manage
data better, analyze data better.”
Rollin Ford
Chief Administrative Officer
Walmart

6. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 6
Accessed from https://www.amazon.com/adidas-miCoach-G83963-Smart-Ball/dp/B00L7R2CWO on 2016-06-22
Digital and data-driven business models

7. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 7
Transformation towards the digital and data-driven enterprise
leads to the understanding of data as a strategic resource
Data-driven enterprise (Provost & Fawcett 2013; Davenport, 2014)
Goals:
• maximize the use of data and analytics
• promote data-driven and fact-based management
approaches
Priorities:
• leverage BI and analytics for real-time decisions
• explore big data platforms and advanced analytics
New roles and stakeholders:
• Chief Data Officer, data scientists, BI experts...
Digital transformation (Matt et al. 2015; Westerman et al. 2014)
Goals:
• use of digital technologies to radically improve
performance and reach of the enterprise
Priorities:
• digital business models and products/services
• operational excellence in existing business
processes
• digital customer experience and interaction
New roles and stakeholders:
• Chief Digital Officer, digital initiatives, …
Two complementary (yet overlapping) trends
It is all about data!

8. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 8
Traditional data management mainly focuses on operational
business processes
Based on Schierning (2016): Digitalization - Challenges and Opportunities for Product Based on Information Management. Presented at the 48th CC CDQ Workshop on February 25th 2016
Company
Source Produce Distribute
Demand
Order Fulfillment Cycle
(fulfill the demand)
Product/
Service
Traditional focus:
operational excellence

9. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 9
Digital and data-driven businesses rely on closed information
loops and integrate the customer in real-time
Based on Schierning (2016): Digitalization - Challenges and Opportunities for Product Based on Information Management. Presented at the 48th CC CDQ Workshop on February 25th 2016
Company
Source Produce Distribute
Promote
Demand
Activation Cycle
(communicate benefits and create demand)
Order Fulfillment Cycle
(fulfill the demand)
Product/
Service
Consume
/Use
Customer interaction
Personalized products
& services
Industry 4.0

10. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 10
Digital and data-driven businesses rely on closed information
loops – involving customers, suppliers, R&D partners and more
Based on Schierning (2016): Digitalization - Challenges and Opportunities for Product Based on Information Management. Presented at the 48th CC CDQ Workshop on February 25th 2016
Company
Innovation Cycle
(align product / service offering to
customer needs)
Insight
Source Produce DistributeDevelop
Ideation
Benefit Promote
Demand
Activation Cycle
(communicate benefits and create demand)
Order Fulfillment Cycle
(fulfill the demand)
Product /
Service
Idea
Product/
Service
Collaborative
innovation
Regulatory
requirements
Consume
/Use
Customer interaction
Personalized products
& services
Industry 4.0

11. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 11
Data?!?
The data universe is becoming increasingly complex!
Leveling et al.: Big Data Analytics for Supply Chain Management, 2014.
Community & Reference Data:
business partner addresses,
standards, regulations, country
codes, GTINs
Big & Open Data:
sensor data, tweets, social media
streams, weather data, news, …
…
Corporate Nucleus Data:
master data, transaction data,
company documents
Vendor
Product
Customer

12. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 12
Agenda
1. The changing role of data – data as a strategic resource
2. Real-world challenges in the digital and data-driven enterprise
3. Conclusion

13. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 13
In practice, data is hardly managed as a strategic resource
Source:
1) http://www.cio.com/article/2375573/leadership-management/cios-consider-putting-a-price-tag-on-data.html
2) https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards
3) https://hbr.org/2016/12/breaking-down-data-silos
“Only 3% of companies’ data meets basic quality
standards.”
Harvard Business Review, September 2017
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
“80% of the work involved (in advanced data analytics) is
acquiring and preparing data.”
Harvard Business Review, December 2016

14. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 14
Only few companies know the value of their data
Big Data At Caesars Entertainment –
A One Billion Dollar Asset?
The most valuable of the individual assets …
is the data collected over the last 17 years
through the company’s Total Rewards loyalty
program, which gained Caesar’s a reputation
as a pioneer in Big Data-driven marketing.
How much worth is
your data?
https://www.forbes.com/sites/bernardmarr/2015/05/18/when-big-data-becomes-your-most-valuable-asset/#561009e1eefd

15. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 15
Reproduction Cost Method2
Financial valuation of data
Market approach
Based on market prices or multiples
Often not suitable.
In many cases, markets and market
prices for intangible assets do not exist.
What is the price a buyer would pay for
an asset on a competitive market?
Income approach
Present value of cash flows attributable
to an asset.
Suitable.
Cash flows from the use of data are a
good measure for data value.
What is the value that my data generates
in the business processes?
Cost approach
Reproduction or replacement cost
Suitable.
In many cases, data reproduction cost
can be quantified reliably.
How much would it cost to reproduce or
replace an asset?
Approach1)
Concept
Data valuation
context
Leading question
1) Table adapted from IDW S5
2) The cost-based approach for data valuation was developed and practically applied in a prior research project. An overview of the concept and functioning of the cost-based valuation approach is provided in: Schmaus, P. (2015). Bewertung von Stammdaten als Intangible Asset.
Controlling, 27(7), 392–395. doi:10.15358/0935-0381-2015-7-392. For additional documentation and background on the cost-based valuation tool please do not hesitate to contact the authors of this presentation.
3) The cost-based approach for data valuation was developed and practically applied in a prior research project. Zechmann, A. & Möller, K. (2016). Finanzielle Bewertung von Daten als Vermögenswerte. Controlling, 28(10), 558-566.
Quantity of
Customer Master
Master Data
Production Costs
445.579,57 EUR
Average Master
Data Age
29 months Average Quality [%] 89,40%
Total Usage
Impairment
301.212,87 EUR
Total Usage
Impairment [%]
76,88%
Total Quality
Impairment
90.595,04 EUR
Total Quality
Impairment [%]
23,12%
Total Others
Impairment
-13,24 EUR
Total Others
Impairment [%]
0,00%
Total Impairment 391.794,68 EUR
Total Impairment
[%]
87,93%
Value of Customer Master Data
53.784,89 EUR
Phase 2 - Valuation & Analysis
2.3 Calculating value of master data and analysis
Customer Master Data
Spezification
ERP-Data (SAP),
Country=DE,
Account Group
(tbd)
Information about Customer Master Data
Customer Master Data Valuation
10.000
Previous [1.5.3] Process
445.579,57
391.794,68
53.784,89
0,00
50.000,00
100.000,00
150.000,00
200.000,00
250.000,00
300.000,00
350.000,00
400.000,00
450.000,00
500.000,00
Master Data
Production Costs
Total Impairment
Value of Customer
Master Data
76,88%
23,12%
0,00%-25,00%
0,00%
25,00%
50,00%
75,00%
100,00%
Total Usage
Impairment [%]
Total Quality
Impairment [%]
Total Others
Impairment [%]
87,93%Tools and
methods for
application
Use-based valuation3Data value multiplies
Examples:
Data market prices
Example:
A. Zechmann: Data Valuation

16. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 16
Cost approach: Applying a »Reproduction Cost Method«1 to
measure customer master data
The numbers presented are an example and do not represent the actual figures of the valuation case.
1) Schmaus, P. (2015). Bewertung von Stammdaten als Intangible Asset. Controlling, 27(7), 392–395.
What would it cost to produce a perfect duplicate of data with same attributes and the same DQ?
reduced by
Guiding question
Functioning Cost to reproduce data
Adjustment charges due to
lacking DQ
Cost-based data value
equals
A. Zechmann: Data Valuation
General accounting principles
Class No. Data Quality
Impairment
Percentage
1 < 50% 95%
2 ≥ 50%; < 80% 80%
3 ≥ 80%; < 90% 30%
4 ≥ 90%; < 98% 10%
5 ≥ 98% 0%
Class Last Use Category
Impairment
Percentage
1 within last 6 months 0%
2 > 6 months; ≤ 12 months 10%
3 > 12 months; ≤ 24 months 50%
4 > 24 months; ≤ 36 months 75%
5 > 36 months 95%

17. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 17
Cost approach: Applying a »Reproduction Cost Method«1 to
measure customer master data
Company 1
5m €
2m €
3m €
≈500’000 records
9m €
5m €
2m €
3m €
The numbers presented are an example and do not represent the actual figures of the valuation case.
9m €
6m €
5m €
2m €
3m €
-60%
Customer Master Data
Value
9 mn €
Master Data
Production Cost
6 mn €
DQ Adjustment
Charges
15 mn €
Company 2
-40%
Customer Master Data
Value
DQ Adjustment
Charges
3 mn €
2 mn €
5 mn €
Master Data
Production Cost
≈80’000 records
Example
3 : 1
~6 : 1
1) Schmaus, P. (2015). Bewertung von Stammdaten als Intangible Asset. Controlling, 27(7), 392–395.
What would it cost to produce a perfect duplicate of data with same attributes and the same DQ?
reduced by
Guiding question
Functioning Cost to reproduce data
Adjustment charges due to
lacking DQ
Cost-based data value
equals
A. Zechmann: Data Valuation

18. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 18
Income approach: Applying a »Use-based Method«1 to
measure product master data
1) Zechmann, A. & Möller, K. (2016). Finanzielle Bewertung von Daten als Vermögenswerte. Controlling, 28(10), 558-566.
What are economic benefits an organization obtains by using data in specific data use contexts of a business process?
result in
Guiding question
Functioning Data use contexts
Economic benefits given actual
DQ
Use-based data value
equals
610
TEUR
-250
TEUR
-100
TEUR
590
TEUR
Data quality management cost
Cost from using data
610
TEUR
610
TEUR
Year 1Today Year 2 Year 3 Steady state
Cost savings per period
-250
TEUR
-100
TEUR
-250
TEUR
-100
TEUR
-250
TEUR
-100
TEUR
-250
TEUR
-100
TEUR
Cash flows from the use of product
master data in customer service process
Discounted cash flow valuation
Use-based value of
product master data
2.232
TEUR
Valuation
assumptions:
Discount rate: 10%
Growth rate: 0%
The numbers presented are an example and do not represent the actual figures of the valuation case.
A. Zechmann: Data Valuation

19. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 19
Recommendations for managing data as a strategic resource
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
Assess the business value and impact of data
à Data valuation

20. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 20
In practice, data is hardly managed as strategic resource
“Only 3% of companies’ data meets basic quality
standards.”
Harvard Business Review, September 2017
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
“80% of the work involved (in advanced data analytics) is
acquiring and preparing data.”
Harvard Business Review, December 2016
Source:
1) http://www.cio.com/article/2375573/leadership-management/cios-consider-putting-a-price-tag-on-data.html
2) https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards
3) https://hbr.org/2016/12/breaking-down-data-silos

21. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 21
Data often is not « fit for purpose »
https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards
https://hbr.org/2016/07/assess-whether-you-have-a-data-quality-problem
Friday Afternoon Measurement (FAM) Method
• Managers assemble 10-15 critical data attributes for
the last 100 units of work completed by their
departments à 100 data records.
• Managers and their teams work through each
record, marking obvious errors.
• They then count up the total of error-free records
à Data Quality (DQ) Score (between 0-100)

22. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 22
Changing the mindset …
From reacting to data quality “incidents” … … to proactively managing data
Key: „Submarines“ of Master Data Quality (e.g. migrations, process
errors, inconsistent reports).
Master Data Quality
Time
Project 1 Project 2 Project 3
DQ-Optimum
Accuracy
Completeness
2000 2013 2014 2015 2016 2017
Maturity Level
3. Defined 4. Quant. managed 5. OpFmizing
MDM
operaFonal
Build up Network,
Governance,
Improvement
Extended
Governance
Governance
Governance
internal &
external
Material
Customer
Vendor
Data Domains new data domains
Schaeffler’s data management journey
CDQ Award 2016
https://www.cc-cdq.ch/cdq-good-practice-award

23. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 23
… and establishing data ownership in business functions
Automotive
Industrial
BA
BB
BC Regions
Functions
Divisions
Europe
Americas
Greater China
Asia / Pacific
CEO Functions
Operations
Finance
HR
R&D
From functional silos …
… to defined data ownership and
engagement model
Schaeffler’s data management journey
CDQ Award 2016
https://www.cc-cdq.ch/cdq-good-practice-award

24. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 24
Recommendations for managing data as a strategic resource
“Only 3% of companies’ data meets basic quality
standards.”
Harvard Business Review, September 2017
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
Measure and improve data quality
à Data governance, SMART data management
Assess the business value and impact of data
à Data valuation

25. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 25
In practice, data is hardly managed as strategic resource
“Only 3% of companies’ data meets basic quality
standards.”
Harvard Business Review, September 2017
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
“80% of the work involved (in advanced data analytics) is
acquiring and preparing data.”
Harvard Business Review, December 2016
Source:
1) http://www.cio.com/article/2375573/leadership-management/cios-consider-putting-a-price-tag-on-data.html
2) https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards
3) https://hbr.org/2016/12/breaking-down-data-silos

26. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 26
Changing the mindset …
From data hidden in silos … … to data democratization
F indable
Unique and globally consistent identifier
Metadata description
A ccessible
(meta)data are retrievable standardized
communications protocol
I nteroperable
formal, accessible, shared language for
representation, use of vocabularies
R eusable
data usage license, detailed provenance
domain-relevant community standards
In the digital and data-driven enterprise, data should be
The FAIR Guiding Principles for scientific data management and stewardship
https://www.nature.com/articles/sdata201618

27. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 27
… defining data semantics and make data FAIR
Good practices:
• Data catalogs & business glossaries
• Metadata management - « data about data »
• Semantic integration
Vendor
Order
Customer
Product Business Object Model
Conceptual Models
Customer
Canonical
Models
Physical Models
Product
Customer Prospect Account
Global Customer ID Global Customer ID Global Customer ID
- Account ID Account ID
Customer Name Name Name
ERP CRM MDM HR CMS …
Logical /
Physical Model
Example – Corporate Data League Wiki

28. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 28
… open up data, share and collaborate
Example: Corporate Data League Wiki
https://www.corporate-data-league.ch/meta/
Corporate_Data_League
Example – Open Data @ SBB (https://data.sbb.ch/)
Data in the hands of a few data experts
can be powerful, but data at the
fingertips of many is truly
transformational
https://www.forbes.com/sites/brentdykes/2017/03/09/why-companies-must-close-the-
data-literacy-divide/#3f35f92f369d

29. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 29
Recommendations for managing data as a strategic resource
“Only 3% of companies’ data meets basic quality
standards.”
Harvard Business Review, September 2017
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
“80% of the work involved (in advanced data analytics) is
acquiring and preparing data.”
Harvard Business Review, December 2016
Democratize data and support data citizens
à Data-sharing culture, FAIR data and applications
Measure and improve data quality
à Data governance, SMART data management
Assess the business value and impact of data
à Data valuation

30. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 30
Agenda
1. The changing role of data – data as a strategic resource
2. Real-world challenges in the digital and data-driven enterprise
3. Conclusion

31. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 31
Effective data management is a foundation of the digital and
data-driven enterprise
“Only 3% of companies’ data meets basic quality
standards.”
Harvard Business Review, September 2017
“It's frustrating that companies have a better sense of the
value of their office furniture than their information assets.”
Douglas Laney, Technology Analyst at Gartner
“80% of the work involved (in advanced data analytics) is
acquiring and preparing data.”
Harvard Business Review, December 2016
Understand and assess the business value of data
à Data valuation
Measure and improve data quality
à Data governance, SMART data management
Democratize data and support data citizens
à Data-sharing culture, FAIR data and applications

32. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 32
Data management is a journey –
Think big, start small & monitor progress!
GOALS ENABLERS
DATA
STRATEGY
PEOPLE, ROLES &
RESPONSIBILITIES
PROCESSES &
METHODS
DATA
LIFECYCLE
DATA
APPLICATIONS
DATA
ARCHITECTURE
PERFORMANCE
MANAGEMENT
BUSINESS
CAPABILITIES
DATA
MANAGEMENT
CAPABILITIES
RESULTS
BUSINESS
VALUE
DATA
EXCELLENCE
The CDQ Data Excellence Model https://cc-cdq.ch/data-excellence-model

33. Competence Center Corporate Data Quality (CC CDQ) | 2017 | 33
Questions?
christine.legner@unil.ch
Professor of Information Systems &
Academic Director Competence Center Corporate Data
Quality (CC CDQ)
HEC Lausanne
Prof. Dr. Christine Legner
Tel.: +41 76 3382782
Competence Center Corporate Data Quality (CC CDQ)
https://cc-cdq.ch/
The CDQ Data Excellence Model
https://cc-cdq.ch/data-excellence-model
HEC Research Blog – Effective Data Management in a
Digitally Driven World
http://wp.unil.ch/hecimpact/effective-data-management-in-a-
digitally-driven-world/