Managing Data as a Strategic Resource – Foundation of the Digital and Data-Driven Enterprise

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

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

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/

Managing Data as a Strategic Resource – Foundation of the Digital and Data-Driven Enterprise

Managing Data as a Strategic Resource – Foundation of the Digital and Data-Driven Enterprise

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