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Rethinking Data Management - Data Sharing in Business Ecosystem
The document discusses data sharing in business ecosystems. It provides an example of a trusted network called the CDQ Data Sharing Community that facilitates two levels of data sharing between companies. The first level involves sharing data knowledge such as reference data and semantics. The second level involves sharing validated data assets. Benefits of data sharing include improved data quality, reduced data maintenance efforts, lower risks, and increased trust in shared data. The document also outlines lessons learned and opportunities for further expanding data sharing between organizations.
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Rethinking Data Management - Data Sharing in Business Ecosystem
- 1. 0 Rethinking Data Management Data Sharingin Business Ecosystems Prof. Dr. Christine Legner Competence Center Corporate Data Quality (CC CDQ) cc-cdq.ch / meta.cdq.ch
- 2. 1 The sharing economyis in full swing …. only data assets are still managed in silos! Why not start with data sharing? Asset sharing reduces costs, improves utilization and sustainability YardClub
- 3. 2 1 Background andmotivation 2 Example: Business partner data sharing in trusted networks 3 Lessons learned and outlook Agenda
- 4. 3 Competence Center CorporateData Quality (CC CDQ) The CC CDQ is a research consortium and expert community in data management 2006 Foundation +35 Members +60 CC CDQ Workshops 14 PhD Graduates +1500 Contacts within CDQ community CC CDQ co-creates methods, tools and solutions for managing data assets NB: Overview comprises both current and former partner companies
- 5. 4 Companies face majorchallenges in managing their data assets Data silos Regulations impose transparency and traceability, but most companies deal with them as one-time efforts. Company A Company B Data is stored and maintained in silos – with significant overlaps and gaps. Data quality Despite awareness of data quality issues, data maintenance is mostly reactive, with high manual efforts. Data is a strategic asset in the data economy. But: Existing data management approaches do not scale! Compliance
- 6. 5 1 Background andmotivation 2 Example: Business partner data sharing in a trusted network 3 Lessons learned and outlook Agenda
- 7. 6 The CDQ DataSharing Community is a trusted network of companies that manage business partner data collaboratively Exam ple Exam ple https://meta.cdq.ch
- 8. 7 Shared data knowledge Shared data assets Twolevels of data sharing in the CDQ Data Sharing Community 2 1 à Data validation, curation and enrichment à Shared data maintenance (and beyond)
- 9. 8 Two levels ofdata sharing in the CDQ Data Sharing Community Level 1: Shared data knowledge is the foundation Shared data knowledge Linking reference data: • Open / public data Defining shared semantics: • Semantic data model • Business rules à Data validation, curation and enrichment Ontology & linked data Data quality ⬈ Accuracy Timeliness Consistency Completeness 1
- 10. 9 Business Partner Address Country Germany An organization which has…. Has address Hascountry Is a A physical location that… Has definition Legal form Has legal form Aktien- gesell- schaft Is a Has definition Used in Rule legal form allowed … … Has constraint Rule legal form valid … Has constraint Rule country valid … Has constraint Name Has name Data model and semantics are collaboratively defined Data model and business rules represented in a single ontology Exam ple
- 11. 10 Business rules andreference data are collaboratively maintained Exam pleA semantic wiki acts as shared repository Comprehensive rule base, with >1200 data quality rules (documented in business terms and executable) External reference data, including 258 countries, 2224 legal forms, >70 externally managed data sources
- 12. 11 Validation services checkquality of business partner records according to business rules Some business partners (e.g. hospitals) do not need a legal form (category is assigned automatically) Missing legal form Known legal form, but not valid for Germany c/o information in name For Germany, either a VAT number or an “old” tax number is ok Exam ple
- 13. 12 Data sharing platform– Level 1: shared data knowledge Workflows and platform How does it work? 1. You provide your data as-is, mapping is managed in CDQ Cloud 2. You can check your own data for errors or duplicates and compare to reference data 3. You receive a weekly DQ summary report 4. You can select which records to update, and/or subscribe to automatic updates 5. Updates in your local systems are performed through API, connector or your data management team Reference Data Pool regular updates CDQ Data Sharing Cloud Open Data/ Public Sources Company A CDQ Web Apps regular data quality checks check for updates reports alerts Your Data Mgt. Team Emails, Push Info Exchange API weekly report Business Partner Data Data Mirror for Company A Exam ple
- 14. 13 Risks ⬊, trust⬈ Maintenance efforts ⬊ Data sharing in the CDQ Data Sharing Community From shared data knowledge to peer-based sharing of validated data Shared data knowledge Linking reference data: • Open / public data Defining shared semantics: • Semantic data model • Business rules à Data validation, curation and enrichment Ontology & linked data Data quality ⬈ Accuracy Timeliness Consistency Completeness Shared data assets Fraud detection, risk mgmt. • Whitelists, trust scores Peer-based sharing of validated data: • Cross-company workflows • Integration into IT systems à Shared data maintenance (and beyond) Auto- mation & Machine learning 2 1
- 15. 14 Data sharing platform– Level 2: shared data assets Workflows and platform How does it work? 1. You provide your data as-is, mapping is managed in CDQ Cloud 2. You can check your own data for errors or duplicates and compare to reference data 3. You receive a weekly DQ summary report 4. You can select which records to update, and/or subscribe to automatic updates 5. Updates in your local systems are performed through API, connector or your data management team 6. Sharing with peers can be decoupled from sync‘ing with public sources; managed in reference data pool Reference Data Pool regular updates Open Data/ Public Sources Company A CDQ Web Apps regular data quality checks check for updates reports alerts Your Data Mgt. Team Emails, Push Info Exchange API weekly report Business Partner Data Data Mirror for Company A Sharing Community CDQ Data Sharing Cloud Community Data Pool changes
- 16. 15 Data sharing fordetecting fraud and increasing trust Whitelist and trust score Companies document known fraud cases in a CDL database and use this database to check if a given bank account was used for a known attack or if a given business partner was affected by a known attack Whitelist approach Trust score Exam ple
- 17. 16 Risks ⬊, trust⬈ Maintenance efforts ⬊ Two levels of data sharing in the CDQ Data Sharing Community From shared data knowledge to peer-based sharing of validated data Shared data knowledge Linking reference data: • Open / public data Defining shared semantics: • Semantic data model • Business rules à Data validation, curation and enrichment Ontology & linked data Data quality ⬈ Accuracy Timeliness Consistency Completeness Shared data assets Fraud detection, risk mgmt. • Whitelists, trust scores Peer-based sharing of validated data: • Cross-company workflows • Integration into IT systems à Shared data maintenance efforts (and beyond) Auto- mation & Machine learning 2 1
- 18. 17 1 Background andmotivation 2 Example: Business partner data sharing in trusted networks 3 Lessons learned and outlook Agenda
- 19. 18 Lessons learned fromsharing data • Does data sharing really work? Yes, it works! – But: it requires trusted networks of (mature) companies and collaborative efforts for data, process and platform design. • What are the benefits of data sharing? Shared data knowledge à higher data quality. Shared data assets à lower data maintenance efforts, reduced risks, trust in data. • Are there further opportunities for data sharing? Yes, there are many - and we are working on further data sharing ideas: – Material (spare parts) – Medical licenses in pharmaceutical industry – Sustainability reporting (certificates, labels, claims) – …
- 20. 19 Food for thoughtand outlook My questions to you 1. Do you know how much time and effort you spend for managing your most critical data assets? 2. How do you get rid of your data silos and leverage synergies within and outside of your company? 3. Where can you make use of external (open) data and data sharing with peers? Data sharing may become reality for you earlier than you expect https://www.government.nl/documents/reports/2019/02/01/ dutch-vision-on-data-sharing-between-businesses https://skywise.airbus.com/ https://www.sophiagenetics.com
- 21. 20 Contact christine.legner@unil.ch Academic Director Competence CenterCorporate Data Quality (CC CDQ) Prof. Dr. Christine Legner CDQ Data Sharing Community Wiki: https://meta.cdq.ch Competence Center Corporate Data Quality (CC CDQ) https://www.cc-cdq.ch/ Executive Education CDQ Academy for data managers with University of St. Gallen https://www.cdq.ch/training CAS Data Science & Management with EPFL / Swiss Data Science Center and University of Lausanne https://execed.unil.ch/certificat-data