By: Nora Burema-Bracho Surga (1) &  Zéger Nieuweboer (2) (1) Nora Burema-Bracho Surga works as an Information Expert for the Ministry of Infrastructure and Water Management, The Netherlands, Europe. Nora holds a bachelor degree (BA) in Modern Languages at the Universidad Metropolitana in Venuzuela and a master of science degree (MSc) in International Business at the Sheffield Hallam University in the United Kingdom. Linkedin profile: linkedin.com/in/nora-burema-bracho-4840a55 (2) Zéger Nieuweboer works as a Data Steward for the Ministry of Finance, The Netherlands, Europe. Zéger did his minor at the University of Illinois and holds a master of science degree (MSc) at the Wageningen University & Research in the Netherlands. He holds a professional degree in engineering (PDEng) at the Dutch School for Technological Design at Eindhoven Technical University. He is certified in planning and inventory management (CPIM) and data management (CDMP). Linkedin profile: linkedin.com/in/zegernieuweboer   Topics of Interest in this article: Data Stewardship, Case Study, Innovation Lab, Lessons learned. Abstract The investments in data tracking and tracing in a big data organization are needed for compliance to data ethics and GDPR. Data tracking and tracing starts with identification, the metadata of data. Data tracking and tracing is successful in a well-organized metadata driven company.   Introduction For years I worked within the consumer goods industry, with a main focus at marketing and logistics.  In the consumer good industry a goods tracking and tracing system is needed. Because of  government legislation and consumer risk management. Later, I made a career switch to a big data organization within the Dutch government. In this organization, we can learn from the goods tracking and tracing proces of the consumer goods industry.   Logistics It is obvious that data is needed to manage the physical goods flow. Inventory data, production data, sales data, customer data. In a data organization the main flow is not a physical flow but a data flow. The data needed to manage a data flow in an organizations is called business metadata. Tracking and tracing Data tracking is the downstream activity to determine the data products with a given origin. Data tracing is the upstream activity to determine the origin of a data product. Data lineage is a data process that roughly covers the data tracing and tracking form source to data warehouse. The downstream activity data tracking becomes more important in a GDPR compliance organization. To prevent deliveries of privacy sensitive data products which can conflict with data ethics.   Case Study Data Tracking and tracing In a government organization a discrepancy in use of the personal diversity data was detected. Some business information products contains personal diversity data which were not in compliance with data ethics and GDPR. The first upstream activity was data tracing: which data warehouse from which source contains this obsolete personal diversity data. An handicap was that data lineage was right in place for only the new data environment. With a data retention period of ten years older data stocks must be analyzed manually. The second downstream activity was data tracking. Which other business information product contain the obsolete personal diversity data. The metadata repository was of big help in the tracing search. In the historic metadata versions of the business information products it became clear where the diversity data was used and on which privacy detail.   Business metadata standard Data tracking and tracing is successful in a well-organized metadata driven organization. A new business metadata standard was developed in a big data organization of the Dutch government. To accelerate the development the learning and prototyping process was done in an innovation lab: “The Living Blue Lab”. The developed new business metadata standard consists of three levels: Business rules Business Information products Data building blocks The business rules represents the tax guidelines and internal framework agreements. The business information products represents the key performance indicators of the data company. The data building blocks are the connection between he business information products and the data flow. As part of the data building blocks, the data quality rules are described.   Simultaneously a business metadata work process was designed and implemented in the learning and prototyping innovation Lab. De business metadata process consists of three stages in the workflow: Define business metadata by information experts Review business metadata by business stakeholders Publish business metadata in metadata repository    Lessons learned The business metadata standard was tested in the learning and innovation lab “The Living Blue Lab”. Results from the use of the business metadata standard are: The business metadata standard and the use of the actual metadata repository are requiered for the compliance to the international data ethics standard GDPR. The business metadata standard improves the quality of the business information product by the validation with the business rules. The business metadata standard improves the quality of data by integrating data quality rules in the design process with data vendors and data customer The biggest lesson learned is that deployment of a successful lab solution into the harsh reality of the regular organization requires excellent transition leadership. Bases upon the results, the new business metadata standard is deployed in the organization. The new business metadata standard is the foundation of the standard data management solution.   Conclusion The investments in data tracking and tracing in a big data organization are needed for the compliance to data ethics and GDPR. Data tracking and tracing starts with identification, the metadata of data. Data tracking and tracing is successful in a well-organized metadata driven company.   Version: July 2021, The Netherlands This article is submitted for the:  Data Governance & Information Quality Conference (DGIQ) December 6-10, 2021, San Diego, Californië   Sources Data Stewardship Second Edition, David Plotkin. 2021, Elsevier Academic Press, ISBN 9780128221327 Supply Chain Logistics Management, Fifth Edition, Donald Bowersox, David Closs and M. Bixby Cooper. 2019, McGraw-Hill Education, ISBN 9781260547825 DAMA-DMBOK, Data Management Body of Knowledge, 2 ND Edition, DAMA International. 2017, Technics Publications, ISBN 9781634622349     ... View more

Introduction For years I worked in a marketing and logistics focused organizations with a physical goods flow. I made a career switch to a tax execution organization of the Dutch government. I wonder why an organization with a goods flow can deliver goods in days to their customers. On the other hand an organization with a primary data flow has severe problems to deliver results with less challenging customers’ requirements. Logistics approach to data It is obvious that business data is needed to manage the physical goods flow. Inventory data, production, sales data, customer data. In the tax execution organization the main flow is not a physical goods flow but a data flow. The data needed to manage a data flow in an organizations is called business metadata.   The organization with a primary data flow can learn from the logistics management of an organization with a goods flow. Two learning points are the use of data stocks and business metadata integration in a data supply chain. Data stock The point where a goods flow doesn’t flow is called a stock. Logistics management is focused to balance the stock with customer requirements and costs of stocks. To balance stock with customer requirements depends upon uncertainty in the supply chain. Stock costs consist of interest costs, stock room costs and obsolete costs. In a company with data flows an accumulation of data is called a data stock. In previous times data stocks were badly monitored. The costs of data stocks consist only computers costs of disk space. With the introduction of the GDPR legislation organization became aware of privacy risk costs of data stocks. Metadata integration Logistics focused organization have very sophisticated management information systems. The customer requirements are very demanding and can only be fulfilled with an integrated logistics management system. The business metadata systems of the tax execution organization are poor and fragmented. The data flow from origin to customer is not integrated in a single management information system. Therefore is it hard to track and trace data. Business metadata standard There isn’t an accepted business metadata standard in the fragmented data organization of the tax execution organization. A new business metadata standard was developed in the co-creating, learning and prototyping environment “The Living Blue Lab”. This new business metadata standard consists of three levels: 1) Business rules 2) Business Information products 3) Data building blocks The business rules represents the tax guidelines and internal framework agreements. The business information products represents the key performance indicators of the data flow. The data building blocks are the connection between he business information products and the data flow. The definition of the data quality rules are part of the data building blocks. In addition a business metadata work process was designed and implemented in the lab. The business metadata process consists of three stages in the workflow: 1) Define business metadata 2) Review business metadata 3) Publish business metadata Living Lab The business metadata standard and the business metadata process were tested in the lab environment of the Ministry of Finance. Results from “The Living Blue Lab” were:  a) The business metadata standard improves exchanging knowledge between vendor and customer by better communication. b) The business metadata standard improves the validation of business rules in the business information products c) The business metadata standard improves the quality of data by integrating data quality rules in the design process with data vendors and data customers Based upon these results, the new business metadata standard is deployed in the tax execution organization.  Conclusion The reason that organizations with a goods flow excel in the logistics performance towards customers is the excellent use of data. Therefore the phrase data driven can better applied to these organizations. Excellent busines metadata is needed to excel in an organization with a data supply chain. In this case the phrase metadata driven organization is more appropriate.   The author Zéger Nieuweboer holds a master of science (MSc) degree at the Wageningen University and a professional degree in engineering (PDEng) at the Dutch School for Technological Design at Eindhoven Technological University. He is certified in production and inventory management (CPIM) and data management professional (CDMP).   Submitted paper to the Data Governance & Information Quality Conference (DGIQ) of DAMA International, U.S.A. (750 words) ... View more

By: Zéger Nieuweboer, Data Steward, Dutch Tax and Customs Administration (DTCA), Apeldoorn (NL)   Abstract:  The overall objective of metadata management is to deliver reliable and unambiguous information. The acceptance of metadata by the business is essential for achieving this objective.  The experience of the Dutch Tax and Customs Administration (DTCA) shows that a very simple metadata model increases the business acceptance of metadata.   The Metadata pitfall   In 2012 the Dutch Government launched a metadata programme. The goal of the programme was to standardize definitions, in order to compare the performance of the eleven departments of the Dutch government. The metadata programme  and the metadata repository was based upon the Australian Metadata Standard (Alasem, 2009) At the annual Metadata Day in the Hague, the results of het metadata programme were shown to the business owners of the departments. During the several presentations of the Australian standard and the metadata repository the resistance of the audience grew. The business customers couldn’t recognize themselves in the technical presentations. Even some people in the audience felt that the ownership of definitions was “stolen” by the metadata programme team.   Obviously there was a big gap between the expectations of the business customers and the deliverables of the metadata programme team. Time for a different approach.   The Business metadata approach   In the last five years the Dutch Tax and Customs Administration paved the road for a better business acceptance of metadata.   Starting point was to set up a simple metadata model: 3- Business Rules Business rules describe the operations, definitions and constraints that apply to an organization. The business rules affects the definition of the delivered BI/Control Products. 2 –BI/Control Product Based upon the performance and/or control objectives  the metadata of the BI products and control products is described.  The operational rules describe in which way and with which selection the figures are presented. 1- Data Building Bricks The data building bricks, like LEGO bricks, are the link between the BI-Product and the data source. The data quality rules describe the availability and correctness of source data.   This three layer model is distributed as the Business Metadata standard.   The Metadata tool   The Dutch Tax and Customs Administration has chosen for  SAS Business Data Network (SAS BDN) as the standard metadata management tool. The general concept of the metadata tool SAS BDN aligns with the Business Metadata standard.  The administration of data quality rules and data monitoring is heavily supported by SAS BDN.  The administration of  business rules and their relation to delivered BI Products needs further development.   Keywords Metadata, Business, Tool   Acknowledgment I want to thank Diederik Dulfer, Architect at DTCA, for his comment at an earlier version of this article.   Bibliography Alasem, Abdurrahman, An overview of e-Government Metadata standards,  Strathclyde University (UK), 2009                                                   ... View more

The Netherlands Tax Authority is rolling out the German application SAP for their key processes.  The American application SAS is implementend for BI and Analyses of the key processes of the Netherlands Tax Authority. The connection between de process apllication SAP ECC and the BI application SAS is yet pragmatic, using extracted text files. In which way can the Netherlands Tax Authority professionalize the connection between SAP and SAS? Zéger Nieuweboer, Data Steward, 2015 ... View more