The logistics approach to data in the metadata driven company
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)
