What is the Decision Data Model?

The Decision Data Model (DDM) is the standard data input schema for Supply Chain Design & Planning. Downstream modeling processes, including Model Building recipes and demand modeling, require data in a standard format to perform their transformation workflows. Introducing the DDM has helped us build a cloud storage schema from which we can provide current and new modeling and analysis processes.

The DDM is designed to contain data relevant to supply chain activities, such as products, locations, historical shipments, and orders. It's the source of truth for supply chain data. All modelers can pull from this common data set as they build models using the Model Building feature. The DDM will become the data foundation to support many different purpose-built models.

The DDM supports data over a greater horizon period and at a more granular level than Supply Chain models. Current supply chain modelers might need to shift their mindset as they look to bring data into their DDM. In the past, modelers focused on gathering and preparing data at the level they needed for modeling. Model data often is prepared at some level of aggregation. With the DDM, we want modelers to bring their data in at a lower level than they would use in modeling. The DDM often contains transactional level data, such as SKUs, sites, customers, and historical shipment transactions. Using Model Building, modelers can quickly and flexibly aggregate this data as they build supply chain models.

DDM provides the following additional benefits:

• Centralized data for all users — The DDM provides an extendable data model that shortens the data preparation life cycle. It ensures that the data is in a central location, with a consistent structure and format, so it can be utilized across a wide array of use cases by different users.

• Speed of implementation — Once a DDM is initially populated for your first use case, you can leverage the data for all models moving forward, dramatically reducing implementation times for additional use cases.

• Supported and purpose-built asset — The DDM has regularly released schema upgrades targeted at improving performance and adding to the base schema to support additional use cases. It's built in a scalable way and can handle the storage of large data sets.

• Enables model building processes — Quickly generate new models with varying levels of aggregation and scope without having to fully prepare and cleanse data each time.

• Prepare for future Coupa innovation — Many of Coupa's future roadmap items are being developed around the DDM data foundation, including data validation, visualization, enrichment tools, AI prescriptions, demand forecasting, and suite synergies with other Coupa products. Investments in DDM prepare data for expanded value in the future.