Research shows that the amount of business data, across all industries, doubles every 1.2 years. Lots of data is generally a good thing. It can help companies digitize and automate their supply chains and gauge supply and demand effectively. Too much data, though, can be a problem, especially when it comes from multiple systems. Different systems often can mean different data models and different master data. For example, a reference to a “Customer” in one system may mean something different than it does in another system. Or the systems may use different labels to refer to customers, making the data difficult to reconcile. This is why a single data model is better for your supply chain and, particularly, demand signaling.
When does too much data become a problem?
For supply chain executives, big data provides them with valuable insights into their business operations, such as inventory optimization, demand planning, and forecasting. Good-quality, actionable data is also great for demand signaling. It notifies demand planners when there is a need for new materials or products, for example.
Management consulting company Deloitte identified the top four supply chain capabilities of big data:
- Demand forecasting
- Integrated business planning and supplier collaboration
- Risk analytics
Big data can only achieve these capabilities, however, if it comes from a unified data model. Too many systems with too much data can be confusing for supply chain executives and hinder demand signaling.
“Data is good, but it is not very useful until it is turned into information. Information is what we make decisions on. It can be quite hard to turn data into good information,” says operations consulting firm Stroud International. “How do we understand if a pattern is causal or only correlated? How do we filter out bad data points, outliers, and other non-representative data? “
What is the unified data model?
A unified data model utilizes one shared data store instead of multiple application systems. Users can still access data from multiple sources, but all of this information is in one centralized place, so there’s no need for several programs and servers.
“A unified data model [will] determine the methods, practices, and architectural patterns that correlate to the best outcomes in your organization,” says CA Technologies. “It will also force your institution to future-proof your data architecture by leveraging new technology data types and attributes.”
A unified data model comes from the growing need for simplicity. Today, too many systems have too many layers, which can limit the demand signaling process rather than optimize it.
Benefits of a unified data model
Although a unified data model can facilitate communication between companies and suppliers, its real value comes from its ability to unify internal teams — all those people in your business who are part of the supply chain management process. These include demand planners, supply planners, and supply chain executives. With this model, data becomes more visible and streamlines demand signaling.
A unified data model comes with few limitations. It’s easy to scale and optimizes data migration. Plus, it’s so much better than relying on several different data systems. In fact, it could be the best thing you do for demand signaling this year.
Sixty-two percent of companies say the supply chain is the most frequently prioritized function for digital investment, making it more important than product design and marketing. Still, most companies rely on old data models that don’t provide them with any value. Switching to a unified data model, however, could optimize supply chain functions like demand signaling significantly.