Data models aren’t typically high priority during system implementations, but they’re a key part of an organization’s long-term success. Creating data models and defining a reporting strategy lays the foundation for reliable and consistent reporting from the start. Without proper data now, there's nothing to measure performance against in the future.
A data model is a blueprint that defines how data is organized, stored, and related within a system. In the context of an ERP, it's essential to define the data model before implementation because it ensures that data is structured properly to support all business functions. This helps avoid data errors, ensures smooth integration between departments, and supports the efficient creation of actionable reports after go-live—something often overlooked. By having a well-defined data model, businesses can generate accurate, insightful reports quickly, enabling better decision-making and more effective use of the ERP system.
Let’s dive deeper into why it’s important to build data models early on during system implementations.
First, what is a data model? Data models exist to ensure reports meet business requirements and align with business goals. They serve as the basis for reporting for finances, operations, compliance, and other key areas.
Data models largely depend on the type of data organizations have and where they store it. However, the process for building a data model is largely the same across the board. It starts with documenting business processes, gathering requirements for reporting, and interviewing key stakeholders. It’s crucial to understand stakeholders’ short and long-term goals for the company as these findings will drive the reporting strategy. Key questions to address include:
These findings will drive how data is structured, stored, and accessed within a system. This feeds into the overall reporting strategy.
Data should be accessible, consistent, and reliable. Data isn’t useful if it’s incorrect or inaccessible when needed. This is why it should be set up correctly from the start. There are always unknowns in implementations, and building data models early minimizes those unknowns. Data models can also require additional resources that weren’t initially planned for, so for time and budgetary reasons, early is better.
Developing data models and a reporting strategy late in the process can result in significant rework if the system was originally design without proper consideration of reporting needs and business requirements. With every implementation, data models are revisited. There’s almost always a refinement after go-live as new processes develop, new customizations are added, people are learning how to use the system, and new information is discovered. Building a data model early helps ease the load when refinements come. These refinements are far different than starting from scratch as they’re usually smaller things—adding a new dimension to a report, changing user access, adding a new graph, etc.
At Trenegy, we help organizations define a reporting strategy and understand how to build a data model that aligns with business goals. For assistance building a reliable and consistent reporting strategy from the get-go, reach out to us at info@trenegy.com.
