The Spreadsheet Ceiling
Spreadsheets are the universal starting point for business data management — and for good reason. They are flexible, familiar, and require no setup. But as datasets grow in complexity (rather than just size), spreadsheets hit a ceiling that dedicated information management software is designed to break through.
Limitation 1: Flat Data Model
A spreadsheet is a grid of rows and columns. Each row is an independent record. Relationships between records — this person works for that company, this document relates to that entity, this asset is owned by that trust — must be represented through manual cross-references (lookup formulas, hyperlinks, "see tab X" notes). These cross-references are fragile, not queryable, and invisible in aggregate.
A SIMS uses a relational or graph data model where connections are first-class citizens. "Person A is a director of Company B" is not a note in a cell — it is a structured relationship that can be queried, visualized, and traversed. The system knows about the connection and can use it in searches, filters, and graph views.
Limitation 2: No Relationship Visualization
A spreadsheet can show you a list of people and a list of companies, but it cannot show you the web of connections between them. To understand how entities relate, you must mentally reconstruct the graph from cell references — feasible for 20 entities, impossible for 200.
A SIMS includes interactive relationship graph visualization. The connections you have recorded become visible. Unexplored paths become apparent. The graph reveals what the spreadsheet obscures: the structure of the network.
Limitation 3: Document-Entity Disconnection
Spreadsheets can contain hyperlinks to documents, but those links are just text in a cell. There is no structured relationship between the document and the entity it concerns. Finding all documents related to a specific entity requires searching through cells. Finding all entities referenced in a specific document is impossible without opening it.
A SIMS links documents to entities as structured relationships. The contract is linked to the signing parties. The board minute is linked to the company and the directors. Documents are findable through the entities they relate to, and entities show all documents associated with them.
Limitation 4: Multi-User Consistency
Shared spreadsheets (Google Sheets, Excel Online) allow multiple users but provide no structural consistency. User A puts the contact's phone number in column H; User B puts it in column J. User C adds a new person but does not link them to their company because they do not know which column to use. Over time, the spreadsheet accumulates inconsistencies that make searching, filtering, and reporting unreliable.
A SIMS provides a defined data model. Every person record has the same fields. Every relationship is created the same way. The structure is enforced by the software, not by team discipline — which means it survives team turnover and inconsistent usage patterns.
Limitation 5: Query and Reporting
Spreadsheets support filtering and pivot tables, but complex queries — "show me all entities in Jurisdiction X that have directors who also appear as directors of entities in Jurisdiction Y" — require manual assembly across multiple sheets and are error-prone. A SIMS with a SQL database can answer such questions in seconds with a query.
The Migration Path
Moving from spreadsheets to a SIMS does not mean abandoning spreadsheets entirely. Spreadsheets remain useful for ad-hoc analysis, financial modeling, and data exchange. The SIMS becomes the system of record — the authoritative source for entity data, relationships, and document links. Spreadsheets become analysis tools that consume structured data from the SIMS, rather than the database itself.
ONS Data Terminal as the Spreadsheet Successor
ONS Data Terminal is designed to be the structured successor to the spreadsheet-based tracking that most organizations start with. It provides entity management, typed relationships, document linking, graph visualization, and timeline tracking. Data is stored in PostgreSQL — meaning you can still export to CSV for spreadsheet analysis when needed, but the authoritative copy lives in a structured, queryable, relationship-aware database.