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Unlocking workflow automation: Relational data’s vital role
Here at Autologyx we understand the importance of relational data structures when designing and building technology solutions for your business. In part one of this three-part blog series, we delve into the details.

So, what’s the status quo? Most businesses rely on technology that offers a flat file type of data management. Think of your business data as a massive spreadsheet. Each row represents a different entity whether it be client or project or matter, and each column holds a specific type of information, like name, address, or history narrative.

This setup works fine for small operations, but as your business grows, managing this giant spreadsheet becomes a headache, with an ever-increasing number of columns, subject areas, divisions and business units. Whilst it is structured data, it doesn’t offer flexibility and scalability when thinking about business process automation.

Now, imagine a network of connected spreadsheets instead. You have one for clients, another for projects or matters, and another for history narratives. Each spreadsheet is linked through unique identifiers, like client IDs. This relational setup makes finding and organising data a breeze.

Why does this matter for automating business processes?


With relational databases, you can fetch and manage data much faster. For instance, if you need all the matters for a specific client you simply follow the link from the client to their matters.


Keeping data in separate but linked spreadsheets reduces the chance of errors. With flat-file storage, you might accidentally enter customer info twice or mess up copying it around. Relational structures store data just once, minimising mistakes. Or with a large amount of data points on one record you might accidentally select the wrong piece of data.


You can add, remove, or change data without disrupting other parts of the database. In contrast, every tweak in a flat-file could affect many rows and columns, risking errors.


As your business grows, relational databases handle larger data volumes gracefully. A giant spreadsheet, on the other hand, quickly becomes unwieldy.

In essence, relational data structures streamline data management, making it more efficient, accurate, flexible, and scalable. And since business process automation hinges on data, these benefits are key.

By combining a relational data structure and the matter data with our workflow engine, you can build real-time processes that use this data to organise work, track obligations and maximise client outcomes.   

Keep an eye out for the second post in this blog series.