Your financial data is the backbone of your business. It's what you use to make decisions about how to grow and improve your business, how to allocate resources and funds, and how to evaluate performance.
If your financial data isn't clean, consistent, accurate, and timely, it can lead to missed deadlines, difficulty communicating with upper-level executives, and an inability to make informed strategic decisions about your business.
Today, we'll look at the difference between clean and messy financial data and how to fix it so your business can thrive.
What qualifies clean data?
Clean data is accurate, relevant, and timely. Heard this before?
That’s because these are accounting basics.
Messy data is just as easy to define.
Messy financial data is incorrect, has mistakes, is disorganized, is sorted inconsistently, and can even be late or absent.
So why is this important?
Because financial data is used to measure current value and past performance, messy data can make it hard to form an accurate picture of where you are now, which makes accurately planning for the future even harder.
So how can you successfully manage your financial data?
Organizing your data, establishing a framework and structure, and defining your processes and workflows will give you the control you need to effectively manage your data on a consistent basis.
We’ve broken this process into 3 main stages: organization, structure, and control. Let’s look at each more closely.
To organize your financial data, we recommend you follow these 5 simple steps:
Keeping your financial data under control is not a set-it-and-forget-it process.
To build and maintain clean data processes year after year, establish a new cycle of management and accounting processes that revolve around consistency, accuracy, and timeliness.
Here’s what that means.
After organizing your data and adding structure, you’ll begin to gain the control you’ve needed over the financial data.
So what next?
Now that everyone in your company is on board with consistent, accurate, and timely processes, you can begin to add prevention steps.
Things like automation and detection measures (like alerts and reviews) can help you stay on top of your data, keeping it clean for the long term.
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