Edgar de Wit
In the fast-paced world of modern business, accurate and dependable financial data is crucial. Whether your organization is big or small, poor data quality can have significant consequences.
This article will explore the importance of reliable financial data quality and some of the risks that can arise when data quality is inadequate.
Financial data is the lifeblood of any business. It provides the information that business leaders need to make informed decisions about everything from budgets and investments to pricing and marketing strategies.
There are several reasons why data quality is so important. I will mention the 4 most important ones.
Financial controllers need data that is up-to-date and timely to make informed decisions, respond to changing market conditions, and adjust business strategies.
It is, therefore, important that the data in reports can be easily updated.
Financial controllers are responsible for providing compliance with various regulatory requirements.
You must therefore ensure that you can always trace back to where your information comes from and why a data item is classified in such a way.
Financial controllers use data to create forecasts and budgets, which requires accurate and reliable data.
The person who makes the forecast must be able to explain what his forecast is based on. This can be current contracts and agreements or trends in his business environment.
And the last thing we often encounter is a lack of consistency.
Consistent and standardized data helps to ensure that financial reports are accurate and reliable, which is essential for building credibility with stakeholders.
Reports that look different every month, the data collection process has a different routine each time, no use of checklists for month-end closings, and various uses of reporting labels.
All these things undermine the reliability of your reporting.
A study by Harvard Business Review (HBR) estimated that companies in the United States spend about half of their time dealing with data problems, and over 3 trillion dollars on bad data each year.
A lack of good data costs decision-makers, managers, knowledge workers, data scientists, and other professionals so much money because they have to accommodate it in their everyday work. But this is costly and time-consuming.
In the face of a deadline, many individuals simply make corrections themselves to finish the task at hand, even though the data they need has plenty of errors. They don't think to reach out to the data creator, explain their requirements, and help eliminate root causes.
This research is from 2016, but unfortunately, it is still relevant.
When we talk to organizations that are looking for a solution, we often hear that they have the following problems.
The most common errors here are formula errors in spreadsheet reports, duplicate entries, incomplete or missing data, and messy chart of accounts .
For example, let's say you're tracking how many employees you have in your company. If you accidentally input the same employee twice, your total number of employees will be artificially inflated.
By connecting or grouping multiple sources together, productivity is lost. These manual actions also ensure that every change involves a lot of rework and therefore higher costs.
We often see self-made budget forms in spreadsheets, which are normally intended for the budget holder. If these are not formatted properly or do not have data validation rules included, they can lead to a lot of mismatched data.
This then puts a lot of unnecessary pressure on the financial controller to go back and forth with the budget holder and find out where the problem is and delay issues even further.
If you recognize these problems within your organization, I recommend the following steps to solve them.
The data validation would help you ensure that the data imported into your reports are correct, according to your own rules and logic.
This would help to minimize errors and ensure that the data is accurate and reliable.
By automating the data import process, you save a lot of time and reduce the risk of errors. This would allow you to focus more on analysis and understanding, rather than spending time manually entering and reconciling data.
Having access to the latest information on time is crucial for any organization to make informed decisions. By automating your data retrieval, you keep up-to-date information.
For example, our platform XLReporting can do this with 40+ systems, including Xero, Quickbooks, Exact Online, and Twinfield.
The ability to define user roles and permissions gives you greater control over who can see and do what in the system.
This would help to ensure that sensitive financial data is only accessible to authorized users, reducing the risk of misinterpretations.
By combining different sources of data, you get a better understanding of the situation. It is important to be able to make the connection between the outcome and the details.
You want to use different visualizations for this in your analysis work.
From our experience, and seen at many companies, these steps can no longer be done without technical solution(s).
The solution must be able to connect, verify and check your data. And then you want to visualize it to whoever is allowed to see it.
Many solutions on the market can take over one or two of these steps for you. This does mean that you have to pay extra setup, implementation, and license costs.
That is why we developed XLReporting with which you can import, visualize and forecast data. All-in-one solution with full authorization and audit trial.
In conclusion, good financial data quality is crucial for any business, regardless of size or industry.
By using solutions, just like XLReporting, to automate financial processes, validate data, and gain insights, businesses can improve financial data quality, reduce errors, and make better-informed decisions about their operations and future strategies.← Back to home