This is the introductory post in a 5-part series discussing the various features in Microsoft Dynamics 365 Finance Insights. Finance Insights proves invaluable by offering adaptable solutions for predicting cash flow, receivable payments, and generating budget proposals. It also lets you utilize machine learning templates to construct models using your data. In this blog, we will cover budget proposal capability in Finance Insights.
Companies invest significant time and resources in formulating their budgets. A considerable portion of this task is repetitive and doesn’t add much value, like collecting the data necessary for the budget.
The budget proposal functions in Dynamics 365 Finance Insights offer advantages like:
- Simplifying the collection of past data from actual figures or previous budgets for use in Dynamics 365 Finance budgeting.
- Allowing alterations to the budget based on varying periods or a mix of past budgets and actual results.
- Crafting an updated budget which can be detailed further, using valuable insights to input information that might not be reflected in past data.
- Presenting the budget proposal in a format known as a budget register document, which is straightforward to adjust, transfer, or use for routine reports in Dynamics 365 Finance.
Importing Sufficient Data for a Good Prediction
The accuracy of your predictions relies on having plenty of cleaned and consistent data spanning several years. Sometimes, data from three years might work, but having data from five to ten years is often better. If you don’t have 10 years of data in your system, think about cleaning up older data that’s not in the system and adding it as a past budget. When we say ‘cleaning data’, we mean making sure the data stays consistent, especially when there’s been changes like a company restructure, or when adding old data that was made before changes to the account charts or financial structures
The Budget Proposals tool utilizes historical information combined with your contributions to develop a machine learning model. This advice aims to enhance and guide the model’s outcomes. Machine learning is most effective when examining consistent data over extended periods. Ideally, it’s beneficial to have 10 years of consistent data with unchanged account charts and dimensions. The broader the data a model accesses, the more refined its performance. These models harness past data and advanced calculations to predict potential outcomes, making budget creation more efficient. However, achieving top-tier budgets results from active managerial involvement in refining the suggested figures.
Certain financial activities, like routine payroll expenses, are straightforward to forecast compared to more variable ones. It’s crucial to compare the model’s predictions against actual figures. This can be done using both the actual vs. budget analysis and detailed financial reports that, when adjusted, can provide in-depth monthly variance insights. By producing forecasts for past activities, you can assess its precision against the actual data from the same time frame.
Proving out with actuals versus budget inquiry
The ‘Actual vs Budget’ tool offers a detailed comparison between real numbers and the suggested budget. Within its inquiry parameters, you can define the date range and budget model, ensuring you select ‘Draft’ as the budget entry status. The subsequent result gives an annual overview, which includes actuals, the proposed budget, variances, and their respective percentages. For more detailed insights, the ‘Period Balance’ page offers a breakdown of variances for each account over specific periods.
Proving out with financial reporting
For those using financial reports for evaluation, the ‘Actual vs Budget – Default financial’ report provides a comprehensive comparison between real figures and the budget proposal. Typically presenting an annual overview, users can adjust it to depict monthly data. To correlate with the proposal model, navigate to ‘Report Options’ and then select from the ‘Scenarios’ dropdown. This action will realign the report to your chosen model. Furthermore, when a 12-month report is exported to Excel, adding graphics or sparklines offers a visual representation, highlighting data trends.
If you are interested in learning more about how to use Microsoft Dynamics 365 Finance Insights, contact us here to find out how we can help you grow your business. You can also email us at email@example.com or call (312) 345-8817.