Depending on the size of the company and the complexity of the operations the budget reporting may vary from simply adding a column that illustrates if the variance was positive or negative or using graphs so that the relevant parties can get a better picture of what happened. Using BI for budgeting can actually bring many benefits to the table through its reporting, automation, and visualization capabilities.
The first step is to identify the sources for the data that feed the budget: be it a database, the output files generated by the company’s ERP, or Excel spreadsheets. Will that data require any sort of transformation, enrichment, or validation? Define how and where will that “cleansed” data be stored, so that it can be loaded and analyzed by the BI application of your choosing.
The application should provide the ability to aggregate data, being able to implement driver-based forecasting, user-defined reports shouldn’t require coding knowledge, dashboard creation, and automatic updating of the different dimensions and measures when a file with new information is uploaded.
The desired goals of implementing a business intelligence process for budgeting should be:
- Making it easy for the required personnel to access and see the information (avoiding the bureaucracy of email requests for X report and incurring in delays in decision-making), not only the raw data but the generated dashboards.
- Facilitating benchmarking and trend analysis through visualization. It grabs people’s attention, it’s easier to see what happened, comparing, and even getting an image of where you’re heading.
- Incrementing the efficiency around gathering and loading up the required figures to present the corresponding budget. The more automated, the better for everyone.
- Smart resource management. Taking advantage of the best opportunities, through a BI application it’s easier to notice and understand patterns in what has happened and carry on “what-if” scenarios with a few clicks.
As we explored on the first post there are some areas where business intelligence and analytics overlap, so it’s no surprise that a solution in this area will also help through its collection, analytical, reporting, and visualization capabilities. Once the process around data consolidation and cleaning is done, meaning it’s ready to be analyzed the company can work on studying past trends to work on measures such as predictive modeling. Unlike business intelligence, business analytics has a heavier reliance on past data, which means that before implementation the company requires to have years of data in the database. The greater number of periods stored, the stronger the insights that can be generated.
Business analytics can be used on both the integrated data and the data being gathered; for example, for the costs and drivers. Building models for the mentioned elements will also need more than a couple of years of past data, not just the totals presented on the budgets. Models on particular drivers and costs fed with more than two years of data give way to more reliable “what-if” scenarios that can be presented to upper management with the reassurance that they were created from facts gathered from the company’s databases.
Budgeting shouldn’t be a process that takes over a month to be completed, it should be dynamic, being able to change and accommodate to the changing operations. Budgets should be a measure of control and planning, not an inflexible restraint.