One of the major advancements in predictive analytics in corporate finance has been the development of predictive analytics and predictive budgeting to create financial reports.
While not entirely new, modern software applications have allowed for more robust data aggregation and analytics resulting in an ability to utilize historical data like never before. The end product of this data is a more streamlined and automated budgeting process.
As finance departments grapple with balancing cost against providing value there has been a paradigm shift in the way these departments are being utilized. This is largely the result of applications that have removed the burden of manual and routine data entry from finance professionals and automated it.
This not only saves time but allows these professionals to shift their focus from data management to data analytics and strategy development.
One activity that historically has been time-consuming for most finance departments is the budget process. The end product of the budgeting process is a financial roadmap of sorts that defines how the firm will allocate resources based on its goals and strategies.
However, one limitation of the budget is that it sometimes lacks sufficient foresight. Historical trends and patterns are sometimes ignored or not identified, leading to ineffective or inefficient resource allocation.
What Is Predictive Budgeting?
Predictive budgeting is a form of budget forecasting that involves the use of historical data and artificial intelligence to identify recurring trends and patterns in historical data sets.
This data is then incorporated into a budget to provide a predictive model of how to allocate resources best by utilizing the historical trends and patterns the system has identified. The end product is a budget that is driven by statistical analysis of past business trends, results, and performance.
While forecasting is always part of the budgeting process, a budget forecast has typically only used the values of the budget as its inputs. The other assumptions in a budget forecast are typically static and might or might not incorporate historical data. Modern ERP and CPM systems have created large amounts of transactional, operational, and performance data. Predictive budgeting utilizes technology to aggregate and analyzes this data continuously, resulting in an evolving and learning predictive data set for future performance.
Benefits Of Using Predictive Budgeting
While predictive modeling has been around for centuries, predictive budgeting as a practice is a modern development in the field of corporate finance. It is often used as a means to oversee and refine the final budget that will be used.
This is because predictive analytics is backward-looking and therefore has a difficult time identifying future trends and analyses that have not materialized.
Management typically uses their experience and sometimes intuition about the business environments they operate in to identify goals and set targets. Predictive budgeting might not necessarily be able to identify these market conditions if there is no data set for the system to reference.
However, there are a host of benefits to implementing predictive budgeting in any finance department. Among the benefits are a few areas where predictive budgeting really shines.
Better Cash Flow Forecasting And Modeling
The process of scrutinizing and analyzing historical trends for patterns that impact cash flow is an immensely powerful benefit of predictive budgeting. Liquidity remains a top concern for almost every business and regulatory oversight on certain industries requires liquidity ratios to be maintained to avoid punitive damages.
Helps To Identify Business Drivers
The patterns that emerge as a result of data analytics that predictive budgeting performs provides insight into the business activities that, when invested into, yield the most optimal results. Understanding your business-critical drivers is incredibly important for navigating the future.
Highlights Areas That Require Attention
The predictive budgeting process brings to light any deficiencies in your budget that could result in material losses. Having this insight affords you the ability to make necessary changes proactively rather than reactively. Being proactive with your budget is more likely to result in success than having to be reactive, which typically is aimed at damage control over the execution of goals.
Requirements For The Successful Implementation Of Predictive Budgeting
As with any major technology implementation, there are some prerequisite requirements that need to be met to ensure successful implementation. Here are four critical things to have in place before implementing predictive budgeting software.
Centralized Data Sets
Aggregating disparate data sets would be ideal to prepare for predictive budgeting. If it is not possible to aggregate all of your data sets, then you will require access to the various sources of data.
Clean And Accurate Data Sets
The saying “bad data in, bad data out” has plenty of merits. The predictive budgeting process relies on historical data sets to build the forecast model. This means any bad data will be inherently incorporated into the assumptions created during the analysis of information. You can also check this with your spend analysis.
Adequate Technology To Access Data
Because there is such a heavy reliance on data, it is wise to get the right technology in place to access the information you need, when you need it. This will allow the predictive budgeting system to utilize data and query information quickly and with ease.
As with all technology implementations, it is wise to place the right people in the right roles. Predictive budgeting requires heavy analytics, and the individuals who implement the system and develop the advanced models need to be well-versed and specialized. If not, it would be akin to having the most advanced race car, but no driver or team to maintain it.
Predictive Budgeting Best Practices
Before jumping into predictive budgeting be sure to take the time to analyze the implementation and create a realistic plan of action. It is important to break the process into smaller, easier-to-execute projects, to ensure effective use of the system. Begin with short-term forecasts and then build on those short-term plans, to create larger forecasts.
Take time to focus on the most relevant business drivers that can provide efficiencies and yield noticeable results. Keep your team up-to-speed, and supply them with adequate access to training and development to ensure the caliber of individuals supplying the system with information are as capable as the system.
Finally, predictive budgeting is a powerful resource when performing scenario modeling. Use predictive budgeting regularly for scenario modeling to gain valuable insight into the various possible outcomes. This is one of the most powerful aspects of predictive budgeting and not using it in this way is a gross oversight.
Using Datarails, a Budgeting and Forecasting Solution
Datarails enhances spreadsheets with real-time data and integrates fragmented workbooks and data sources into one centralized location. This allows users to work in the comfort of Microsoft Excel with the support of a much more sophisticated data management system at their disposal.
Every FP&A team knows how tedious building a budget and forecast can be. Integrating cash flow forecasts with real-time data and up-to-date budgets is a powerful tool that makes forecasting cash easier, more efficient, and shifts the focus to cash and financial analytics.
Regardless of the budgeting approach your organization adopts, it requires big data to ensure accuracy, timely execution, and of course, monitoring.
Datarails, a leading cloud FP&A software, is an enhanced data management tool that can help your team create and monitor cash flow against budgets faster and more accurately than ever before.