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Rethinking Budgeting, Technology during the Pandemic for U.S. Agencies

The COVID-19 pandemic caused a total of about US$300 billion through FY 2022 in state and local revenue losses. While this number is lower than previous dire predictions, pandemic-related costs continue to rise in many localities, resulting in budget deficits that may force states to cut back on other services, lay off workers or raise taxes.

The economic recession resulting from the pandemic accelerated government procurement of cloud technologies to deliver remote services to citizens that could not come to city hall or for workers who needed to work from home. As a result, public-sector leaders have been forced to reevaluate their digital transformation strategies, including budgeting.

The governments that had already transitioned from Excel-based to cloud-based budgeting were most efficient and strategic throughout this turbulent time. Digital budgeting solutions enable collaboration in a way that analogue systems never could, with interactive features that allow teams to communicate more effectively. With budgeting data accessible from anywhere, administrators and officials could analyse historic data and adapt current budgets as the needs of their constituents evolved.

The COVID-19 pandemic accelerated digital transformation in the public sector. As governments review their past performance and evaluate budgeting and technology options for the future, there are four key factors they should consider.

  • Greater emphasis on strategic planning

Strategic planning is central to local governments, helping them engage the community and distil their needs and expectations. Leaders can then further refine those needs and expectations into a mission statement, a vision statement, goals and objectives. Modern budgeting and performance management software is essential to strategic planning because it allows governments to not just tie a budget to goals and objectives in a more automated fashion, but also to report on actual expenses and performance achievements toward those goals. For example, cloud-based budgeting software can create a virtual chart of accounts that allows leaders to tie allocations of line items to strategic objectives.

  • Prioritising forecast budgeting

Technology enables governments to automatically recalibrate their forecasts and address these pressing questions. With cloud software, governments can automatically forecast budget execution through the end of the year based on current spending. By automating this process, governments can easily get a new base year and make necessary strategic decisions.

  • Scenario planning is essential

What-if scenario planning is especially important in helping governments identify which plans to implement during times of crisis or during more stable periods. State and local governments must start developing their own action plans according to different circumstances. If a crisis arises, governments will then be prepared to staff and properly respond to any situation, even the worst case.

  • The necessity for continuous budgeting

Budgeting best practices are always evolving. Line-item budgeting was replaced by priority-based budgeting, and now continuous budgeting is the next frontier. At its core, continuous budgeting means all activities take place in a cycle of financial planning, executing, reporting on financial plans, adjusting those plans and repeating the cycle. All the while, it’s important that the budget always aligns with strategic plans.

Continuous budgeting requires acquiring financial, economic and performance data, cleaning it and making it available to various applications. Cloud-based solutions can help facilitate the process of bringing all the data together into one portal. At that point, governments can create the right mathematical models to understand the data, make forecasts and predict outcomes.

This is also where collaboration comes into play. As more stakeholders are involved in the budgeting process to coordinate budgets and strategic plans, a centralised application that is not susceptible to version control issues is essential. Similarly, the introduction of social functions will streamline workflow and provide greater transparency and accountability.

Eventually, predictive analytics, statistical algorithms and machine-learning techniques will be essential to helping cities identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has already happened to capture insights into what could happen in the future.

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