Your budgeting and forecasting goals are off track. How can you realign them with your boss's expectations?

Begin by assessing the quality of your data. Look for inconsistencies, errors, missing values, and outliers. Use data profiling techniques to understand the distribution and characteristics of your data. To further validate your data, cross-verify your data with multiple sources to ensure its accuracy and reliability. If discrepancies are found between different sources, investigate the reasons behind them and determine which source is the most trustworthy. Finally, compare historical data with actual outcomes to validate the accuracy of your data. Look for patterns, trends, and correlations to ensure that the data accurately reflects past performance.

To realign your budgeting and forecasting with your boss's expectations, assess where things went off track, then have an open discussion to understand any changes in priorities. Adjust your forecasts accordingly and create a clear action plan. Monitor progress regularly and stay flexible to adapt to any new challenges or shifts in goals.

The first step is to go back to your data to check for any errors even numerical errors. Go back to check your historical information. Make complete analysis projections. There may be errors discovered from your side that can be corrected. Then communicate any findings and also compare the data of your boss. Mutually agree on underlying issues.

Incorporating feedback from your boss's perspective with openness is important. The issue is that credible budget and clear forecasting goals should prevail. Any errors that have been identified and corrected should be used as learning curve and way forward to ensure quality at all times.

List out all the assumptions that underpin your budgeting and forecasting models. These could include factors like sales growth rates, production costs, inflation rates, market demand, etc. You should also analyse historical data to validate the assumptions made in your models. Look for patterns, trends, and outliers that may indicate whether your assumptions hold true in the real world. If there are discrepancies between historical data and your assumptions, it may be necessary to adjust them.