What's the best way to improve data quality in budgeting and forecasting?

1 Define data quality standards

Before you start collecting, processing, and analyzing data for budgeting and forecasting, you need to define what data quality means for your organization and your goals. Data quality standards are the criteria and metrics that you use to measure and evaluate the quality of your data. For example, you might want to consider aspects such as completeness, accuracy, validity, timeliness, consistency, and relevance of your data. By defining data quality standards, you can set clear expectations and benchmarks for your data sources, systems, and users.

2 Implement data quality checks

Once you have defined your data quality standards, you need to implement data quality checks to ensure that your data meets them. Data quality checks are the processes and tools that you use to identify and correct data quality issues. For example, you might use data validation rules, data cleansing tools, data profiling tools, data auditing tools, and more. Data quality checks should be performed regularly and systematically, both before and after data entry, integration, transformation, and analysis. By implementing data quality checks, you can detect and fix data quality issues early and prevent them from affecting your budgeting and forecasting outcomes.

3 Document and communicate data quality issues

Another important step to improve data quality in budgeting and forecasting is to document and communicate data quality issues. Data quality issues are the deviations and discrepancies that you find between your data and your data quality standards. For example, you might find that some data is missing, inaccurate, outdated, inconsistent, or irrelevant. By documenting and communicating data quality issues, you can inform and educate your data sources, systems, and users about the causes and impacts of data quality issues, and the actions and solutions that are needed to resolve them.

4 Train and empower data users

One of the main factors that affect data quality in budgeting and forecasting is the human factor. Data users are the people who collect, enter, access, use, and share data for budgeting and forecasting purposes. For example, they might be managers, analysts, accountants, or planners. Data users have a significant role and responsibility in ensuring data quality, as they can introduce or prevent data quality issues. Therefore, you need to train and empower data users to improve their data quality skills and awareness. For example, you might provide them with data quality guidelines, best practices, feedback, incentives, and support.

5 Monitor and improve data quality performance

The final tip to improve data quality in budgeting and forecasting is to monitor and improve data quality performance. Data quality performance is the degree and extent to which your data meets your data quality standards. For example, you might use data quality indicators, dashboards, reports, and scorecards to monitor and evaluate your data quality performance. By monitoring and improving data quality performance, you can track and measure the progress and results of your data quality efforts, and identify and implement areas and opportunities for improvement.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?