How do you handle data quality and consistency issues in rolling forecasts?

1 Establish data governance

Data governance is the set of policies, processes, and roles that define how data is collected, stored, accessed, and used in your organization. It helps ensure that data is consistent, complete, accurate, and secure, while avoiding data silos, duplication, and conflicts. To establish data governance for your rolling forecasts, you need to define the scope and frequency of forecasts, identify data sources and systems, assign roles and responsibilities for data collection and validation, create data quality rules and metrics, implement data quality checks and audits at regular intervals, document policies and processes, and provide training to users and owners. This will enable you to monitor and improve data quality over time.

2 Automate data collection and integration

Manual data entry and manipulation are vulnerable to human errors, delays, and inconsistencies, which can impede the quality and timeliness of your forecasts. Automating data collection and integration can reduce these risks and improve data efficiency and accuracy. Automation can streamline and standardize data extraction, transformation, and loading (ETL) processes from different sources, eliminate manual data entry, copying, and pasting, enable real-time or near-real-time data updates and synchronization across platforms and tools, save time and resources, as well as free up staff to focus on data analysis and interpretation. To automate data collection for your rolling forecasts, you need to evaluate your current architecture, choose appropriate automation tools and solutions that fit your needs and budget (e.g. ETL software, cloud-based platforms, or APIs), design and implement workflows and pipelines, test and validate their functionality/performance, monitor/maintain processes, as well as troubleshoot any issues or errors that may arise.

3 Align data definitions and formats

Data alignment is the process of ensuring that your data is consistent and compatible across your data sources, systems, and tools. This is essential for your rolling forecasts, as it allows you to compare and consolidate data from different sources, improve clarity and transparency, and enhance data analysis and reporting. To align data definitions and formats for your rolling forecasts, you need to establish a common data dictionary or glossary, define the data formats, units, scales, and currencies that you use in your forecasts, implement normalization or standardization techniques, and review and validate your data alignment regularly to resolve any discrepancies or issues.

4 Update data drivers and assumptions

Data drivers and assumptions are the factors that influence your forecasts, such as market trends, customer behavior, competitor actions, or internal performance. These are based on historical data, current data, or future projections and can change over time due to internal or external events or conditions. Updating your data drivers and assumptions is essential for your rolling forecasts, as it enables you to capture the latest changes and developments in your business environment and adjust your forecasts to match your current reality and expectations. This will help enhance forecast accuracy and relevance, as well as support decision-making and planning processes. To update your data drivers and assumptions for your rolling forecasts, you need to identify the key drivers and assumptions that affect your forecasts, monitor and analyze them regularly, review and revise them periodically, incorporate new information or insights that may affect your forecasts, and communicate them clearly and consistently to data users and stakeholders.

5 Solicit feedback and input

Feedback and input are the opinions, suggestions, or questions that you receive from your data users and stakeholders. This type of feedback is valuable for rolling forecasts, as it can validate data quality and consistency, provide different perspectives, and engage stakeholders in the forecasting process. To solicit feedback and input for your rolling forecasts, you need to define the purpose, scope, and format of your requests, ask specific questions or prompts that elicit useful responses, collect and analyze the feedback received, prioritize the actions or improvements to be implemented, and acknowledge and appreciate the feedback. Furthermore, share the results or outcomes of your actions or improvements with your data users and stakeholders.

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?