Data management is the practical implementation of your data governance framework, ensuring that your organization’s data assets are handled in a consistent, reliable, and trustworthy manner. While data governance sets the rules and guidelines, data management brings them to life in the day-to-day activities of your organization. It’s the piece most people are familiar with, and it’s crucial to maintaining trust in your data—particularly as AI plays an increasingly prominent role in business decision-making. The quality of AI insights depends heavily on the quality of your data management practices. Below, we’ll explore the key components of data management that can help you stay on top of your data and keep it primed for the future.
Data Entry: Maintaining Data Quality from the Start
The foundation of quality data starts with proper data entry. It’s easy to overlook this step, but inaccurate or inconsistent data entry can have a ripple effect, causing errors down the line and diminishing trust in the system.
Best Practices for Data Entry
- Standardize data fields and formats across the system.
- Implement validation rules to prevent incomplete or erroneous entries.
- Use picklists and lookup fields to minimize free-text errors.
- Provide descriptions and help texts on all fields to help guide users.
- Provide training for staff to ensure consistency and accuracy.
By embedding these practices into your processes, you’re ensuring that the data coming into your system is trustworthy, right from the start.
Data Cleansing: Keeping Data Accurate and Relevant
Even with the best data entry practices, data inevitably becomes outdated or human error will introduce bad data. Regular data cleansing is critical to removing inaccuracies, duplicates, and obsolete information. Apsona is a great tool to help with data cleansing.1
Key Strategies for Data Cleansing
- Schedule regular data audits to identify inaccuracies.
- Use automated tools to de-duplicate data.
- Update incorrect or outdated information based on new insights.
- Set alerts or triggers for data entries that deviate from your standards.
Clean data ensures that decision-makers can trust the information they’re working with, and when AI processes this data, it yields better, more reliable results. In a recent poll of users, 17.4% of respondents noted that their leadership does not trust their data to make decisions; almost 35% weren’t sure if their leadership trusted their data for decision making. If your data is not trustworthy, why are you collecting it?
Data Enrichment: Adding Value to Your Data
Data enrichment involves enhancing your existing datasets by incorporating external data sources. This step ensures that your information stays current and comprehensive. Data Cloud2 is the most recent innovation direct from Salesforce to enrich data by connecting all the information on your constituents for a 360 degree view. Data enrichment is not limited to the data you own; for example, you can enrich your data on nonprofit organizations using Candid®’s APIs3.
Steps for Effective Data Enrichment
- Identify external data sources that can complement your existing data.
- Use APIs or data integration platforms to automate data enrichment.
- Ensure that external data sources meet your governance standards and are current and relevant.
- Regularly review and verify the accuracy of enriched data.
By keeping your data enriched and up-to-date, you provide a more complete picture for business intelligence tools and AI models to work with, leading to more nuanced and actionable insights.
Data Retention: Knowing What to Keep
Data retention policies are crucial for compliance and operational efficiency. Knowing how long to retain certain types of data helps streamline storage and ensures you’re meeting regulatory requirements. Reviewing regulatory requirements on a regular basis and updating the organization wide data retention policy is also important to maintain compliance. Tying data retention policies to your backup plan helps streamline this effort. Backing up your data on a regular basis is also critical and there are numerous options available4.
Best Practices for Data Retention
- Clearly define retention periods for different types of data with consideration for the consumers and their needs.
- Automate data retention policies within your CRM or data management platform.
- Ensure compliance with industry-specific data retention regulations (e.g., GDPR, HIPAA).
- Regularly review retention policies to ensure they remain relevant as business needs evolve.
Data retention helps you keep only what’s necessary and prevents your system from being clogged with irrelevant or outdated information.
Data Archiving: Keeping What Matters, Compliantly
While some data may no longer be relevant for day-to-day operations, it often still needs to be retained for legal or regulatory reasons. Data archiving helps you manage this without cluttering your active data environment.
How to Implement Effective Data Archiving
- Define the types of data that should be archived rather than deleted.
- Implement secure, accessible archiving solutions to store this data.
- Ensure that archived data can be easily retrieved when needed.
- Monitor archived data to ensure it remains secure and intact.
Archiving is not just about storage; it’s about ensuring compliance while maintaining the agility to retrieve data quickly when necessary.
Data Disposal: Ending the Lifecycle Securely
When data is no longer needed and does not require archiving, secure disposal is essential. Improper data disposal can lead to security risks and breaches.
Guidelines for Secure Data Disposal
- Establish policies for securely deleting data when it’s no longer required.
- Use encryption and secure deletion tools to prevent unauthorized access.
- Document data disposal actions for compliance purposes.
- Ensure that all stakeholders are trained on secure disposal processes.
By securely disposing of unneeded data, you mitigate risks and reduce your data footprint, making your overall data management more efficient and secure.
Conclusion
Data management is at the heart of maintaining a trustworthy, efficient, and compliant data ecosystem. As AI continues to grow in importance, the quality of your data will determine the value of the insights it produces. Ensuring that you are on top of key data management areas—data entry, cleansing, enrichment, retention, archiving, and disposal—will help your organization not only manage today’s challenges but also prepare for tomorrow’s AI-driven opportunities.
REMEMBER:Â There is a direct correlation between how good your data management is and how good your AI results are.
