Agentforce's Best Friend: Clean, Reliable Data

Agentforce's Best Friend: Clean, Reliable Data

Today on the Salesforce Admins Podcast, we talk to Lizz Hellinga, a Consultant and Salesforce MVP. 

Join us as we chat about why clean data is essential for leveraging generative AI and how you can get your organization moving.

You should subscribe for the full episode, but here are a few takeaways from our conversation with Lizz Hellinga.

Generative AI thrives on clean data

Lizz has a clear message: clean data isn’t optional anymore. She stresses, “If your data isn’t ready for generative AI, your business isn’t ready.” As organizations eagerly adopt generative AI tools, Lizz cautions that the insights and outputs from AI are only as reliable as the data feeding into it. Simply put, poor-quality data undermines the potential of these powerful tools.

What constitutes “bad data”?

Bad data comes in many forms, and Lizz highlights the usual suspects:

• Duplicates

• Inaccuracies

• Incomplete records

• Outdated information

• Hoarded data

The concept of hoarded data stands out as particularly problematic. Many organizations have accumulated large amounts of data they no longer use but are reluctant to delete. According to Lizz, the solution starts with collaborating with stakeholders to establish a robust data governance policy that defines data quality standards and retention policies. Reporting, she adds, can be a powerful tool to illustrate the value of clean data and spotlight areas of improvement.

Steps to prepare data for AI

Lizz has four steps you can take to start your journey toward clean data:

1. Document your processes: Map out key workflows to find the key points where data enters the system. How can you make it easier to get high-quality data at these moments?

2. Engage your stakeholders: Explain why clean data is vital for AI initiatives. Use examples to illustrate how high-quality data enables AI to deliver actionable insights that can make their work easier and more efficient.

3. Prioritize key areas: Focus on cleaning data critical to processes where AI will have the most impact, such as sales or customer engagement. Start with essential objects and gradually expand to other areas.

4. Leverage your tools: Use reports, dashboards, and tools like Salesforce’s “Clean Your Room! Dashboard” to gamify data-cleaning efforts. Friendly competitions can motivate teams to tackle bad data without feeling overwhelmed.

Proactive data management strategies

Preventing bad data is just as important as cleaning up existing records. Lizz suggests:

• Using paths and dynamic forms to streamline data entry.

• Highlighting key fields on page layouts to emphasize their importance.

• Integrating email tools like Einstein Activity Capture to ensure accurate, up-to-date information.