Companies exploring how artificial intelligence (AI) can improve their business operations often miss a crucial element: data quality.
At the heart of any successful AI project is solid data. Companies are surprised to find that the majority of work in an AI project revolves around data – engineering it, integrating it, cleaning it up, and enriching it. Without these foundational steps, even the most advanced AI models can fail.
As exciting as AI is, it’s only as good as the information feeding it. In a recent webinar, we discussed data quality and AI and wanted to share these insights from Jeff Roberts, Founder & CEO at Innovation Vista.
Garbage In, Garbage Out
AI doesn’t have human judgment. It works purely with the data it’s given.
High-quality data can produce incredible results, while poor data can lead to inaccurate outputs. This is why data quality is so important when working with AI. Nothing can kill momentum faster than a model that churns out false positives and negatives.
To ensure AI delivers something of value, you need to prioritize these two strategies at the start:
- Conduct a Data Quality Audit
Pay close attention to your data. Conduct a one-time data audit before building your model and regularly audit the quality of your data to identify missing or inaccurate information.
One interesting tactic is to use AI to abstract data from your audit documents and ask it to find those gaps and any trends that appear inconsistent.
- Adopt a Human-in-the-Loop Approach
AI shouldn’t work alone. Instead, adopt a human-in-the-loop approach where AI generates initial drafts and a person reviews and refines them.
This method ensures that a human expert can catch any issues that may have slipped through the AI.
Improving Data Entry
The strategies above rely on accurate data entry, which often comes in through the humans on your team.
Consider your sales team: They’re working in your CRM, inputting data about prospects, leads, and customers daily – an often mundane task with little immediate reward. If employees can’t see the value of accurate data entry, it becomes easy to abandon it.
In contrast, with a human-in-the-loop approach to data and AI, employees can see the direct impact of their inputs and outputs, encouraging them to be more aware of how data quality affects decisions made at the executive level.
Improving data entry for ongoing data quality is a culture shift where data is embedded as an asset in day-to-day operations. When employees see the direct return on their efforts, they understand that high-quality data leads to better insights and improved outcomes for the business – and themselves.
By prioritizing data quality and incorporating human oversight, companies can access the full potential of AI models for analyzing their data. If you’re curious about using AI to transform your business model, contact infoFluency. We can help you harness this powerhouse to make smarter, faster, data-driven decisions.
