January 9, 2024

Good Data is Essential to Unlocking the Power of GenAI. But What Does "Good Data" Really Mean?

John Ream
Good Data is Essential to Unlocking the Power of GenAI. But What Does "Good Data" Really Mean?

Table of Contents

By John Ream, Enterprise Sales Engineer at People.ai

In today's rapidly evolving world of Go-To-Market (GTM) sales technology, Generative AI is at the forefront of innovation. We're witnessing the dawn of a future where auto-generated dashboards will effortlessly highlight strengths and weaknesses, while chatbots stand ready to dive deeper into users' inquiries. The potential is undeniable, but here's the catch: the success of any Generative AI solution hinges on one crucial factor—high-quality data.

High-Quality Data: The Backbone of Valuable Insights

It might sound like stating the obvious, but it’s a point worth emphasizing: high-quality data is the lifeblood of high-quality insights. The truth is, many current Generative AI solutions are built upon a foundation of low-quality data. Consider this example:

What you see here is Captain Obvious in action, presenting information that offers no real value. It’s a stark reminder that, without high-quality data, Generative AI insights can be painfully obvious and utterly unhelpful.

The Pitfall of Inadequate Data Quality

However, the problem goes beyond merely “Captain Obvious”. It extends to the risk of being flat-out wrong. In today’s market, numerous automated activity capture solutions promise seamless data automation for your organization. Yet, there’s a significant gap between these solutions and the quality of data they deliver.

Is more automation always better? The answer isn’t straightforward. Yes, automation is beneficial when it’s accurate and precise. But, it becomes a liability when it either misses key relevant data or worse, populates data in the wrong places. When this happens, it can lead to rather comical situations:

Avoiding Costly Mistakes in Generative AI Insights

So, what are some prime examples of automated activity capture solutions gone awry, potentially harming your Generative AI insights? If your automated activity isn’t matching at a high and accurate rate, essential activities could be missed or incorrectly categorized. Let’s delve into an illustrative example:

GenAI Insights for Opportunity. Reality and Bad GenAi Insightsl

When such data is missing or mismatched, your Generative AI insights may steer you in the wrong direction.

Is it the GenAI Solution’s Fault or Your Data’s Fault?

Here’s the million-dollar questions: when your Generative AI solution falters, is it the GenAI’s fault, or is it the data’s fault? No one wants to be stuck in this predicament…

Key Considerations for Your Automated Activity Capture Solution

To ensure success with  your Generative AI solution, certain essential elements must be in place within your automated activity capture solution:

  1. Strong Filtering of Non-Business Activities: Ensure that non-business related activities are filtered out effectively
  2. Protection of Sensitive Data: Make sure sensitive data is never placed into your CRM system.
  3. Accurate Score-Based Matching: Utilize AI-powered algorithms for account and opportunity level activity matching
  4. Automated Contact Creation and Continuous Contact Enrichment: Streamline the process of creating contacts within your CRM and ensuring they are always fueled by the most up-to-date information.
  5. End User Dashboards: In reality, no solution is 100% perfect. The next best thing is enabling the field to make it perfect. Provide users with relevant dashboards to easily detect and curate any inaccuracies in matched activities.

By prioritizing these factors in your automated activity capture solution, you’ll be well on your way to ensuring the quality of the data that fuels your Generative AI Solution, ultimately paving the path to insightful, actionable results.

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