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Evolution Podcast: Greta Attard on Data Quality in Machine Learning
Go Autonomous Data Annotation Team Lead Greta Attard joined the Evolution Nordics podcast to discuss data quality in machine learning — and why the work of labelling and classifying data is foundational to the accuracy of AI-powered B2B solutions.
Executive Summary: Go Autonomous Data Annotation Team Lead Greta Attard joined the Evolution Nordics podcast to talk about data quality in machine learning — alongside Jesper Steen Andersen from Moodagent and Paolo Masulli from iMotions. The episode explores why labelled data quality is foundational to AI accuracy, and how data annotation teams work in practice to ensure machine learning models perform at the level that real-world B2B applications demand.
About the Episode
In a recent podcast by Evolution Nordics, Greta Attard, the Data Annotation Team Lead at Go Autonomous, talked about the importance of managing data quality with Jesper Steen Andersen, Head of Music Data at Moodagent, and Paolo Masulli, PhD, Product Manager at iMotions.
It is inspiring for us to exchange knowledge with industry peers to learn how they work with areas that mean so much to us, our business, and our customers. You can listen to the full episode on Spotify here.
Why Data Annotation Matters at Go Autonomous
At Go Autonomous, the data annotators play a vital role in labelling and tagging the data we use to train our machine learning models. Their work involves analysing and understanding data, identifying relevant features, and assigning labels or tags to data points according to predetermined criteria. They work closely with the machine learning team to ensure that the data is accurately labelled and meets the requirements for the models.
The accuracy and effectiveness of machine learning models heavily depend on the quality of labelled data. Ultimately, the success of our solutions depends on this — so the work that goes into the labelling and classifying is of utmost importance.
How data annotation connects to customer outcomes
When we onboard new customers, we start analysing their data in order to teach our models. That is why we are able to ensure a high level of satisfaction and accuracy in our products. The quality of Go Autonomous’s Autonomous Commerce platform — its ability to correctly interpret an incoming B2B order, resolve ambiguous product references, and apply the right pricing rules — depends directly on the labelling and classification work Greta and her team do every day.
For manufacturers and distributors, this translates directly into fewer errors, fewer exceptions, and higher straight-through processing rates from day one of deployment. See how our customers experience this in practice →
What the podcast covers
In the episode, Greta explains how her team and the rest of the Go Autonomous family work together to ensure best-in-class solutions and results for our customers. The conversation covers:
- How data annotation teams operate within AI product organisations
- The practical challenges of maintaining data quality at scale
- How different industries approach labelling for machine learning
- What good looks like when human review and model accuracy intersect
If you are interested in learning more, listen to the podcast to gain valuable insights into managing data quality in machine learning. And if you would like to understand how our solution can help your company, you can book a demo here.
Frequently Asked Questions
Greta Attard is the Data Annotation Team Lead at Go Autonomous. Her team labels and tags the data used to train the machine learning models that power the Autonomous Commerce platform.
Machine learning models are only as accurate as the data they are trained on. In B2B commerce, where the AI must correctly interpret orders and apply pricing rules, data quality directly determines how often transactions complete without errors.
Data annotation is the process of labelling and tagging raw data so machine learning models can learn from it. At Go Autonomous, annotators label B2B transaction data to teach models how to correctly classify and respond to different types of commercial requests.
Go Autonomous combines expert data annotation with continuous model training. When a new customer is onboarded, their data is analysed and used to train the models before go-live — ensuring the platform performs accurately from day one.
The platform uses AI to execute B2B commercial work end-to-end — from reading an incoming order to confirming it in the ERP — without manual intervention on routine transactions, across email, EDI, portal, and document channels.
See Autonomous Commerce in Action at the 2026 Summit
The Autonomous Commerce Summit 2026 brings together operations and commercial leaders from B2B manufacturing and distribution who are actively transforming how revenue is executed. Hear from the teams and customers behind the platform — and from the people building the technology that powers it.
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