Achieving a high level of accuracy in data labeling is vital to many of its practical uses. For example, if the application of annotation for autonomous vehicles is off by even a small percentage, what may seem like a minor detail within several images of the work can collectively mean a complete breakdown and failure to the safety and lives of drivers or pedestrians.

That same concept can be more simply understood if we think about a mural of Rubik’s Cubes®.

Mural of Rubik's Cubes

If we go from solving one single cube to crafting a beautiful mural of varying colors using a number of Rubik’s Cubes®, like the one in this video, the need for accuracy becomes more clear. The sheer coordination and complexity of labeling each and every cube in precisely the right manner is crucial to the end result. Put another way, the accuracy in the individual cubes drives the overall quality of the completed mural and whether it looks as the artist intended.

This analogy comparing a mural of cubes to a dataset for autonomous vehicles is reflective of the quality and approach required for your data labeling to come together seamlessly and perform as expected. The training dataset you use for your machine learning model will directly impact the quality of your predictive model. Just like those cubes coming together to create the perfect mural, it’s extremely important that you use a dataset applicable to your AI initiative and accurately labeled with your specific business requirements in mind.


Let’s say you want to create a mural that uses 1,000 Rubik’s Cubes® to complete the picture. Think of the cubes as the pieces of data that you use to train your AI model. Each point of lower accuracy gets you close to the mural on the left versus the one on the right.

Rubik's Cube

That’s essentially what the final quality of your AI model will look like if you choose to use data that isn’t accurately labeled. Even small misses on accuracy can have an enormous impact on the quality and functionality of the final product.

The more accurate each individual piece of data, the higher the quality of the full dataset. But how can we ensure a symbiotic process?


Hands down, achieving the highest level of accuracy is unattainable without the integration of good communication. And being able to communicate to each and every worker the context of the whole makes for better synergy.

Therefore, it’s vital that you’re able to communicate proactive and reactive feedback directly to the individual workers as they annotate your data so as much of the dataset is usable as possible and repeat errors are minimized or eliminated entirely.

Returning to our mural analogy, halfway through you may discover that you need a red corner on your cubes instead of orange. With strong communication between yourself and the cube solvers, these changes can be made quickly, all while optimizing throughput and outcomes more easily.

CloudFactory achieves this real-time efficiency in communication by having dual points of contact for the client.


CloudFactory uses a combination of a dedicated team lead and client success manager to support our clients and their work. How can dual points of contact create synergy for your data project?

First, the team lead is the main point of contact on the ground with your team of workers. They take your critical feedback or new direction as it comes and quickly cascade it to all members of the team. This real-time input ensures they have what they need to make adjustments as soon as possible.

The team lead also manages individual performance to make sure everyone is executing to the quality standards set from the beginning. Both the individual and the team as a whole are receiving the direct feedback they need to optimally perform.

Meanwhile, client success managers works with the client to understand their business needs and goals—ensuring the team lead and the team understand the context and importance of their work to the final product or use.

This powerful combination of communication support from both the business context and on the implementation side, ensures we seamlessly bring together those individual pieces making a brilliant whole picture for our clients. The mural is complete, it is accurate, and it performs as it should.


While all those crowdsourcing models can only support a random collection of individuals working on their own with little to no direction, CloudFactory has distinguished itself. We uniquely deliver a dedicated managed workforce, with robust immersive training, and the most multi-faceted communications available—all to achieve your greatest vision.

This trio of distinction is just one way we engage and align to achieve your perfect mural. Learn more about How It Works here.

Data Labeling Data Workforce Workforce Strategy AI & Machine Learning

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