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Data Quality and Machine Learning

What Is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) wherein computers learn to discern and act on subtle data patterns without being explicitly programmed to do so. By their very nature, machine learning models are very sensitive to the quality of the data with which they operate. Even relatively small errors in the training data can lead to monumental errors in the systems output.

Why is Data Quality Important to Machine Learning?

The quality of data used in any machine learning project will inevitably have a huge effect on its chances of success.

Data-intensive projects consistently have a single point of failure: data quality.

As a result, assessing and improving data quality should be the first step of any machine learning project. This includes checking for consistency, accuracy, compatibility, completeness, timeliness, and duplicate or corrupted records. Often, manually cleansing data is an impossibility, may take months or be cost-prohibitive.

For any company that wants to participate in the machine learning revolution – one that is already disrupting today’s business landscape – data quality is an issue that simply cannot be avoided.

Does your business need assistance in data management and improving data quality for machine learning?

Or do you need help creating data solutions in order to leverage other businesses?

Robinson Ryan is driven by the belief that the best data promotes the best business, and we are here to support you. Contact us today 

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We are an established data consultancy, working on some of Australia’s
biggest data management projects across seven capital cities.

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Megan Kelly General Manager

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