Machine learning and artificial intelligence are reshaping the technology world. But machine learning is only as effective as the data that drives it. In other words, if you want to implement effective machine learning, you need to pay attention to data quality.

Machine learning and artificial intelligence (AI) are not new concepts. They have been around since the early decades of computing when theorists like Alan Turing began imagining ways to make computers “think” rather than just follow instructions.

Worries that sentient computers could enslave humans soon followed, exemplified by films like Jean-Luc Godard’s Alphaville.

Machine Learning and AI Today

Fast forward to the present, however, and machine learning and AI are not for theorists and sci-fi films anymore. They are exerting a huge influence on the way software is developed and used.

Part of the reason is that Internet of Things (IoT) devices rely heavily on machine learning to decide what to do. For example, smart thermostats like the Nest don’t just turn the heat on at times that you configure. Instead, they decide when you want the heat to be on and flips the switch automatically, by learning your preferences. They gain that insight based on machine learning and AI.

Machine learning has also become a crucial part of the way organizations use technology because it’s the only means by which to achieve instant results when operating at a large scale. If you want your website to suggest products to customers when they visit it, and you get thousands of visitors per day, there’s no way you could make recommendations manually. So, you use machine learning to generate recommendations in real-time as visitors come to the site.

Data Quality’s Role in Machine Learning

Sometimes, machine learning may seem like a “silver bullet.” It allows us to do things with technology today that our predecessors could barely imagine.

Yet the tricky thing about machine learning and AI is that, without high-quality on which to operate, they don’t work well at all. In this sense, machine learning is less magical. It’s not just something you program into your app or website to get amazing functionality that requires no maintenance. Instead, machine learning requires an ongoing commitment to data quality to drive it.

Why? Because the algorithms that power machine learning and AI engines need data in order to validate or confirm the conclusions they draw.

For example, let’s say the AI engine on your website makes product recommendations to website visitors based on information about their geographic location and past shopping habits. But perhaps the geographic data that you collect about site visits is wrong because of a coding problem. Instead of recording the data accurately, your website dumps default values to your database. As a result, site visitors might end up seeing product recommendations that aren’t available in their locations.

In this example, data quality problems undercut the effectiveness of your machine learning algorithms. And unless you manually audit the product recommendations, the error – though simple – can be hard to identify.

Achieving Data Quality

To avoid issues like this and make the most of machine learning and AI, it’s important to include data quality solutions – like Trillium, which is now part of Syncsort’s suite of data liberation, integrity and integration solutions – within your operations. Don’t let data quality problems prevent you from reaping the rich benefits that machine learning and AI are now providing to businesses.

For more information about leading Data Quality solutions, download the Gartner Magic Quadrant for Data Quality Toolsreport.

Let’s block ads! (Why?)

Syncsort blog