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Quality data: The key driving force behind artificial intelligence (AI)

Artifical Intelligence

By Tim Holt 3 min read

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Without doubt, Artificial Intelligence (AI) is the ‘buzz’ word in marketing at moment. Marketers love technology of all types especially when it makes the job easier! In fact, some state AI is one of the most transformative powers of current times. While there is debate around whether AI is a good or bad thing, there are no quibbles when it comes to the driving force behind it – AI would be nothing without the input of quality data.

What is Artificial Intelligence (AI)?

AI is generally defined as the science of making computers do things that require intelligence when done by humans. In simple terms, AI is the ability of a computer program or a machine to think and learn allowing it to mimic human behavior. There is little dispute that machine learning (ML) and AI are transformative technologies in most areas of our lives.

For example, the billions of searches done every day on Google provide a sizable real-time data set for Google to learn from our typos and search preferences. Apps like Spotify and Netflix, while relatively simple compared to other examples of AI, deliver a useful task: recommending music and movies based on the interests you’ve expressed and judgments you’ve made in the past. By monitoring the choices you make and inserting them into a learning algorithm, these applications make recommendations that you’re likely to be interested in.

Artificial Intelligence continues to flourish due to the exponential growth of data

AI technologies have existed for several decades, however, it’s the explosion of data—the raw material of AI—that has allowed it to advance at incredible speeds.

Each year, the amount of data we produce doubles - this data is instrumental in helping AI devices learn how humans think and feel, and accelerates their learning curve and also allows for the automation of data analysis. The more information there is to process, the more data the system is given, the more it learns and ultimately the more accurate it becomes. Artificial Intelligence is now capable of learning without human support.

In the past, AI’s growth was restricted due to limited data sets, representative samples of data rather than real-time, real-life data and the inability to analyse massive amounts of data in seconds. Today, there’s real-time, always-available access to the data and tools that enable rapid analysis. This has propelled AI and ML and allowed the transition to a data-first approach.

AI and quality data

Any AI system you choose to make can only be as smart as the information you provide it with. This harks back to the common philosophy – rubbish in rubbish out. Even ML technology – which can make decisions and adjust its actions even without explicit programming – need exposure to quality data in the first place. Even if this data is not continually governed and maintained, then it still needs some form of administration to ensure it is used in a fair, responsible and accurate way.

Before you embark on an exciting new project to introduce a ‘virtual assistant’, or use clever AI algorithms to start segmenting customers, you first need to ensure you are happy with the data you want to fuel it with. This requires data to be both carefully selected (to consider the data you should and shouldn’t use) and meticulously prepared - consistently formatted and cleansed to fix missing data and inaccuracies removed.


Your smartphone, your car, your bank, and your house all use AI on a daily basis; sometimes it’s obvious what its’ doing, like when you ask Siri to get you directions to the nearest petrol station. Sometimes it’s less obvious, like when you make an abnormal purchase on your credit card and don’t get a fraud alert from your bank. AI is everywhere, and it’s making a huge difference in our lives every day and set to change the way marketers market. But don’t forget what makes this transformative force tick…. well structured and organised data that AI can learn from.

Further reading: Data HQ's complete guide to data quality

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