Artificial intelligence and machine learning may not be new technologies, but they're only just starting to show their potential in what they can do for business. Big data may be the missing piece of the puzzle as to how they can profit, according to insights from Teradata.

The company's director of Global Analytics Business Consulting, Alec Gardner, says AI and machine learning have grown exponentially in a short space of time.

“Their business value lies in the fact that these technologies can automate business processes that would usually require human intelligence. However, it's one thing to apply deep learning and artificial intelligence tools to data. It's another to realise meaningful results that can make a real difference to the business,” he explains.

“To do this requires massive amounts of data, which helps AI systems better understand how to make the right decisions that will deliver optimum results. AI and ML don't just apply a fixed set of rules to prescribed situations; they constantly adjust and learn as new information is provided. Therefore, the more information these systems have, the better and more accurate their decision-making will be.”

Both AI and machine learning have practical applications in preventative maintenance, anti-fraud applications for banks, customer service robots and in eCommerce recommendations. However these solutions need to process the right data in order to evolve and deliver a competitive advantage for businesses.

Teradata believes there are three key reasons why data is essential for AI and machine learning success:

1)  AI is enabled by big data. AI has, in the past, been held back by limited sample sizes and an inability to process huge amounts of data fast enough to be useful. Now, AI can take advantage of larger databases and process data fast enough to provide meaningful learning and results. This makes it more useful in real-world scenarios where accurate, fast decision-making is essential.  2)  Machine learning relies on training data. Training data is the initial data set that the machine will learn from. Training data has inputs and pre-answered outputs so the ML model can look for patterns in any given output. For example, the input could be customer support tickets with email threads between a customer and a customer support representative (CSR). The outputs could be a categorisation label from one to five, based on the company's specific category definitions. The more volume and detail available in this data, the more effectively the machine can learn.  3)  Learning is ongoing. The key feature of machine learning is that it learns rather than simply applying fixed rules. So, as it digests new data, a Machine learning application adjusts its rules. This makes it even more important for machine learning to have an abundance of data to learn from so that it can apply sophisticated ‘thought' processes to decision-making.

“AI and machine learning have come into their own because of big data. Businesses looking to get maximum value from AI and machine learning must ensure they're coupling these technologies with big data,” Gardner concludes.