Why artificial intelligence needs big data to offer business value


Big data is proving to be the missing piece of the puzzle that will let businesses profit from artificial intelligence (AI) and machine learning (ML). While AI and ML aren’t new technologies, they have begun to really demonstrate their business value as a result of being informed by big data.

AI and ML have grown exponentially over the last few years. 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.

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.

AI and ML solutions can be used for applications such as preventative maintenance, anti-fraud applications for banks, as customer service robots, and to provide e-commerce recommendations. If these solutions are fed the right data, they will be able to evolve and deliver a competitive advantage.

We have identified three key reasons data is essential for the success of AI and ML solutions:

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) ML 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 ML is that it learns rather than simply applying fixed rules. So, as it digests new data, a ML application adjusts its rules. This makes it even more important for ML to have an abundance of data to learn from so that it can apply sophisticated ‘thought’ processes to decision-making.

AI and ML have come into their own because of big data. Businesses looking to get maximum value from AI and ML must ensure they’re coupling these technologies with big data.

Alec Gardner, Director – Global Analytics Business Consulting, Teradata