Artificial intelligence (AI) is everywhere nowadays. Shop around a few marketing tools, a bit of HR tech or some financial software and you’re bound to see something labelled ‘AI-powered’. It’s ubiquitous in many industries, though perhaps not as well known in data management. But it’s with AI data management where this technology can really make an impact.
First up, let’s recap on the different types of AI. In the context of data management, we’re mostly referencing automation and machine learning.
Traditionally automation involves software that follows certain rules in order to execute tasks (it doesn’t always have AI involved, but when we mention it in this piece, this is what we’re referring to). It performs repetitive, monotonous tasks that people can’t (or won’t) do. This frees up time for people to focus on more interesting, strategic stuff.
Machine learning is a subset of AI whereby a machine is trained on a specific set of data to perform tasks. It is limited to whatever data sets it has been given. So, an image recognition algorithm isn’t going to be much use in language processing. There’s a feedback loop in machine learning. This allows it to refine its processes over and over again until it becomes the master at that particular task. It performs mundane, routine tasks to free up people. If that sounds similar to automation, that’s because it is. Automation can use some machine learning in order to get smarter at performing its tasks.
True AI doesn’t really come into this. It’s not been cracked yet. True AI mimics human intelligence by being good at a lot of different things. That requires a LOT of data. If you imagine a child growing up, they’re taught all sorts of things. They need to know how to talk, read, move, count, comprehend their environments, sense danger, interact with other humans, and make decisions. That’s how intelligent AI needs to be in order to mimic human logic and reasoning. There’s still a long way to go.
Deep learning uses artificial neural networks to produce insights. Just as with neurons in a human brain, each individual algorithm adds to the whole. It can process more complex tasks and analysis in comparison to machine learning. It’s the middle ground between machine learning and true AI.
Data management faces many challenges, not least the sheer volume of data that organisations now have to deal with. Big data doesn’t exist anymore, it’s all just data. It’s all big. Organisations need to sift through this mountain of data in order to uncover the right insights. It’s a tough job and one that can be made much easier with AI.
There are many potential applications and benefits of using AI data management. The key with all these tools is to remember the human element. You’re using them to do most of the grunt-work for your engineers.
For want of a better phrase, AI helps separate the wood from the trees. With the help of AI data management, you’ll be able to spot problems with more easily and stop would-be hackers or data misuse. It won’t solve all of the challenges that data management faces, but it can definitely help you combat them.
Are you searching for ways to develop your data management? Read about the latest approaches and technologies in our Data Management white paper.