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.
Defining AI data management
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.
AI data management’s mountains
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.
5 ways AI data management can help now
- Sorting vast quantities of data: Automation via AI allows companies to sort and manage vast quantities of data much more easily. It can analyse certain data sets and tell the organisation how it should be stored, where, and whether it needs further processing. Automation can also cleanse and process data to be in the right format. This can significantly cut down on the amount of work that your data team has to do in order to prepare your data for analysis. It’s scalable too. As your amount of data grows, AI can scale with it. Instead of you having to hire more and more people, your AI will do most of the legwork.
- Flagging sensitive data: Then there’s GDPR. Four letters that I’m sure we’re all a bit tired of hearing right now! GDPR raises the stakes for all organisations. Sensitive personal data needs to be secured and managed well. AI can help with this too. It can flag confidential information based on patterns. Emails follow a particular format, for instance usually ‘email@example.com’ or similar. An AI can be trained to spot this and then let someone know which data sets contain it. That allows a data team to prioritise the security of those data sets, which increases their efficiency.
- Monitoring for threats: AI can proactively monitor the environment of your data. It can constantly check for threats, analyse who has access to the data and what they’re doing to it – and flag any anomalous behaviour. With large data sets, it’s impossible to manually achieve this level of security. Therefore, a degree of machine learning is required.
- Finding data: With all the data currently available, manually finding the data that you need can be a bit like a needle in a haystack. Luckily, AI can work like a magnet and find that needle in next to no time.
- The legal industry, for instance, has hundreds of rulings and previous legislation to trawl through. This used to be a job for junior lawyers, who would often burn the midnight oil reading case after case in order to find the most relevant ones for their seniors. Now AI can use text recognition to understand keywords and other content. It will then flag this to a lawyer to review. In this way, lawyers only need to focus on the most relevant information.
- There’s also exciting applications in medical surveys. IBM’s AI technology Watson has been helping researchers spot patterns in past medical research to help cure diseases including cancer. It cuts down on the amount of time they have to spend on their own searches. With all the past medical papers out there, manually going through every single one would take a lifetime.
- Automated data integration: AI-powered tools to load and ingest data are becoming more common. This means that the traditional process of Extract, Transform and Load (ETL) might soon be a thing of the past. AI can suggest rules, data transformations, or the next best action after data integration. It can also recommend data sets to a data team for specific analysis projects.
Let AI data management do the work
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.
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