When thinking about how computers manage and work with data, a cartoonish image of filing cabinets, binders, and folders might come to mind. It’s a silly way of picturing this virtual information, and we all recognize that it’s just a useful metaphor. When we take a step back to think about what that process actually looks like, a lot of us are truly left in the dark.
Perhaps even more confusing to those unfamiliar with the practice is that there is no one standard way to structure data, which can lead to some pretty big logistical problems if not treated with care. Further, as machine learning continues to come into vogue, the ability to rapidly access enormous quantities of data throws another curve into the task. There’s a lot of jargon here, so let’s break it down. What is data layer architecture, how does machine learning fit in, and what does all of this mean?
Data Layer Architecture
Stepping back to that filing cabinet example, think about how this disconnect might be a problem. Multiple people and, likely, multiple teams will need to be accessing company data for different reasons and processes and, unless otherwise defined, these different people may have different conceptions of what those filing cabinets actually look like. Unchecked, this can lead to confusing and inefficient data structure and organization that can be a nuisance at best, and disastrous at worst.
Having consensus on a data pattern is not enough on its own, however. There is no universal way to structure a data layer, and that’s because different needs and processes can benefit from different structures—here are some common patterns. How your data architecture looks can depend on a variety of factors, like who and what needs to be accessing the information, and how quickly the data needs to be accessed.
Implementing Machine Learning
There has been a big boom with machine learning in business spaces, and for good reason. The ability to more-or-less automatically forecast trends and evaluate complex and nuanced decisions not only sounds great, but has proven to be quite useful in practice. In actuality, the business sphere is rapidly adopting a variety of machine learning practices, and those who resist doing so are at risk of being left behind.
“Implementing machine learning” is much more than just a mindset or a simple decision, however. There is no machine learning switch on your existing code or architecture, and any sort of artificial intelligence requires a fundamentally distinct approach to data. Namely: volume and access. To put it simply, machine learning is a tool used for predictions that is “trained” with a data pool. Give the algorithm a few million examples of what you are and aren’t looking for, and it’ll “learn” the patterns on its own. Then, it needs continued access to this and more data going forward in order to make new and insightful predictions based on information already available to you. Sifting through much more data than humanly possible, this algorithm will be able to spot details and solutions much faster and more precisely than any person. That’s the magic.
Quick and ready access to data pools isn’t exactly trivial, though. Before, data storage looked quite literally like the filing cabinet image with on-premise hardware. Now, businesses are rapidly adopting a cloud or hybrid approach that, among other things, grants two key benefits: accessibility and speed. Most, if not all, machine-learning-oriented data structures will likely have a cloud implementation for this very reason.
The Future of Data and AI
Few things stay stagnant in the tech industry, especially when a paradigm shift forces change. For us, this shift is artificial intelligence and machine learning, and it’s forcing us to reimagine a lot of the traditional approaches we have with software. The sheer quantity and speed required to make these processes work challenge common data structure and organization practices, and beckon towards new ways of managing data altogether.
Uncharted as they may be, these leaps forward are exciting. The fluidity and power of machine learning crosses new frontiers in agile practices, and opens new doors for all businesses that successfully adapt. Now more than ever, it’s important to read up and stay ahead of the curve, because these changes are happening fast.
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