Welcome to 2024, where we’ve mastered the fine art of data hoarding! It seems organisations (and not all) have decided that more information automatically equals more intelligence. Never mind that we’re drowning in so much data that the quality is being swept away. In the frantic rush to gather as much data as possible, we’ve created a paradox: the more info we collect, the more panicked we get trying to figure out what to actually do with it.
Of course, we’re all secretly hoping AI will sweep in like a digital Marie Kondo, tidying up the chaos and sparking joy by turning it all into useful insights. But here’s the kicker: the faster we collect data, the less we care about its accuracy. It’s like stuffing random items into a closet before guests arrive — hoping no one notices the mess.
Let’s face it: we’re all obsessed with being first. Not necessarily the best, and definitely not the most accurate — just first. In today’s world of instant gratification, social media (I’m looking at you, Twitter — sorry, X) breaks news faster than newsrooms can fact-check, and companies have adopted the same strategy with data. “Quick! Get the results out before anyone else!” is the battle cry of modern business, while fact-checking quietly sits alone in the corner like an uninvited guest. Does this sound familiar? Extra, Extra, read all about it.
The sad reality is that organisations are more concerned with speed than with the quality of information they’re using. It’s a mad dash to collect data, analyse it later, and deal with any inaccuracies only when the next breach or PR disaster rolls around. It’s like sprinting toward a finish line no one can actually see.
Let’s be real: we’ve all become data hoarders. We’re grabbing every piece of information we can get our hands on and burying it in digital databases like squirrels hiding nuts for winter. The problem? We’re collecting way more than we’ll ever use, and we don’t even know what to do with most of it. It’s like asking someone to find a needle in a haystack — then adding more hay every five minutes just to be safe.
In theory, all this data is supposed to make us smarter. But here’s the thing: just because you have a pile of random facts doesn’t mean you’re going to get anything useful out of them. AI can help, sure — but it’s not magic. It still needs good data, not a mishmash of random bits thrown together out of fear that we’re missing something.
Remember when data quality used to matter? Those were the good old days. Now, we’ve entered an era where more data is better, whether it’s useful or not. “Big Data” has become a bit too literal. It’s like going to a restaurant, and instead of getting a gourmet meal, they just dump 50 random ingredients on your plate. Sure, it’s technically food, but it’s not the kind of dining experience anyone actually wants.
And here’s the craziest part: we’re acting like this is progress. The more data we collect, the more we pat ourselves on the back for being “data-driven,” even if half of that data is completely useless. Somewhere along the way, we forgot that the point of data is to inform decisions, not to overwhelm us with an endless flood of numbers.
Now, let’s talk about AI — poor, overworked AI. We’ve convinced ourselves that AI is the janitor we can call in to clean up our data mess. We dump a massive pile of information into the AI machine and cross our fingers, hoping it will miraculously organise it into neat little piles of insight. But here’s the thing: AI is only as good as the data we give it. Garbage in, garbage out.
We’re treating AI like some sort of superhero, when it’s more like an overworked intern trying to figure out which spreadsheet to tackle first. Yes, AI is powerful, but it’s not infallible. It can’t fix your data problems if you’re feeding it low-quality information. The more data you throw at it without curation, the more likely you are to end up with results that are just as messy as the data you started with.
Let’s break it down: intelligence, in the realm of data, isn’t just about hoarding as much information as possible. It’s about transforming raw data into something actionable. And here’s the kicker: the quality of the data is what really matters. If your data isn’t accurate, relevant, or timely, your “intelligence” is basically useless.
True intelligence emerges from the balance between depth of information and clarity. Well-curated, clean data allows organisations to make informed decisions, while poor-quality data leads to confusion and bad decisions. It’s that simple.
Methodology for Smarter Data Use
Creating actionable intelligence from data requires a structured approach. Here’s and example methodology on how to do it right:
If you’re into hoarding data like it’s a digital treasure chest, just wait until a breach happens and regulators start knocking. With every extra byte you store, you’re upping the ante on a potential disaster — and when the breach happens, the penalties will make you rethink your life choices. If fines and legal repercussions don’t motivate you to downsize your data collection habits, nothing will. After all, why invite more trouble when you could just delete what you don’t need?
Here’s the harsh truth: we’ve become obsessed with collecting data because we’re terrified of missing out. It’s like we have data FOMO (fear of missing out), and it’s leading us down a path where quantity trumps quality. But the fact is, we don’t need to be first, and we don’t need to collect more data than anyone else. What we need is better data.
So, next time you’re tempted to hoard more information or rush to release insights, remember it’s not about how much you collect — it’s about how well you use it. And if your plan is to let AI figure it out, maybe give it a break. It’s already sorting through enough junk as it is.