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Hey IT person, your data ís not gold (yet)
Data is the new gold. So they say. But those who look beyond the hype know: that's true only in exceptional cases. In practice, the average data set looks more like scrap metal, garbage or at best: used tin.
We seem to have forgotten that gold is rare. That you have to mine, separate, purify. And that applies just as much to valuable information. Because data without context, without quality, without interpretation is not gold. It is ballast.
It is tempting to go along with the fairy tale of the data treasure: if we collect enough, the value will automatically surface. But that's not a strategy; that's hoping for magic. Alchemy 2.0.
Because bad data doesn't get better from a data lake or a fancy dashboard. On the contrary. Crap in, crap out. And that too on a grand scale. And even good data is still just a raw material. Only when you process that raw material, enrich it, connect it to the right questions and processes, does information emerge that supports decisions. That creates value.
In times when everyone is clamoring to “do something with data,” it's more important than ever to realize: not all data is valuable. And not every IT system is able to tell the difference between clutter and efficiency. That requires a sharp and trained IT team.
So before you invest in yet more data storage, yet another data platform or dashboard tool, ask yourself this question: what do you really want with all your data? Do you really want to strike gold or are you accidentally filling your basement with tin?
The hidden costs of poor data
Bad data costs money. A lot of money. According to 2016 IDC research, the global cost of bad data and poor data analytics was a whopping $3.1 trillion per year. Not because of a few mistakes in an Excel sheet, but because of structural disruptions in decision-making, processes and customer relationships. And that was 9 years ago!
What makes bad data so expensive?
First of all: wrong decisions. If your management reports are based on outdated, incomplete or incorrect data, you are making decisions on quicksand. Wrong estimates of customer needs, incorrect forecasts, poorly timed investments - all possible because the basics are wrong.
Second, inefficiency. Employees who endlessly search for the right numbers, have to fix errors or juxtapose systems to resolve discrepancies waste valuable time. Even processes involving data (think logistics, billing, compliance) go awry or wrong if quality is lacking. And AI doesn't solve any of that so.
And then there's the damage to trust. If customers or partners notice that your systems are off the mark - wrong salutation, wrong delivery addresses, missing information - your credibility drops. And that ultimately translates into lost sales. You don't notice it so much at first, but that's a slow-rolling snowball that keeps getting bigger.
Specifically, what does that mean?
Say you run a company with annual sales of 100 million. Translating IDC's estimate (at the time ~18% of U.S. GDP), bad data could cost your organization up to 18 million annually. Even if you assume a more conservative rate of 5%, you're still talking about 5 million in missed opportunities, error margins and lost productivity.
Let me reiterate: if you think it's not that bad, read this sentence again: for a turnover of 100 million euros, bad data costs you 5 million euros annually.
So bad data is not just a technical problem. It's a strategic risk. And one that you simply cannot afford.
Data is growing exponentially. So are your costs.
Every day worldwide we produce about 2 exabytes of data. That's a 2 with 18 zeros. Or, something more concrete: two million laptop SSDs full. Per day. And again tomorrow. And again the day after tomorrow.
That amount is growing explosively - fueled by new technology like IoT, observability platforms, smart meters, connected devices and logging systems that want to capture everything, anytime, anywhere.
And all that data sounds great. Until you have to do something with it.
Because somewhere in that ocean is value. But to find it, you have to invest. In storage. In computing power. In tooling. In data models. And in people. Just selecting, validating and cleaning raw data, before any useful insight emerges, costs more and more.
Working data-driven is no longer a choice; it is necessary to stay relevant. But it has become an expensive necessity. And what companies sometimes forget: information alone does not generate revenue. Not if it doesn't convert into action. Not if it doesn't lead to optimization or innovation.
So the balance between cost and revenue is shifting. Where data was once a byproduct, it has now become a cost of magnitude. You're not just paying for the storage of noise, you're mostly paying for the illusion that it will ever be gold.
The question is not whether you should do anything with data. The question is: how much waste can you still afford? And when do you draw the line between value and ballast?
How to make data processing much more efficient AND cheaper
At Sciante, we see it time and time again: reprocessing data costs organizations far more time and money than it should. Not because they are doing nothing, but because they continue to work with tools and processes that are simply not designed for today's scale.
Many enrichment and transformation tasks are technically done correctly. But that which is functionally correct is often totally unoptimized for performance. Especially not when volumes explode, as we are seeing everywhere today. And that's happening more and more: because of IoT, logging, monitoring and other data-generating systems, more data is being added every day than ever before.
The tools vendors offer? Those are rarely designed with this pace and volume in mind. And because it is often unclear exactly how those tools work under the hood, they are rarely deployed in a truly efficient way. The result: sky-high cloud bills, slow processing and long waits for reports and insights.
With the right insight - into the tools, the bottlenecks and the optimization possibilities - things can be different. Much differently. And yield large to very large savings.
We have already helped dozens of organizations radically accelerate their data processing. By transforming smarter, taking fewer unnecessary steps and using tools for their intended purpose, optimization is often possible by a factor of 100 to 1000. Not an empty promise, but daily practice, proven time and again. That's what we do every day. Optimization is our business. Saving costs is the result we leave behind.
Want to know how much you can save on your data processing and how to get from raw data to actionable insights much faster?
📞 Schedule a no-obligation appointment with one of our experts.
We will show you where your biggest profit lies.
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