From Numbers to Actions: Making Data Work for Companies | by Michał Szudejko | Aug, 2023

What flops and what works?

Michał Szudejko
Towards Data Science

Today, organizations and individuals are swamped with data. Each day, 329 million terabytes of data is produced globally, amassing a whooping total of 120 zettabytes per annum [1].

But what does that mean in tangible terms? Consider an iPhone with 128 gigabytes of storage. Let us imagine that every day, the equivalent of three billion iPhones is packed with data*. Sounds impressive? Let’s delve deeper. This daily figure extrapolates to a theoretical 1.08 trillion iPhones overloaded with data annually. Given the current global population of approximately 7.8 billion, this means each person would need to possess nearly 139 iPhones [2]. Absurd, right?

The sheer volume is staggering, but the growth rate is equally astonishing. Just 13 years ago, in 2010, the annual data creation stood at a comparatively modest two zettabytes…

That’s just scratching the surface. Consider the data that never make it online — files stored directly on our devices or notes and documents penned on paper (yes, that’s still a thing!). Estimating that volume?

I wouldn’t even venture a guess.

Image of iPhone sunking in data
No iPhone can take it. Source: image by author, generated in DALL-E 2

So, there’s a ton of data out there.

But what does this mean for businesses?

I recently reviewed the latest Data and AI Leadership Executive Survey [3]. The results showed that 97% of companies have already invested in data and related infrastructure. 92% have put money into big data and artificial intelligence. You’d think this means they’re seeing returns on these investments. Not quite. A mere 40% of those surveyed said they view data as a ‘revenue-generating’ asset.

Only 27% of these companies consider themselves data-driven organizations.

What? Why?

The main issues are twofold. While some problems stem from technology, a massive 92% arise from human factors like organizational culture, people, and processes. Even tech issues often boil down to human errors.

This might surprise some. I once saw a statement that mistakenly said “decision-driven

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This post originally appeared on TechToday.