Common Problems Starting With Data

You’d think by now we’d all agree: data is important. Like, "seatbelt on a rollercoaster" important. But every year, I chat with hundreds of businesses who are just starting out on their data journey, and let me tell you, the stories I hear range from "oops, we didn’t think we needed it" to "help, we hired a freelancer who ghosted us after building something only they understand."

Here’s the thing: many companies don’t prioritise data until it’s too late. Why? Because it’s hard to justify the cost upfront. You’re growing, the budget’s stretched thin, and the last thing anyone wants to hear is “Can we allocate some of this to building a data pipeline?”—especially when growth looks like the sexier, shinier priority. But spoiler alert: it’s a false economy.

Why Data Gets Deprioritised

First off, let’s talk numbers. Here’s what happens to companies who don’t immediately embrace their inner data nerd:

• 60% say, “Nah, not now,” and push data to the bottom of the to-do list because of budget constraints. Of these, a third come back to 173tech 18 months later with a sheepish look and a cry of, “We grew fast, spent money on stuff that didn’t work, and now we wish we’d started with data earlier.”
• 10% go for SaaS tools—those all-in-one magic beans promising to solve all their data problems—but end up frustrated and switching after a year because, surprise! They’re not bespoke enough for their needs. In the past we did genuinely recommend these tools as a good place to get started, but after reviewing this - not so much any more.
• 10% hire a freelancer, which works fine until the freelancer leaves, taking all their undocumented expertise with them. (Fun fact: 45% of these businesses end up starting from scratch later. Ouch.)
• 6% hire an agency that isn’t us, and 20% of them come back to us later. (Not to toot our own horn, but toot toot.) This is a trend we only saw in 2024 though - clearly AI has sparked more agency growth and more people who don't really know what they're talking about.
• 5% hire internally, which is fantastic… until two years down the line when they realise that one person can’t scale a data stack solo, especially if it’s built like a digital Jenga tower.

Moral of the story? Everyone thinks they’re saving money by deprioritising data, but it’s a classic chicken and egg situation. You need data to prevent wastage as you grow, but you also think you need to grow before you can justify spending on data.

The Chicken, the Egg, and a Side of Regret

Speaking of chickens and eggs, the biggest problem isn’t just budget. It’s the mindset. Companies get laser-focused on growth, which is fair—who doesn’t want to become the next big thing? But here’s the rub: scaling without a solid data foundation is like building a house of cards in a wind tunnel.

Let’s say you’ve spent all your money on ads, new hires, and expanding operations. But without data, you don’t know which ads work, which customers are worth retaining, or how to streamline operations. Fast-forward six months, and you’re left with regret, technical debt, and a deep desire to time-travel back to the day you said, “Let’s just use the in-tool reports for now.”

Oh, and here’s a fun stat: 43% of IT decision-makers cite budget as the biggest barrier to leveraging data, even at the enterprise level. So, if you feel bad about putting it off, don’t worry—big companies mess this up too.

So, What’s The Solution?

If you’re starting your data journey, here’s some advice (sprinkled with the wisdom of hindsight):

1. Start Small, but Start Smart

If you’re early in your journey and can’t afford a full-blown data stack, at least set up some basic visibility. Think of it as the digital equivalent of getting glasses: even a blurry understanding of your key metrics is better than being blind.

2. Don’t Overcomplicate Things

You don’t need a unicorn hire who can do everything (because they probably don’t exist, and if they do, they’re expensive). Instead, focus on hiring or partnering with people who can help you build something scalable and modular. Remember: Rome wasn’t built in a day, and neither is a good data strategy.

3. Think Strategy, Technology, and People

• Strategy: Nail down your business use cases. Map out your customer journey. Know what problems you’re solving before you buy tools or hire anyone.
• Technology: Choose tools that are scalable, secure, and fit your data volumes. Oh, and don’t skimp on privacy—unless you enjoy GDPR-related headaches.
• People: Build a team that works collaboratively. A data engineer and an analyst together can achieve more than one overworked “jack-of-all-trades” ever will.

A Little Tough Love

I’ll leave you with this: no one regrets starting their data journey too early. Plenty of businesses regret starting too late. And the ones who don’t?

So, let me channel my inner motivational coach: invest in your data now. Your future self—and your wallet—will thank you. Or, you know, you can always call us in 18 months when regret sets in. I’ll be here, probably sipping gin and nodding knowingly.