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🎯 In the firing line.
SaaS sales folks have done a number on the data industry. They’ve convinced us (and our business stakeholders) that the only thing between you and “data driven excellence” is their shiny new tool.
Millions upon millions are spent every year at conferences and expos designed to put data leaders and their juicy budgets firmly in the scope of tech sales teams.
Today it’s AI FOMO, a few years ago it was self-service BI, before that, Big Data.
Each of these waves have reframed their associated tools and tech as the goal themselves, rather than as a means to improve decisions, strategy, or performance.
And data leaders, as well as their business counterparts, have taken the bait, hook, line and sinker.
The problem this presents is that we find ourselves on a technology treadmill that’s just getting faster and faster. Constantly taking our attention away from the real reasons we invest time, energy and money in data: to deliver strategic outcomes for our businesses.
Today I’m taking a look at how we got here, and how the hell we get back!
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The real cost of Shiny Object Syndrome
Let’s talk about what this tech-first obsession actually does to you, and your organisation. This is not a neutral distraction. It comes with a huge bill (and I’m not just talking about the massive SaaS fees!)
Quick Poll
Be honest. What usually drives data tool decisions in your org?
Let’s take a look at the impacts of this:
🛍️ Strategy gets replaced with shopping
Instead of asking “what decisions matter most this year?”, conversations move towards “what stack should we be using?”
Data strategies become nothing more than vendor comparison exercises. You end up optimising the architecture before you’ve agreed what success and progress for the business even look like.
🏭 Data teams become feature factories
Teams get pulled into building capabilities nobody has clearly asked for, because “it’s foundational” or “we might need it later”.
You ship dashboards, models or pipelines, but struggle to point to either their strategic purpose or any decisions that actually changed because of them.
😵💫 Executives confuse activity with progress
New tools create the illusion of momentum, “we’re busy building!”
Budgets get approved and demos look impressive. But meanwhile the underlying problems remain unchanged because nobody managed to define them properly. ROI remains elusive (let’s hope no one brings it up!)
😰 You lose credibility when value stays vague
After the third or fourth “transformational” platform fails to transform much of anything, trust in data teams is completely destroyed.
Suddenly data feels too expensive, hard to explain, and utterly disconnected from strategy. And this is where heads start to role. Is there any wonder why CDOs have the shortest tenures in the C-suite?
The good news is this is all fixable. You just have to step off the damned treadmill.
Breaking the techno-centric mindset.
Most data initiatives fail because they are framed as technical work instead of decision support. Fixing that requires changing how work gets defined and approved.
📐 Define the decision first
Every data initiative should start with a clear statement of the decision it supports.
👉🏼 Who makes the decision?
👉🏼 How often they make it?
👉🏼 What information do they rely on today.?
👉🏼 What is missing or unreliable?
If you cannot answer those points, the initiative is not ready to move forward.
🪢 Tie the work to the org strategy execs have already laid out
Describe the expected impact using measures and KPIs that underpin the strategic pillars the business is trying to drive forward.
At the highest level it is typically: Revenue growth, cost reduction, risk exposure, operational efficiency, customer retention etc.
If the benefit only makes sense when explained alongside a specific tool, then the link to value is too weak and the strategic alignment is NOT there.
👩🏽💻 Create space between problem understanding and solution design
Technical teams usually move quickly to implementation. That speed often can hide poor problem definition.
👉🏼 Slow this phase down deliberately.
👉🏼 Test assumptions.
👉🏼 Compare alternatives.
👉🏼 Ask what happens if nothing changes?
Most organisations over-invest in tools because no one challenges this initial framing. make that YOUR job.
Select technology based on its ability to support decisions and strategies
Once the desired outcome is clear, technology choices become far more straightforward.
👉🏼 Will it improve the quality of the decision?
👉🏼 Will it reduce the time to make it?
👉🏼 Will it reduce effort or cost in a measurable way?
👉🏼 Will it help us achieve a strategic goal?
If the answer is unclear, the technology is not the priority.
Good luck out there!
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I hope this has given you a concrete way to push back on tech-led distractions and to lead with strategic outcomes in your tech evaluation process.
Remember, instead of asking “what else could we build?”, shift the conversation to “are these the right business priorities?”
That’s how data teams stay aligned to strategy and avoid the next tool wave.
📨 Forward this to your Head of Data (They might need it!)
💡 If this week’s topic resonated, the Business and Data Strategy Workshop might help.
A focused two-day workshop designed to align data and business priorities. You’ll identify how your data function supports organisational goals and the strategic opportunities that create real value.
→ Learn more about the Business and Data Strategy Workshop
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Tristan Burns
⚡️ Previous poll results
Last week I asked you: What do you think exec teams care about most in data conversations?
Here’s how you responded:
