Does It Really Have to Be This Complicated?
Rethinking Data Frameworks and Dashboards
I just sat through a data strategy briefing where an organization proudly walked us through their new data framework. Slide after slide detailed a sprawling spaghetti chart — a dizzying web of tools, pipelines, connectors, staging areas, and visualization layers. Every "cool" tool seemed to be represented. There were nodes for ingestion, enrichment, orchestration, governance, and delivery, each boasting logos from the latest cloud-native, AI-powered, buzzword-laden products.
It made me wonder: Does it really have to be this complicated?
The presenters, to their credit, were enthusiastic. They had clearly put time and energy into building a modern system. But the underlying message seemed to be: The more complex the framework, the more mature and forward-thinking we must be.
Unfortunately, that’s a dangerous misconception - one that the broader data community is starting to push back against.
Complicated ≠ Better
Two recent posts capture this emerging sentiment well.
Dylan Anderson recently reflected on how most modern data stacks introduce so many layers of abstraction that simply understanding where data comes from and how good it is becomes a monumental challenge. Instead of improving data quality, sprawling architectures often obscure the basics, making it harder to even define what "quality" should look like.
Daniel Beach asks a simple but provocative question: when did "complicated" become a sign of competence? In reality, every layer you add — every integration, every sync, every transformation - introduces risk, technical debt, and operational overhead. Sure, complexity is sometimes necessary, but it should be earned, not assumed.
The real flex in today’s data world isn’t how many tools you can chain together - it’s how few you can use while still delivering reliable, trustworthy, and actionable data.
Sometimes that means intentionally limiting your tech stack. Other times it means focusing maniacally on strong fundamentals - clean source data, tight governance, thoughtful modeling - before you even think about sprinkling in the latest AI-powered observability layer.
“Complexity is easy. Clarity is the real craft.”
Dashboards: When More Isn't Better
This mindset about complexity extends beyond the backend. It’s painfully obvious on the front end too, particularly with dashboards.
Another great piece circulating recently (shared by Yassine Mahboub here) lays it out perfectly: More dashboards aren’t inherently better. In fact, more dashboards often mean less insight.
We’ve all seen it:
25 dashboards for 25 different stakeholder groups.
Dashboards built "just in case" someone needs them.
Redundant KPIs and inconsistent metrics floating across countless reports.
The result? Dashboard fatigue. Users don’t trust the numbers, can’t find what they need, and eventually tune it all out. Worse, when leadership sees different dashboards telling slightly different stories, it erodes confidence not just in the data, but in the entire analytics team.
The post hits on an important distinction: Dashboards should be designed tools, not just delivered outputs. A good dashboard isn’t just a pretty collection of charts. It’s a decision support tool. It has a purpose. It has a clear audience. It highlights the few key signals that matter, not every possible piece of noise.
Back to Basics
So, how do we fight back against this "more is better" instinct in data frameworks and dashboards?
It starts with asking basic, even uncomfortable questions:
What decision are we trying to enable?
Who actually needs this data, and why?
What’s the simplest, clearest way to get them what they need?
What can we remove or simplify without sacrificing trust and usability?
Sometimes that might mean scaling back — pruning dashboards, consolidating redundant pipelines, or sunsetting tools that add little marginal value.
Other times, it means doubling down on craftsmanship: treating data modeling, quality checks, and user experience design as high-skill disciplines worthy of deep investment.
And maybe most importantly, it requires shifting the culture away from complexity as a virtue, and toward clarity, usability, and impact as the true north.
Final Thought
Walking out of that spaghetti chart briefing, I didn’t feel impressed. I felt concerned. It reminded me that in the race to modernize, it’s all too easy to mistake motion for progress.
But modern data teams don’t win by deploying the flashiest tech stack or spinning up the most dashboards.
They win by enabling better, faster, more confident decisions - using the least amount of complexity necessary to get the job done.
That’s the real mark of a mature, forward-thinking data organization.
If you're ready to cut through the noise and turn complex data into knowledge that matters, Storm King Analytics can help.
Learn more at www.stormkinganalytics.com.