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title: “The 80/20 Rule of Data Science: Focus on What Actually Moves the Needle” description: “Most data science effort gets wasted on work that sounds impressive but doesn’t deliver business value - here’s how to focus on what matters.” date: 2024-10-20T10:45:00-04:00 draft: false tags: [“Data Science”, “Leadership”, “AI Strategy”, “Business Value”, “Pragmatism”] category: [“Data Science”, “Strategy”] image: “/img/head/8020.png”

AI Summary

Simple solutions often outperform complex models in real business impact - like how basic inspection scheduling beat predictive modeling for substation maintenance
The difference between successful projects and academic exercises is stakeholder buy-in and clear business metrics, not technical sophistication
Data engineering and clean pipelines matter more than algorithm optimization - mediocre models with good data beat sophisticated models with garbage inputs
Speed beats perfection - ship early, iterate based on feedback, and know when “good enough” extracts the business value

The Million-Dollar Mistake I Almost Made

Last year, my team was burning through millions on monthly substation inspections. The obvious data science solution? Build sophisticated predictive models for each asset class to optimize inspection schedules. We started down that path - different models for transformers, breakers, switches. Complex stuff.

Then someone asked a simple question: “What if we just inspect substations when we’re already there for repairs?”

That basic insight saved us more money than any model we could have built. Instead of prediction algorithms, we implemented a simple rule: use existing maintenance visits as inspection opportunities. No ML required. Millions saved.

This pattern plays out constantly in data science. Teams get lost optimizing models to death while simple solutions sit right in front of them. After watching this happen across multiple organizations, I’m convinced that 80% of data science effort gets wasted on work that sounds impressive but doesn’t move the business forward.

What Actually Matters (The 20% That Drives Results)

The uncomfortable truth? Most business problems don’t need cutting-edge machine learning. They need good judgment about what’s worth solving and the discipline to keep solutions simple.

Simple Models Win Most of the Time

I’ve lost count of how many times basic logistic regression has outperformed elaborate neural networks - not just in accuracy, but in actual business impact. Simple models are easier to explain, faster to deploy, and way less likely to break mysteriously six months later.

A decision tree that a business user can actually understand will get used. A black-box model requiring a PhD to interpret sits unused, no matter how accurate.

Clear Success Metrics Save Everything

The most successful projects I’ve led started with a crystal-clear answer to “how will we know this worked?” Not “improved model performance” or “better predictions,” but actual business metrics:

“Reduced customer churn by 15%”
“Saved $50K in operational costs”
“Cut inspection expenses by 40%”

If you can’t articulate the business impact in one sentence, you’re not ready to build anything.

Data Engineering Is Your Secret Weapon

Here’s what they don’t teach in bootcamps: time spent on solid data pipelines pays off way more than algorithm tweaking. A mediocre model with reliable, clean data beats a sophisticated model with garbage inputs every single time.

I’ve watched teams waste months debugging “model performance issues” that turned out to be data quality problems. Build the infrastructure first. The fancy stuff can wait.

Getting People to Actually Use Your Work

The graveyard of data science is littered with brilliant models nobody used. The difference between successful projects and academic exercises? Usually stakeholder buy-in, not technical sophistication.

This means:

Spending time with business users
Understanding their actual workflow
Building things that fit how they already work
Accepting that revolutionary insights requiring complete process changes rarely get adopted

Speed Beats Perfection

I’d rather have a working prototype next week than a perfect solution next quarter. Fast iterations let you validate assumptions, get feedback, and course-correct before investing months in the wrong direction.

The best teams I’ve worked with ship early and often. They put imperfect solutions in front of users because real-world feedback beats theoretical optimization every time.

The Time Wasters (The 80% That Feels Important But Isn’t)

The Accuracy Trap

Every data scientist has fallen for this. Your model works well, but you’re convinced you can squeeze out just a bit more performance. So you burn days tuning hyperparameters, trying different algorithms, engineering new features.

But going from 89% to 91% accuracy rarely translates to meaningful business impact. That time could have solved an entirely new problem.

I learned this the hard way building threat detection models. We spent weeks implementing sophisticated sentence structure matching, shingling algorithms, and complex distance metrics. The first iteration with simple keyword extraction would have caught 90% of what we needed with 10% of the effort.

MLOps Theater

Good MLOps practices have value. But I’ve seen teams spend months building elaborate CI/CD pipelines for models that retrain quarterly. The tooling becomes more complex than the actual problem.

Build automation where it saves real time. Don’t build it because it looks good in architecture diagrams.

Notebook Sprawl

Every ad-hoc analysis living in a one-off Jupyter notebook is technical debt waiting to explode. Someone will need that analysis in six months, but the notebook won’t run because:

The environment changed
The data moved
The person who wrote it left

Standardize workflows. Build templates. Make things reproducible from day one, even when it feels like overhead.

Solutions Looking for Problems

This might be the biggest trap. You learn some cool new technique and suddenly you’re hunting for places to apply it. But starting with technology instead of business problems almost always wastes effort.

The best projects start with pain points people already feel, not algorithmic innovations that might be useful someday.

How to Tell When You’re Off Track

Your projects never finish. Constantly finding new optimizations? You might be avoiding the harder work of actually deploying something useful.

Nobody uses your outputs. If models and dashboards aren’t changing decisions, something’s fundamentally wrong.

You talk more about tools than outcomes. When discussions focus on technical architecture over business impact, you’ve lost the plot.

Leadership keeps asking “when?” If you can’t give clear timelines, you’re probably working on problems too vague or ambitious.

The Art of Knowing When to Stop

Here’s my personal test for “good enough”: Can we extract business value right now?

Once you hit that point, pause. Look at what’s left on the table. Is the remaining value worth the effort? Usually, it’s not.

This isn’t about lowering standards. It’s about recognizing that perfection is often the enemy of impact. That extra 2% accuracy won’t matter if the solution ships three months late and users have already found workarounds.

Redirecting Leadership Energy

Leadership often doesn’t grasp what’s realistic or valuable in data science. They push for complex solutions because they sound impressive, or demand unrealistic timelines without understanding constraints.

Translate everything into business language. Talk about customer retention, not model accuracy. Operational efficiency, not feature engineering.

Show quick wins early. Don’t wait six months to demonstrate value. Find something useful to deliver quickly, even if it’s not the final solution.

Be honest about complexity costs. Every additional sophistication layer makes things harder to maintain, debug, and explain. Make sure leadership understands this trade-off.

The Discipline of Doing Less

The hardest part of effective data science isn’t building complex models - it’s having the discipline to stop when you’ve solved the actual problem. This means:

Saying no to interesting side quests
Avoiding perfectionism
Focusing relentlessly on business outcomes

The most successful data scientists I know aren’t necessarily the most technically sophisticated. They have good judgment about what’s worth building and the communication skills to get their work adopted.

They build simple things that work reliably. They ship early and iterate. They spend as much time on adoption as algorithms.

Most importantly, they recognize their job isn’t building elegant technical solutions - it’s helping the business make better decisions with data.

The Bottom Line

Teams focusing on simple, practical solutions consistently outperform those chasing technical sophistication for its own sake. This isn’t about lowering standards - it’s about strategic investment of limited time and energy.

Before starting your next project, ask: “If this works perfectly, what specific business outcome improves, and by how much?”

If you can’t answer clearly, you’re about to join the 80% of effort that doesn’t move the needle.

The goal isn’t impressive models. It’s solving real problems in ways people will actually use. Everything else is just an interesting academic exercise.

The best data science isn’t the most complex - it’s the most useful. Focus on impact, not sophistication.