Business Outcomes First

You need to get somewhere 50 miles away. If you ask a running coach how to get there, they’ll hand you a 12-week ultramarathon training plan. Ask a professional cyclist, and they’ll map out the safest route with elevation details and gear recommendations. But ask an Uber driver? They’ll just say, “Hop in.
Same destination. Drastically different approaches.

One gets you there today. The others might get you there eventually — if you survive the training.

Stop Complicating What’s Simple

“Focus on business outcomes, not tools” sounds obvious. Yet most technology implementations fail because they flip this equation. We make things more complicated than they need to be. We obsess over tool selection before we’ve defined what we’re trying to accomplish.

It’s like watching someone trying to balance a hard-boiled egg on its end. They build elaborate contraptions with popsicle sticks and hot glue, spending hours on increasingly complex solutions. Meanwhile, the kid who taps the egg on the table to flatten its bottom slightly completes the task in seconds.

The objective was to make the egg stand upright. Nobody said it had to remain intact.

It’s About the House, Not the Hammer

We see this pattern when teams implement technology like Palantir Foundry.Teams get caught in endless debates about features, capabilities, or technical specifications while losing sight of why they started the project in the first place.

The obsession becomes the hammer, not the house.

Data tools don’t create value. They enable value creation when aligned with clear business objectives. That fancy dashboard showing real-time metrics? If you can’t implement the insight it’s trying to show you, it’s just an expensive infographic stuck on your computer.

How We Turn Data Chaos Into Business Clarity

Many companies position themselves as Palantir experts. The landscape breaks down roughly like this:

Some consulting firms (typically the bigger ones) see Palantir as just another tool in their bag — one hammer among many. They’ve got fifteen different hammers and will happily sell you whichever one you ask for. Others, often staffed by former Palantir employees, position Palantir as the only tool you’ll need.

We take a different approach because we’ve been in your shoes. We’ve owned the problems you’re trying to solve. We’ve had to justify the ROI. We’ve been accountable for building and selling the house, not just swinging the hammer.

Our concern isn’t what’s in the tool bag — it’s the house we’re building. And while Palantir is the right tool for 80% of data transformation projects, it’s that final 20% where many implementations falter.

When all you have is a hammer, every problem looks like a nail. But sometimes you’re dealing with screws, and no amount of hammering will get the job done right.

Ask About Outcomes, Not Features

Our consultative approach begins by asking, “What levers do you have to pull? What behaviors are you trying to change in your operations?”

This question surprises many clients. They expect us to immediately dive into data architecture or system requirements. But fancy algorithms don’t help companies save or make money. Behavior change does.

The files might be “in the computer,” as Derek Zoolander would say, but no value is created until someone gets them out and changes their actions based on what they learn.

It boggles our minds how often teams ignore this fundamental truth. They get caught up in code quality, AI capabilities, and technical elegance while forgetting that none of it matters if it doesn’t change how people work.

Real Results: Turning E-Waste Decisions Into Dollars

We worked with an e-waste processing company that had a classic high-frequency decision problem: For each lot of electronic waste they acquired, they needed to decide whether to sell it immediately or process it for component recovery.

Before finding us, they’d tried every other analytics solution on the market. They’d been through the entire tool catalog looking for the right hammer.

But their problem wasn’t tools — it was decision confidence. They needed to ask, “Will we make more money selling this lot now or processing it first?”

Making this calculation required integrating rapidly changing market data with internal processing capabilities and costs. The underlying data was messy, spread across systems, and constantly shifting.

We started by framing the exact decision they needed to make. Then we worked backward to identify the data points required for confidence in that decision. Only then did we implement Palantir — not because it was the shiniest hammer, but because its ability to create a unified operational data layer made it the right tool for this specific house.

The result? A system that delivers clear sell/process recommendations with each option’s financial impact clearly displayed. Operators make better decisions faster, and management easily tracks the value each choice creates.

Speak Business, Build Technical — Not the Other Way Around

This approach requires a different kind of team. We pair technical leads with business leads on every project. While the engagement director focuses on communication and problem deconstruction, the technical lead listens and translates operational realities into logical models.

We never start by asking for join keys between data sets. We start with outcomes, then walk through the problem with operational folks until they naturally reveal the technical requirements.

“When I look at this ticket over here with this ID, and that ticket over there with that ID, I know they’re the same because of this field.”

That’s a businessperson giving you a primary key join without realizing it. They’re speaking their language, not ours. That’s how it should be.

We embrace the concept of the Forward-Deployed Engineer — someone who knows both how to do something and why you do it. Until recently, those were treated as separate roles: business analysts wrote requirements, and technologists implemented them.

That’s a broken model. When the tool you’re using can eliminate 80% of the wasted effort needed with other platforms, our experts have the space for our teams to understand the “why” behind what they’re building.

Start Small, Win Fast, Then Scale

The biggest mistake companies make? Selecting the wrong use case to start.

The best initial use case has two characteristics:

It happens frequently.
Each instance has a relatively small cost.

This combination means that over time, the small costs accumulate into significant value.

Take invoice deduplication. Only 5% of invoices may be duplicated, but catching those before payment prevents double-spending, supplier confusion, and administrative headaches from credit memos.

Starting here means you can demonstrate clear ROI within four to six weeks: “We stopped these duplicate invoices worth $X million from being paid.”

But because you solved this problem, you also created a data lake with your most critical information — purchase orders, vendors, employees, and invoices. Now, when you want to tackle that three-year capital optimization plan, you already have the foundation built.

The second pitfall? Not anchoring your use cases in dollars. If your metric doesn’t have money in either the numerator or denominator, it’s probably not the right place to start.

I don’t care that you made somebody 5% more efficient unless you can translate that efficiency into dollars. Making your employees happier might be valuable, but if you can’t draw a direct line between happiness and financial impact, don’t start there.

Why are we saying the quiet part out loud? Because this stuff is expensive. You’ll likely spend $350K+ annually on licenses and implementation. You need to generate at least a million dollars in either margin improvement or increased sales to justify that investment.

That’s why we’re confused when clients want to start with use cases like operational scorecards — “Did I drill this hole well?” Who cares? How much did the hole cost to drill? What decisions will you make differently based on that information?

You can create dashboards 15 different ways with Excel and Power BI. Don’t spend hundreds of thousands on a solution that doesn’t drive financial outcomes.

From “We Think” to “We Know” — Fast

Considering Palantir? Ask yourself these questions:

What specific decisions am I trying to improve?
How frequently are these decisions made?
What’s the financial impact of making these decisions better?
What behavior needs to change to realize that value?
What information would drive that behavior change?

Only after answering these should you consider which tools fit the job.

At RANGR, we don’t get points for deploying slick technology. We only succeed when the value we create exceeds the cost — when there’s a measurable return on investment. That’s why we focus relentlessly on business outcomes first, tools second.

We don’t think Palantir is the answer to every problem, but for the right problems, it’s the most effective way to cut through data chaos and drive real-world results. We know because we’ve done it — not just as consultants, but as operators who deliver on our promises.

Technology is just the Uber driver who gets you there. Turn your data chaos into business clarity.

Better decisions. Bigger outcomes.

Why Tools Don’t Matter Until You Know Why You’re Building