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Stop Talking, Start Calculating: The Data Leader's Guide to Proving ROI
- Christian Steinert
- Oct 21
- 8 min read
How to Turn "We Need Better Analytics" Into Hard Dollar Savings Executives Can't Ignore
Setting the Foundation
When beginning a data initiative, what is the objective of your company/client?
Where are they in the data maturity lifecycle?
What was the initial pain that brought you on board to help?
How is data being used/going to be used as a growth driver for the company?
What is a solution that speaks to a significant problem that ties directly to the company’s value chain?
These are all foundational questions to ask. When speaking to executive leadership, money talks. The deeper I’ve gotten into data strategy consulting at Steinert Analytics, the more I’ve needed to understand the financial ROI of what we’re doing.
Without hard numbers, the intangible benefits of data WILL NOT resonate with leadership. Those are layered on as icing after you bake the cake. The cake is your ability to calculate and articulate the financial value of what data unlocks for your organization.
In this three part mini series, for the next three weeks I’m taking you through our process for calculating the financial ROI of a given data solution. We’re going to keep the focus on one data product/deliverable. My goal is to keep this straight-forward and actionable for you. I’m pulling from my experience as a healthcare-focused Chief Data Strategist & Consultant. There’s no bull shitting here.
I’ll be giving you the inside details on what I’ve done and how it’s been received so far. I’m open to further discussion, feedback and improvements as we’re all data leaders trying to tackle this most difficult hurdle: Proving our value as a team.
Back to the Questions & Background Context
I opened with those five foundational questions. Why? Because high value data solutions always start with questions. As the strategic enabler of a business, the data leader needs to focus on solving a stakeholder’s problems.
I’ll share my responses on each of these and detail what I’ve seen with our clients at Steinert Analytics.
1. When beginning a data initiative, what is the objective of your company/client?
Is the company greenfield, or do they already have an existing reporting ecosystem (brownfield)? This is critical to understand. If the company isn’t currently doing reporting, why is that? Maybe it’s a smaller company that is satisfied with their current analytics mechanisms. For example, a small residential roofing company probably doesn’t need a fully scaled reporting practice. If you’re a data consultant, these might not be the best prospects for you. However, if it’s a $100M+ revenue medical company, you likely have far more people and departments that generate (a lot of) data - from multiple sources.
One of the biggest issues I’ve seen for an immature data lifecycle company is inconsistent values for the same metric across reports/departments. This is a big pain point for our ideal customer at Steinert Analytics.
2. Where are they in the data maturity lifecycle?
Similar to my above response, the maturity of their data practice will determine how you’re able to help them.
For an immature org, baselining their descriptive (what happened, when did it happen) reporting with consistent, quality data is a critical pain point.
A more mature data organization is looking to get enriching insights from their data. Instead of just descriptive and diagnostic reporting that helps make accurate, cost-effective decisions, they’re wanting to leverage data as a growth driver.
Can we predict what marketing campaigns will grow sales by 10% in Q4 2025 by looking at our historical customer data?
Are we able to leverage historical patient data in a HIPAA compliant way that enables the prediction of certain diseases for a particular patient profile?
You go from using data to run your company efficiently, thus saving money - to pulling data’s levers as a strategic growth driver for your business that grows customers and enhances the product/service experience.
3. What was the initial pain that brought you on board to help?
Always go back to this. It helps to see how your organization’s needs have evolved. The original issue is usually surface level. The more conversations you have unveils a new elephant in the room.
For example, when I started with one of our largest clients we were trying to alleviate the burden of their on premise data infrastructure by migrating to the cloud. Although that was important to limit IT labor hours, the real need surfaced much later. After our Proof of Concept phase, we got an inside perspective that the company’s entire financial reporting practice runs on manual processes.
Pulling from ten different legacy Power BI reports (directly hitting the EHR) and native EHR exports right in the UI, a centralized Excel spreadsheet was the source of truth. Hard coding numbers manually into the spreadsheet from all these sources, with no level of automation or standardized definition around each metric’s logic. There was no timing consistency on when the metrics would be updated daily.
4. How is data being used/going to be used as a growth driver for the company?
Keep your sights on this. I’d say the majority of our clients are too immature for discussions around data as a growth driver. It’s currently all about cost savings, efficiency and risk aversion, especially in the conservative healthcare industry.
It’s good to pepper this perspective in as icing on the cake you’ve baked early on in a company’s data maturity journey.
5. What is a solution that speaks to a significant problem that ties directly to the company’s value chain?
Pulling from an elite data architect and someone I admire, Ergest Xheblati’s playbook, always start with data solutions closest to a company’s value chain. That is, build data solutions that tie directly in with how the company makes money.
Why? This is the fastest way to gain traction with a CEO and how the data solution you’re building aids the company in managing their cash flow and profit (especially big in healthcare revenue cycle management!).
Start with Time Savings
Like I said, this article is designed to be specific and straight-forward. Sticking with the automation of a daily revenue cycle financial report, how do you begin to quantify this?
Start with time to dollars saved.
For one of our healthcare clients, we automated their daily revenue cycle management reporting. Before our solution, the VP of Revenue Cycle spent 90 minutes every single day manually pulling data from multiple Power BI reports and native EHR exports, then hard-coding numbers into a centralized Excel spreadsheet.
Ninety minutes. Every. Single. Day.
Dollar numbers are for this use case’s examples only. We cannot disclose actual dollar figures of our clients. But it’s ballparked well.
Pro Tip: Use an LLM of your choice for the below steps. I use Claude Pro’s Sonnet 4.5.
Here’s how we calculated the financial impact:
Step 1: Quantify the Time Investment
First, document the current state with precision:
Daily time spent: 1.5 hours
Working days per year: 250 days (accounting for holidays, PTO, weekends)
Total annual hours spent on this task: 375 hours
That’s over nine full workweeks spent on manual data entry and report compilation.
Step 2: Convert Time to Dollar Cost
Next, calculate what that time actually costs the organization. For our client’s VP of Revenue Cycle:
Annual working hours: 2,080 hours
VP salary converted to hourly rate: $72.12/hour
Annual labor cost of manual reporting: 375 hours × $72.12 = $27,045
That’s $27,045 per year spent on a process that could be automated. And this is just one person. If you have multiple team members touching this process, the cost multiplies quickly.
Step 3: Factor in Your Implementation Costs
Now here’s where consultants often get squeamish - you need to be honest about what this will cost to build:
Initial Investment:
Development time: $18,000 (6 weeks from our consulting package)
Annual technology costs: $8,500 (cloud infrastructure, Power BI licenses, API costs)
Total first-year investment: $26,500
Yes, the first year you’re in the red. And that’s okay. This is where you need to walk leadership through the payback timeline.
Step 4: Calculate Net Annual Savings
After year one, here’s what the financial picture looks like:
Annual labor savings: $27,045
Annual technology costs: $8,500
Net annual savings: $18,545
Step 5: Determine Payback Period
This is the number executives care about most - when do we break even?
Payback period = Initial development cost ÷ Net annual savings Payback period = $18,000 ÷ $18,545 = 0.97 years (roughly 11.6 months)
After less than a year, you’ve recovered your initial investment. Every year after that is pure value creation.
Step 6: Project Long-Term ROI
Don’t stop at year two. Show them the five-year picture:
5-Year Financial Projection:
Total investment: $52,000 ($18,000 initial + $34,000 in ongoing tech costs over 4 years)
Total labor savings: $135,225 (5 years of VP time saved)
Net 5-year benefit: $83,225
5-Year ROI: 160%
The Reality Check
Here’s what this calculation doesn’t capture, but you should mention to leadership:
Capacity Created: That VP now has 375 hours per year to focus on strategic revenue cycle initiatives instead of manual data entry. What’s the value of their expertise applied to reducing days in A/R or improving denial management? That’s opportunity cost recovered.
Error Reduction: Manual processes introduce errors. Every mistake in financial reporting requires correction time and can lead to poor decision-making. Automation eliminates the human error factor.
Scalability: As the organization grows, the manual process would require more FTEs. The automated solution scales without additional labor costs.
Employee Satisfaction: Nobody got into healthcare leadership to spend 90 minutes every day copying and pasting numbers into Excel. Reducing soul-crushing manual work improves retention and morale.
But remember - lead with the hard numbers. The intangible benefits are the icing, not the cake.
Wrapping Up Part 1
The foundation of any ROI calculation for data products starts with documenting what’s broken and what it costs right now. Time tracking is your best friend here. Before you automate anything, before you build anything, you need to establish your baseline metrics:
How many hours per week/month/year are being spent on this process?
What is the fully-loaded cost of those hours?
What will it cost to build and maintain the solution?
How long until we break even?
What’s the long-term financial benefit?
For our healthcare client, we could walk into the CFO’s office and say: “We’re going to spend $26,500 in year one to save you $27,045 annually in labor costs. You’ll break even in 11.6 months, and over five years you’ll see a net benefit of $83,225 with a 160% ROI. Plus, your VP of Revenue Cycle gets 375 hours back per year to focus on strategic initiatives that actually drive revenue.”
That’s a conversation executives understand.
📬 COMING NEXT WEEK: PART 2
Building the Business Case - From Costs to Value Creation
Next week, we move beyond time savings to calculate total value and present it to different stakeholders. We’ll walk through the complete ROI formula and explore value beyond time savings—faster decision-making, reduced turnover costs, and scalability benefits. Plus, I’ll show you how to frame ROI differently for your CFO vs. COO vs. VP of Operations.
Until then, start documenting your baseline metrics. Time those manual processes. Calculate those hourly costs. Build your foundation.
The math doesn’t lie, and neither should we.
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Christian Steinert is the founder of Steinert Analytics, helping healthcare & roofing organizations turn data into actionable insights. Subscribe to Rooftop Insights for weekly perspectives on analytics and business intelligence in these industries.
Also - check out our free Healthcare Analytics Playbook email course here.
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