From Cost Center to Growth Engine: Quantifying the Intangibles in Data & Tech
For decades, large enterprises have treated their data and technology functions as support utilities—departments built to deliver output rather than create measurable outcomes. Whether it was cranking out reports, maintaining infrastructure, or automating business processes, these teams were evaluated on how much they produced, not on what that production achieved for the business.
This mindset has come at a price. By focusing on volume (number of dashboards, code releases, migration tickets closed) rather than value (customer acquisition cost savings, revenue lift, churn reduction), organizations have inadvertently diluted the strategic impact of their data and technology investments.
With global data volumes projected to reach 181 zettabytes by 2025 (IDC) and tech budgets swelling to $5.06 trillion in 2024 (Gartner), the stakes are higher than ever. It’s time for a shift: from measuring activity to measuring outcomes. In other words, data and technology teams must transform from traditional cost centers into bona fide profit centers.
Why Output-Focused Measurement Fails
Let’s first unpack why an output-driven approach is insufficient:
- Misdirected priorities: A data team rewarded for publishing 100 dashboards might keep doing so, even if most of them gather dust
- Incentivized busywork: Engineering teams may prioritize features nobody asked for just to meet release velocity targets
- Disconnection from business strategy: Technology roadmaps become siloed, with no explicit link to revenue growth or customer experience
According to McKinsey, only 30% of data and analytics transformations deliver on their business objectives. The rest fall victim to measuring throughput instead of outcomes For example, how many models built instead of how much incremental margin those models drove.
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How to Transform Data & Tech Into Profit Centers
1. Reframe the Mission From Output to Value
First, change the narrative. Instead of “we ship dashboards”, a data team might redefine its mission to “reduce marketing spend waste by 20%”. A technology team might shift from “delivering features” to “reducing support tickets by 30% via self-service”.
Consider Spotify. Their data teams are measured on improving listening time, a business-relevant metric tied to subscriber retention. When they redesigned their recommendation algorithms, they didn’t celebrate lines of code shipped—they celebrated a 20% lift in listening hours, directly tied to churn reduction.
2. Build Outcome-Oriented Performance Frameworks
Adopt metrics that explicitly tie data and tech efforts to commercial outcomes. For example:
- Revenue generated through personalization models
- Cost savings via infrastructure optimization
- Uplift in Net Promoter Score (NPS) driven by new product features
- Reduction in fraud loss through ML risk models
An example of this is Delta airlines. Delta’s data science team helped build an algorithm that predicts the likelihood of a flight delay, proactively rebooking travelers before they even arrive at the airport. That initiative reduced compensation claims and saved $50 million annually, according to published Delta operations data.
A Deloitte survey in 2023 found that organizations with outcome-linked data teams were 2.4x more likely to exceed their profitability targets.
3. Embed Data & Tech Into Commercial Decision-Making
Historically, data scientists and engineers worked downstream of business strategy. Flip that. Bring these teams upstream into growth, pricing, and product discussions.
Netflix, for instance, uses data engineers directly in its content acquisition process. Before buying streaming rights, they forecast audience size and subscriber impact using historical watch data. For House of Cards, for instance, Netflix invested $100 million based on audience models that predicted a global hit. The bet paid off, fueling a wave of subscriber growth and setting a precedent for data-powered commercial decision-making.
4. Show a P&L Mindset
There’s a reason functions like sales or product enjoy a seat at the strategy table—they own revenue outcomes. Data and tech leaders can do the same by committing to “P&L impact” language:
- What is the estimated ROI of this data pipeline?
- How will this architecture decision reduce downtime costs?
- What dollar value will this ML model add to customer lifetime value?
American Express is a significant example of this. Their fraud detection models prevented an estimated $3.5 billion in fraud losses in 2023 alone. That is a direct P&L impact, as measurable as any top-line revenue driver.
Another relevant case study is UPS. Their ORION optimization algorithm for delivery routes reduced driver mileage by 100 million miles per year, saving $300–$400 million annually in fuel costs. That initiative turned a data science team into a profit-generating powerhouse.
5. Invest in Business Literacy for Tech Teams
Lastly, a hard truth: many data/tech teams aren’t trained to speak in “outcomes”. They’re comfortable with precision and throughput but less so with commercial storytelling. Upskilling programs that build business literacy and financial acumen can empower these teams to quantify their impact in terms that business leaders care about.
Take the case of Capital One. They established an internal “Tech College” to help engineers and data scientists learn business strategy, P&L thinking, and customer impact storytelling—boosting their influence in executive boardrooms.
In Closing
Data and technology teams are no longer sidekicks. They have the potential to become the profit engines of the enterprise, provided they stop measuring themselves by what they deliver and start measuring what they enable.
In a world where competitive advantages are fleeting, the ability to quantify the intangible, how data and technology drive revenue, cut costs, and delight customers, is what will separate tomorrow’s winners from the also-rans. It’s time to move from counting outputs to capturing outcomes and transform the back office into the growth engine.
NOTE: The views expressed in this article are those of the author and not of Emeritus.
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