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If We Say Data Product Delivery Can Be
75 Percent More Efficient, We Should Be Prepared to Test It
Executive Summary
The Case Under Examination
This article examines whether governed data product delivery effort can realistically reduce by seventy-five percent through structural change, not through aspiration, but through decomposition, modeling, and independent validation.
The model was independently stress-tested by an experienced Collibra Ranger with more than fifty enterprise deployments across regulated environments. His analysis decomposed delivery effort into its operational components, embedded governance at the point of decision, modeled reuse multipliers, evaluated three-year compounding returns, and tested sensitivity under conservative assumptions.
The question is not whether the numbers appear compelling. The question is whether they withstand scrutiny.
Key Dimensions Tested
  • Effort decomposition across delivery phases
  • Governance embedded at the point of decision
  • Reuse multipliers and compounding returns
  • Three-year economic projection
  • Sensitivity under conservative assumptions
Article
A Model Built to Be Challenged
Recently, we presented Latttice to a Collibra Ranger, someone who has worked across more than fifty enterprise Collibra deployments in regulated environments. There was no dramatic reaction during the session. Just careful attention.
Later that evening, we received an email.
He had taken the model we discussed and independently worked through the economics, recalculating effort distributions, challenging assumptions, and pressure testing throughput. He had clearly been motivated by what he had seen in the demonstration to explore the claims in greater depth.
That was precisely the kind of engagement I value.
Throughout my career I have worked deeply in data architecture, platform economics, specialty cloud sales, consulting, and startup environments. I understand engineering delivery patterns at scale. But governance has often entered those conversations as a compliance necessity rather than a structural design principle. It has traditionally been something we satisfy, not something we center.
This Ranger brought decades of hands on governance implementation experience to the discussion. His attention to detail, and his willingness to interrogate the economics independently, strengthened the conversation. It was not about proving a point. It was about improving the model.
To be challenged by someone operating at that level, particularly someone who had clearly been inspired to examine the claims rigorously, was valuable.
Because AI changes the stakes.
Governance is no longer a side consideration attached to a data project. When AI systems influence decisions in real time, governance must operate at the point of decision. It must move from compliance overlay to structural foundation.
To be clear, we had already worked through the numbers before stating that governed data product delivery effort could fall by seventy five percent. We are data practitioners. Over the years we have delivered complex enterprise initiatives across industries. The seventy five percent figure was not created in a marketing session. It was derived from a decomposed operating model built from the ground up and grounded in the realities of enterprise delivery.
But thoughtful challenge improves rigor.
And rigor matters.
Where 120 Hours Go
The one hundred and twenty hour baseline was not a single estimate. It was broken down across requirement clarification, data sourcing and mapping, engineering build, validation cycles, governance approvals, and rework. Rework was modeled explicitly. In traditional delivery models, it is not an anomaly. It is structural.
Across these six phases, the largest concentrations of effort sit in engineering build and rework, two areas where structural change, not incremental optimization, yields the greatest returns. Governance approval, while a smaller absolute number, carries disproportionate calendar-time impact due to handoff delays and sequential approval chains.
Why Governance Can No Longer Sit on the Sidelines
However, what made his feedback important was governance depth. Historically, governance in many data programs, including ones we have delivered, was often treated as a parallel function. Necessary. Compliance-driven. Sometimes bolted on at the end.
That model does not survive AI.[4]
When AI systems are making recommendations, influencing decisions, or automating processes, governance cannot sit on the sidelines. It cannot be an afterthought. It must operate at the point of decision. So while we had modeled the engineering efficiency, his perspective, grounded in deep governance implementation experience, strengthened the structural integrity of the model. It mattered.
At Data Tiles this mindset is shared across our leadership team. With my Head of Revenue, John Goode, who has been involved in data technology startups such as Zoomdata and brings commercial discipline to technical ambition, we expanded the model further. We revisited governance overhead, which in the model reduces by approximately twenty to thirty percent when governance is embedded within the data product rather than applied retrospectively. We stress-tested reuse assumptions. We tightened cost inputs. We reexamined throughput logic under conservative rates.
Then we asked the only question that matters: If we say this changes the economics of enterprise data delivery, does it stand up to scrutiny?
The Evidence from External Research
External research supports the inefficiency embedded in current delivery patterns. The numbers are not in dispute — what varies is whether organizations choose to act on them.
60–70%
Preparation Time
Analytics project time spent on data preparation and integration rather than analysis (Gartner)[1]
40%
Wrangling & Rework
Effort dedicated by data teams to low-value wrangling and preventable rework (McKinsey)[2]
$3.1T+
Global Cost
Annual cost of poor data quality and inefficient processes to organizations worldwide (IDC)[3]
From 120 Hours to 30: The Structural Shift
The Latttice model assumes delivery effort can fall to approximately thirty hours per product. This reduction is structural, not aspirational. Configuration replaces bespoke pipeline engineering. Governance is embedded within the product rather than layered on after build. Governed data assets become reusable rather than disposable.[5]
Traditional Model
120 Hours per product
Bespoke build, manual governance, limited reuse
Structural Model
30 Hours per product
Configuration-driven, embedded governance, governed reuse
The Reuse Multiplier
Reuse is central to the economic case. In the model, a single governed data product may feed five to twenty downstream use cases. That reuse multiplier drives compounding returns. By Year Three, modeled savings increase by approximately twenty-five percent compared to Year One — driven by reuse rather than incremental headcount reduction. Under conservative assumptions, payback is achieved within the first year.[7]
Capacity Is Leverage
When converted into capacity terms using one thousand eight hundred and fifty hours per full-time equivalent annually, the numbers become concrete. One hundred and fifty products per year equates to approximately seven full-time equivalents of capacity. Four hundred products per year equates to approximately nineteen.
This is not a headcount reduction argument. It is a leverage argument.
Gartner research[1] shows that more than seventy percent of organizations cite data team bottlenecks as a barrier to AI execution. The constraint in most enterprises is not the number of skilled professionals. It is the proportion of their time consumed by repetitive build cycles, manual governance approval loops, and preventable rework.
48K
Traditional Hours
Total hours required to deliver 400 data products at 120 hours each
12K
Optimized Hours
Total hours required to deliver 400 data products at 30 hours each
36K
Hours Released
Equivalent to approximately 19 FTEs of capacity redirected to higher-value work
Releasing capacity changes what those teams can focus on. Engineering effort can be redirected toward AI enablement, advanced analytics, domain architecture, and platform resilience, the work that differentiates an enterprise, rather than the work that merely sustains it.
The Conditions That Must Hold
That said, the seventy-five percent reduction is not automatic. It assumes genuine structural change. Layering new tooling onto unchanged delivery patterns will not move the economics. If governance remains manual and approval-driven, overhead will persist. If reuse does not occur because ownership and discoverability are weak, compounding returns will not materialize.
Configuration Over Custom Build
Bespoke pipeline engineering must give way to configuration-driven product assembly. Without this shift, engineering hours remain fixed.
Governance at the Point of Decision
Governance must be embedded within the data product itself, not applied retrospectively through manual approval chains.[4]
Data Products as Assets
Products must be treated as governed, discoverable, reusable assets, not disposable outputs of one-time projects.[5][7]
The Deeper Question
Even under sensitivity scenarios where the reduction is materially lower, sixty percent rather than seventy-five, the economic shift remains transformative. The deeper question is not whether seventy-five percent sounds ambitious.
It is whether the current engineering-heavy delivery model remains economically defensible in an AI-driven environment.[6]
If we promise an outcome, it must withstand examination. If the model fails under pressure, it should be refined.
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Cameron Price.
References
References