Overview
In this episode of The Dashboard Effect, Brick Thompson and Caleb Oaks examine the specific data challenges that mid-market companies face, and why the solutions designed for large enterprises tend to fit poorly while the lean approaches suited to smaller businesses leave too much value on the table. The conversation identifies the gap precisely and makes the case for a model that was designed for it rather than borrowed from either end of the market.
For mid-market leadership teams trying to figure out how to build a data capability that actually serves the business without taking on the overhead of a full internal department, this episode offers a clear and honest framework for thinking through the options. See how Blue Margin’s Managed Analytics & Insights delivers exactly the ongoing partnership model the hosts describe.
What This Episode Covers
The Mid-Market Gap (0:45)
Mid-market companies occupy an awkward position in the data landscape. They generate enough revenue and operational complexity to need mature data infrastructure, but they typically lack the internal data departments that large enterprises have built over years. Big data approaches designed for companies with massive datasets and dedicated data science teams do not translate cleanly to the mid-market context, and applying them anyway tends to produce overbuilt, underutilized infrastructure.
The Pitfalls of Building an In-House Team (2:07 – 3:11, 7:03)
Hiring specialized data talent is expensive, difficult to execute well, and creates a fragile dependency on a small number of individuals who carry disproportionate institutional knowledge. When those people leave, as they frequently do in a competitive talent market, the organization does not just lose headcount. It loses the context behind every architectural decision and the continuity of every ongoing initiative. For mid-market companies, building an internal team too early often creates more risk than it mitigates.
The Pitfalls of Project-Based Consulting (3:52 – 4:41)
Project-based consulting solves the initial implementation problem but creates a different one: the care and feeding problem. Once consultants deliver the project and depart, the organization is left maintaining something it did not build, often without the expertise to evolve it as the business changes. Momentum stalls, reports become stale, and the investment in the initial build depreciates faster than it should because the ongoing iteration that keeps BI relevant never happens.
The Department-as-a-Service Model (5:32 – 8:56)
The model the hosts advocate for is a subscription-based ongoing partnership that functions as an external data department rather than a project team. The key distinctions are continuity and iteration. A partner operating under this model maintains data pipelines, monitors performance, handles security and disaster recovery, and delivers regular updates to reports and data models as the business evolves. The work does not stop at delivery. It continues as long as the business continues to change, which is always.
Cost-Effectiveness and the Right Threshold for Internal Teams (11:15 – 11:51)
For most mid-market companies, an ongoing partnership is more cost-effective than full-time payroll when the total cost of internal ownership is accounted for, including recruiting, benefits, turnover, and the management overhead of running a specialized technical team. The hosts suggest that the natural threshold for when an internal team becomes the right evolution is around one billion dollars in revenue, a point where the scale and complexity of data needs justifies the investment in building that capability in-house.
Who It’s For
This episode is worth your time if you are a CEO, COO, or technology leader at a mid-market company trying to determine the right model for building a data capability without overinvesting in infrastructure the business is not yet ready to absorb, a CFO evaluating the true cost of internal data team ownership versus an ongoing partnership model, a PE operating partner or portfolio company executive assessing how to staff a data function across portfolio companies at different stages of maturity, or any organization that has tried project-based consulting, been left with something it cannot maintain, and is looking for a more sustainable alternative.
Why It’s Worth a Listen
The mid-market data challenge is real and consistently underserved by the conventional options. This episode names the problem with enough precision to make the alternatives genuinely evaluable rather than abstract, and the department-as-a-service framing is one of the clearest articulations of what an ongoing data partnership should actually deliver.
The care and feeding point is particularly worth sitting with for organizations that have invested in a project-based engagement and are now living with the consequences of that model’s limitations. The gap between a delivered report and a maintained one is where most of the long-term value in BI lives, and recognizing that gap is the first step toward closing it with a model that was designed to do so.
And the revenue threshold discussion is a useful calibration tool for mid-market leadership teams that are trying to decide whether they are at the right scale to invest in building internally. Having a concrete benchmark, even an approximate one, changes the conversation from a philosophical debate about build versus buy into a more practical question about where the business currently sits relative to the threshold that makes the internal model worth the investment.
