Overview
In this episode of The Dashboard Effect, Jon Thompson of Blue Margin sits down with Tania DiCostanzo, VP of IT, Strategic Initiatives, and Data Analytics at Trace3, for a conversation that traces the evolution of a data function from network engineering roots to a strategic capability serving a two billion dollar company. Tania’s account of how Trace3 has used data visualization, OKR frameworks, and external partnership to drive organizational alignment and operational efficiency is grounded in the specific decisions and trade-offs that shaped that evolution.
For data leaders navigating the challenge of building analytics capability inside a rapidly growing company, and for organizations evaluating how to balance internal team development with external partnership, this episode offers a candid and instructive account of what that work looks like in practice. See how Blue Margin’s Managed Analytics & Insights helped Trace3 accelerate speed-to-market and adopt the best practices that prevented the data smorgasbord problem Tania describes.
What This Episode Covers
The Role of a Data Mobilizer (3:55 – 7:04)
Tania’s path from network engineering to leading data analytics at a two billion dollar company is itself an instructive story about what data leadership actually requires. Her description of the data mobilizer role centers on the ability to take raw data and turn it into visual trends that change how people make decisions, a capability that is as much about organizational influence as it is about technical skill. The most valuable data leaders are the ones who can translate between what the data shows and what the business needs to do about it, and Tania’s career trajectory reflects that combination.
Strategic Alignment Through OKRs (12:30 – 13:20, 29:33 – 32:55)
After Trace3 was acquired by American Securities, the company adopted an OKR framework to create organizational alignment around standardized metrics. The initial implementation was deliberately manual: a dashboard that required data entry rather than automated data ingestion. That sequencing was intentional. Building the OKR structure and testing the alignment it produced before investing in full automation allowed the team to validate that the framework was working before making the infrastructure investment. It is a pragmatic approach to BI development that trades short-term convenience for long-term confidence in what is being built.
The Partnership with Blue Margin (17:16 – 21:30)
Tania’s account of the Blue Margin partnership is specific about what it delivered: augmentation of an internal team that could not scale fast enough on its own, improved speed-to-market, and access to best practices in dashboard design that prevented the data smorgasbord pattern of building dashboards full of metrics without a clear answer to the why behind each one. The partnership model she describes is not a replacement for internal capability but a complement to it, providing the specialized depth and available capacity that internal teams at the scale of Trace3’s data function cannot always sustain independently.
Operational Evolution from Financial to Sales Reporting (37:56 – 39:15)
Trace3’s reporting evolution follows a pattern that many growing companies experience: financial reporting comes first because it is what the business and its investors need most immediately, and operational reporting follows as the organization develops the data maturity to support it. Tania’s description of the 2023 goal of building an operational efficiency reporting package reflects the next stage of that evolution, extending the visibility the business has into financial performance to cover the operational dimensions that drive it.
The Federated Model as a Future Vision (42:30 – 44:10)
Tania’s vision for where Trace3’s data capability is heading centers on a federated model: standardized, curated datasets made available to citizen scientists and functional leads across the company who can generate context-aware insights within their own domains. The federated model distributes analytical capability while maintaining the data governance that keeps those capabilities producing reliable outputs rather than conflicting ones. It is an ambitious vision that builds on the organizational data maturity Trace3 has developed and points toward what the next stage of scale requires.
Who It’s For
This episode is worth your time if you are a data leader at a mid-market or enterprise company trying to build an analytics function that keeps pace with organizational growth without outrunning the governance and quality infrastructure that makes analytics trustworthy, an executive or operating partner at a PE-backed company navigating the organizational alignment challenges that come with post-acquisition integration and wanting a model for how OKRs and data visibility can support that work, a data team evaluating the right balance between internal capability and external partnership and wanting a practitioner’s account of what that balance looks like at a company that has navigated it successfully, or any organization that has experienced the data smorgasbord problem and wants to understand how a more disciplined approach to dashboard design prevents it.
Why It’s Worth a Listen
Tania DiCostanzo’s perspective is valuable because it spans both the organizational and the technical dimensions of building a data function, and because she is willing to be specific about the decisions that worked and the patterns that did not. The data smorgasbord framing, building dashboards full of metrics without asking why each one matters, is one of the most common and most correctable failure modes in BI development, and hearing it described by someone who has both experienced it and developed a practice for avoiding it makes the guidance more credible than a theoretical best practice would be.
The manual-before-automated OKR implementation is worth particular attention for organizations that are tempted to build automation before validating that the underlying framework is working. The cost of automating a bad process is much higher than the cost of manually running a good one until you are confident it is worth automating, and Tania’s sequencing decision reflects a maturity about BI development that is less common than it should be.
And the federated model vision is useful for data leaders thinking about what the next stage of organizational data maturity looks like beyond centralized analytics. Distributing capability to functional leads without losing governance is the challenge that organizations at Trace3’s level of sophistication are navigating, and Tania’s articulation of what that model requires and what it enables is a useful reference point for anyone thinking about the same transition.
