Case Study
Building Data Systems & Governance for Scalable Microbial R&D
Aligning scientific integrity, data strategy, and organizational culture before scale
WHY — Data Integrity Is a Shared Responsibility
This engagement was driven by a fundamental belief that data strategy is not owned by a single team. It is a company‑wide responsibility that touches every role involved in generating, interpreting, communicating, and acting on data.
When I joined, the organization had no formal data strategy, no shared source of truth, and no common language for how data should be handled, trusted, or used. What existed instead was a growing desire—especially from leadership—for dashboards and high‑level reporting, without recognition that dashboards are downstream artifacts. They cannot exist without well‑organized data, aligned workflows, and intentional strategy upstream.
Beyond operational inefficiency, the deeper risk was erosion of scientific integrity. Without shared standards and alignment, data would inevitably fragment into silos, knowledge would walk out the door when individuals left, and results presented externally would lack consistency, professionalism, and credibility. Left unaddressed, this would have undermined both the science and the business.
The purpose of this work was therefore not simply to “organize data,” but to align the entire organization around data as a shared asset, embed integrity into how data moved through the company, and ensure that strategy, science, and business goals evolved together.
HOW — Strategy, Governance, and Literacy (Not Just Tools)
Guiding Principles
The data systems and governance model were built around several core principles:
- Data integrity before dashboards — automation, standards, and metadata come first
- Governance as enablement, not control — governance should reduce friction, not create it
- Everyone participates in data strategy — not just the data science team
- Literacy before tooling — people must understand data before they can use it well
- Data strategy must align with business strategy — designed in parallel, not retrofitted
These principles shaped both the technical systems and the cultural interventions that followed.
Data Governance Working Group
I founded and led a cross‑functional data governance working group composed of representatives from all major teams across the company. The goal was to ensure that:
- Pain points from every group were heard
- Trade‑offs were balanced across scientific, operational, and business needs
- Data priorities reflected real use‑cases, not abstract ideals
This group directly informed quarterly priorities for the data science team, helping decide which use‑cases, pipelines, reports, and systems were most critical at any given time.
Data Literacy & Culture Building
To reduce resistance and build shared ownership, I hosted multiple company‑wide and team‑specific data literacy workshops. These were intentionally designed to be:
- Accessible across technical backgrounds
- Grounded in real company examples
- Framed around collaboration rather than compliance
Workshops incorporated gamification and storytelling, including the use of avatars representing each major department, collectively navigating a narrative journey to defeat “data chaos” through communication, shared standards, and mutual understanding. This approach helped translate abstract concepts like governance, metadata, and data quality into something tangible and memorable.
Executive Alignment & Strategic Language
In parallel, I worked directly with the CTO to devise data strategy from the ground up. Completing the **UC Berkeley Executive Education program: **Data Strategy — Leveraging Data as a Competitive Advantage helped me bridge my academic and scientific background with executive‑level business thinking.
The program provided the language and framing needed to communicate data strategy in terms leadership understood—value, risk, scalability, and competitive advantage—while still protecting scientific rigor and integrity.
WHAT — Systems, Infrastructure, and Execution
With strategy and governance in place, I led the design and implementation of the company’s data ecosystem end‑to‑end:
- Established the entire data and cloud infrastructure from scratch
- Designed and implemented ELN/LIMS systems
- Built databases, automation pipelines, and reporting standards
- Defined data visualization and branding principles for internal and external outputs
- Introduced vetted third‑party AI tooling, including evaluation, recommendations, training, and integration into workflows
- Ensured compliance considerations (including CMMC‑aligned architecture) were addressed
There was no pre‑existing data strategy prior to this work. Every system, standard, and process was designed intentionally to support long‑term scale, reproducibility, and knowledge retention.
Leadership & Team Orchestration
Over three years, I led a fully asynchronous ecosystem consisting of:
- 1 contract company (4 team members)
- 1 sole contractor
- 4 undergraduate interns
- 1 full‑time employee
This work spanned three time zones, touched every functional area of the company, spanning business development, executive leadership, wet‑lab science, bioinformatics, and project‑based R&D teams that evolved and re‑formed over time, and required constant cross‑disciplinary communication between scientists, engineers, operations, and leadership.
The most persistent challenge—and greatest leadership growth area—was communication across disciplines: aligning people with radically different mental models, incentives, and vocabularies around a shared data vision.
OUTCOME — What Changed
Measurable Improvements
- Significant reductions in duplicated effort and rework
- Faster access to trusted, standardized data
- Improved consistency and professionalism in data shared with clients and partners
- Clear quarterly prioritization for data science efforts
- Reduced friction between teams generating and consuming data
Less‑Quantifiable but Critical Shifts
- Increased confidence in data quality and interpretation
- A shared organizational language around data
- Better alignment between scientific work and business goals
- Reduced reliance on individual memory as a knowledge store
The systems and strategy demonstrated what was possible when data integrity and governance are treated as foundational—not optional.
Who This Is For
This case study is for:
- CTOs without an established data organization
- Early‑stage biotech founders who know data matters but don’t yet know how to operationalize it
- NGOs and research organizations managing complex scientific data with limited resources
It illustrates that:
- Data strategy must start before scale, not after
- You don’t need a full data organization to begin—but you do need the right leadership
- Governance, literacy, and systems must evolve together
For organizations at this stage, the takeaway is often simple:
We don’t need a full team yet — we need someone who can design the system, align the people, and protect integrity while we grow.
This work now directly informs MicroMosaic’s Data Strategy, Governance & Leadership pillar, supporting organizations that want to build data systems that are not only scalable and efficient, but scientifically honest, human‑centered, and aligned with their mission.
Start a conversation