About the practice
Data Advisory builds the data foundations that make customer and audience systems actually work. Usually in places that have never had them before, and usually for problems the organization previously considered out of reach.
That work has included building the identity resolution and customer data foundations inside two of the largest global institutions in financial services and insurance, making precision targeting and decision-making possible where none existed before.
Data Advisory thinks in systems and has a low tolerance for tool adoption dressed up as strategy. It consistently asks what happens after the first version works. Engagements draw on a track record of building and leading teams of 20 or more, attracting talent across organizations, and developing people into leadership. The practice builds investment cases for programs organizations had never attempted, negotiates third-party data partnerships and software licenses, runs RFPs, and manages delivery against those commitments.
Data Advisory speaks and writes about the ethical dimensions of large-scale data and targeting systems, a thread that runs from before the Obama campaign through everything since. Its founder, Carol Davidsen, was named a top 10 person in digital politics by Business Insider, with coverage in the Times, Post, and Bloomberg.
Available for advisory and fractional engagements, and for speaking.
Services
Engagements range from a single evaluation or negotiation to a multi-year program, delivered directly or as fractional leadership embedded in your team.
Strategy & business case
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Digital data & AI transformation business case development
Building the funded, board-level case for data and AI investments an organization hasn't made before: scope, cost, risk, and the sequencing that moves a contested program to delivery.
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CRM, platform & third-party data licensing economics
Evaluating whether a CRM, platform, or third-party data license actually fits how the organization uses it — modeling the true cost per seat, per record, or per source — and running the RFP or renegotiation that fixes the gap when it doesn't.
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Data monetization & pricing strategy
Designing pricing models for data products, including multi-dimensional pricing tied to usage, access tier, and market coverage.
Architecture & governance
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Governance workflow design & development
Designing who owns each data attribute, how a record is matched, enriched, and approved, and how that process holds up across lines of business.
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Entity resolution & identity architecture
Building the matching logic and canonical record structures that let an organization count its own customers, accounts, or constituents for the first time.
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Master data quality & deduplication methodology
Finding and fixing duplicate and fragmented records at scale, then building the ongoing process that keeps the data clean after the cleanup project ends.
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Governed historical reporting & time-series KPI architecture
Designing point-in-time snapshots and segment history so a past metric can be reproduced and explained, not just a table restored.
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AI governance & guardrail design
Translating Risk, Compliance, and Architecture requirements into the technical controls — access, lineage, and approval — that make an AI system safe to put into production.
Vendor & commercial
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RFP & procurement management
Running vendor selection from requirements through contract, for CRM platforms, data warehouses, and third-party data sources alike.
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Data source evaluation
Assessing a prospective data partner or data product against its actual decision value, usable rights, and integration cost before it reaches a deal.
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Data partnership & licensing negotiation
Structuring the commercial terms of a specific data relationship: exclusivity, derivative rights, revenue share, and renewal leverage.
Delivery & leadership
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Project & program management
Running multi-year, multi-stakeholder data and AI programs from charter through delivery, across technology, risk, and business stakeholders.
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Fractional & interim data leadership
Serving as the interim data leader to keep a program or function moving during a search, reorganization, or leadership gap.
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Data organization design & team build-out
Structuring data and analytics teams, defining roles, and hiring the people who run them.
Enablement
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Data & AI literacy training
Training executives and staff to read, question, and act on data and AI systems responsibly, without needing to become technical specialists themselves.
Most engagements start with a conversation about what's actually blocking the decision. carol@dataadvisoryllc.com