What does a fractional Chief Data Officer do?

A fractional CDO is the executive voice on data and AI strategy — not a data engineer, not a data scientist, not an analytics lead. The work covers data platform decisions (Snowflake vs. Databricks vs. open source), AI and ML strategy (which models belong in production, what to build versus buy, where RAG and LLMs actually move the business), vendor selection and contract leverage, data team design and senior hiring, and board-ready communication on data, AI, and risk. Engagements are typically a fixed number of days per month over six to twelve months. You get senior judgment on the calls that lock in cost and capability for years, without adding a full-time executive.

How much does a fractional CDO cost, and how do you think about the value?

Develomentor engagements are scoped to the situation — typically a monthly retainer for a fixed number of days per month, or a fixed-scope assessment with a written deliverable. A fractional CDO is materially less than a full-time hire (which lands well into six figures plus equity) and removes the long executive search and ramp. The value shows up in the decisions you do not have to redo: the vendor contract you scope correctly the first time, the embedding model and data platform you do not have to rip out in eighteen months, the senior data hires who fit the actual roadmap. We send a written scope and price before any commitment.

Fractional CDO vs. full-time CDO, head of data, or VP of ML — when does each make sense?

A head of data or VP of ML is an operator who runs the team day to day. A full-time CDO makes sense when you have a stable, funded data and AI strategy that needs a long-tenured executive to own it for years. A fractional CDO makes sense when the next twelve months require senior strategic judgment on data and AI — platform choices, vendor contracts, AI investment, hiring the right operator — but a permanent executive hire is premature or unaffordable. Many engagements start fractional and end with us helping define and hire the full-time leader, often a VP of Data or head of ML rather than a CDO.

When do I actually need a fractional CDO?

The pattern is usually one of these: you are about to sign a multi-year contract with Snowflake, Databricks, or an AI vendor and want a senior read on lock-in and total cost before committing; you are investing meaningfully in AI, RAG, or search and want to know whether the architecture and model choices will hold up; your data team is shipping dashboards and pipelines but not the analytics or AI capabilities the business actually needs; a board or PE firm is asking pointed questions about data strategy, AI readiness, or model risk that your current team cannot answer; or you are between data leaders and the search will take six months. If two or more are true, a fractional CDO is usually the right call.

How fast can you start a CDO engagement?

Most engagements start within one to three weeks of an initial discovery call. Fixed-scope assessments — AI and data readiness reviews, platform and vendor evaluations, search or RAG architecture reviews, data team assessments — typically begin the same week. On the first call we will tell you whether we are the right fit, what the engagement should actually cover, and a realistic start date.

Fractional Chief Data Officer

Senior data and AI leadership on your terms — from the co-founder of Apache Mahout, an active Apache Lucene committer, and co-author of Manning's Taming Text.

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Trusted by Leading Organizations

If your product is built on data — search, retrieval, recommendations, RAG, machine learning, generative AI — Develomentor is led by someone who was building this stack before “Chief Data Officer” was a title. Grant Ingersoll is co-creator of the Apache Mahout machine learning library, an active Apache Lucene and Solr committer, and co-author of Manning’s Taming Text. As CTO of the Wikimedia Foundation, he led a 150+ engineer organization across 18 teams and replaced its homegrown ML infrastructure. As co-founder and CTO of Lucidworks, he shipped commercial search products at enterprise scale.

The choices in front of you are not engineering choices. They are executive choices about data and AI strategy: where the platform sits, which vendors get long contracts, which models belong in production, what the data team looks like a year from now.

How the work shows up

Data strategy the business can act on — a working plan tied to outcomes, not a deck. Architecture decisions that determine whether your AI product scales: data platforms, vector stores, retrieval pipelines, and evaluation infrastructure.

Vendor selection sits at the center of most engagements, and the stack has changed three times in two years. Snowflake or Databricks or neither. Which embedding model is worth the lock-in. Which RAG framework will still be standing in two years. Which model providers belong in your contract, and on what terms.

Then the team and the guardrails: the senior data engineers and ML practitioners you need, the structure they sit in, what they own. Privacy, lineage, and model evaluation that will hold up when an enterprise customer or a regulator asks.

Who this is for

The clearest fit is Search & AI builders — companies where retrieval, ranking, or AI is core to the product. Work also lands here when a senior data leader has departed, or when a board or acquirer needs an independent voice on architecture, vendor lock-in, or readiness for an enterprise customer.

How we engage

Most engagements are embedded leadership for a defined number of days per month, typically over six to twelve months. Some start as a fixed-scope review — search architecture, RAG production-readiness, AI/ML platform, or data-team structure — with a written deliverable, and convert into ongoing work.

Tell us what you are building and which decision is next. Book a Discovery Call.

Led by Grant Ingersoll

Every CDO engagement is led by Grant — co-founder of Apache Mahout, an active Apache Lucene committer, and co-author of Manning's Taming Text — supported by a team of senior practitioners with deep experience across data engineering, search, and AI/ML.

CTO, Wikimedia Foundation — 18 engineering teams, 150+ people
Co-founder & CTO, Lucidworks — scaled to 80+ engineers
Author, Taming Text · Apache Lucene committer · Apache Mahout co-founder