A leadership monograph
Making trusted data useful in consequential decisions.
Chief Data Officer · United States · Global perspective · Volume I · 2026
Chapter I
I help enterprises turn data into a trusted part of how decisions are made. My work spans ownership, quality, analytics and responsible AI, with a focus on the business practices that allow technical capability to be used well.
I begin with a decision and the people accountable for it. Understanding their information needs helps identify which data matters, where its limitations sit and who must own its quality. Governance should make those responsibilities workable rather than add a parallel layer of administration.
I bring business owners, technologists and risk colleagues into the same conversation. A useful data strategy is one the organisation can explain, operate and challenge when circumstances change.
Chapter II
Trusted data becomes valuable when people can use it responsibly in a decision. I connect ownership, quality and analytical capability to business purpose, with explicit limitations and human accountability for consequential uses.
Simone Sterling
Chapter III
Chapter IV
§ 1
Business functions were maintaining different versions of a critical performance measure. I worked with the accountable owners to define the measure, document its source and agree how exceptions would be handled. The programme treated data ownership as an operating responsibility and gave users a visible path for resolving quality issues.
§ 2
An analytics initiative had produced technically sound outputs that were not part of the management routine. We redesigned the work around a specific decision, involved the decision owners in evaluation and made limitations visible in the way findings were presented. Adoption became part of the delivery definition.
§ 3
Teams wanted to move AI experiments into business use without a consistent review process. I convened product, data, risk and operating leaders to agree evaluation criteria, human accountability and monitoring responsibilities. The approach allowed use cases to be assessed on their actual purpose and exposure rather than on enthusiasm for the technology.
Chapter V
2020 — Present
Aldermoor Group
Leading enterprise data strategy, governance and analytics adoption. Working with business owners and risk colleagues to connect trustworthy information and responsible AI with defined decision needs.
2016 — 2020
Fairhaven Holdings
Built data and analytics capability around priority business uses. Developed operating practices for data ownership, quality evaluation and the communication of analytical limitations.
2013 — 2018
Westmoor Housing Finance
Built the credit analytics capability that reduced losses while widening access to first-time borrowers.
2008 — 2013
Lumen Payments
Designed fraud models at national scale, and learned that a model is only as good as the decision it improves.
Chapter VI
Independent Director
Risk · Data & AI Ethics (Chair)
2023 — Present
Advisory Board Member
Standards Council
2020 — Present
Chapter VII
Naming an owner is only useful if that person can make the decisions needed to keep information fit for purpose.
Read the essayAn analytical output can be accurate and still fail to influence the work it was intended to support.
Read the essayAn AI use case needs to be assessed in the context of the decision or task it supports.
Read the essayChapter VIII
The Applied AI Forum · 2025
“Governing 220 models without slowing the business”
The Boardroom Review · 2024
“The board's role in responsible AI”
The Leadership Ledger podcast · 2023
“Episode 120 — Data people can trust”
An invitation
The next chapter is written in conversation. I welcome enquiries from boards, chairs and those who carry consequential decisions.
office@simonesterling.example