Prepared for boards, nomination committees & search advisers
File No. CDO-096
Making trusted data useful in consequential decisions.
| Subject | Suri Iskandar |
|---|---|
| Position | Chief Data Officer |
| Base | Singapore / Southeast Asia · Global perspective |
| Languages | English, Malay, Mandarin |
| Board seats | 2 current · 0 previous |
| Status | Open to board appointments, advisory roles, speaking invitations |
Summary
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.
| Item | Measure | Figure |
|---|---|---|
| 01 | Trusted source of truthReplacing 37 conflicting reports | 1 |
| 02 | Value from analyticsVerified by finance, year three | ₹410 Cr |
| 03 | Models governedUnder one responsible-AI framework | 220 |
| 04 | Time to decision dataFor the executive committee | 9 days → 4 hrs |
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.
Signed — Suri Iskandar
Independent Director
Risk · Data & AI Ethics (Chair)
2023 — Present
Advisory Board Member
Standards Council
2020 — Present
Matter 01Ref. CDO-096-M01
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.
Matter 02Ref. CDO-096-M02
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.
Matter 03Ref. CDO-096-M03
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.
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.
Made our data trustworthy enough to bet decisions on — and honest about where it was not.
The clearest voice on responsible AI in our boardroom.
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 essayThe 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
Chairs, nomination committees and search advisers may write directly. References and fuller records are available on request.
office@suriiskandar.example