{"id":40338,"date":"2026-01-27T17:20:26","date_gmt":"2026-01-27T17:20:26","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=100-alumni&p=40338"},"modified":"2026-03-09T09:27:08","modified_gmt":"2026-03-09T09:27:08","slug":"gaudenz-koeppel-chief-data-analytics-officer-ts-axpo","status":"publish","type":"100-alumni","link":"https:\/\/www.dataiq.global\/devstage\/dataiq100\/gaudenz-koeppel-chief-data-analytics-officer-ts-axpo\/","title":{"rendered":"Gaudenz Koeppel, Chief Data & Analytics Officer T&S, Axpo"},"content":{"rendered":"

Gaudenz Koeppel is Chief Data and Analytics Officer T&S at\u00a0Axpo, where he leads the end-to-end data and analytics agenda across one of Europe\u2019s most complex energy trading environments.<\/span>\u00a0<\/span><\/p>\n

He began his career as an electrical engineer before spending around a decade in short-term power trading centred on hydro assets. Working in a system where decisions are time-critical, uncertainty is constant and reliability is non-negotiable fundamentally shaped his leadership perspective. Optimising the dispatch and trading of a national hydro portfolio, where power is consumed instantly and outages are unacceptable, taught him that progress depends on trust,\u00a0resilience\u00a0and repeatability rather than theoretical perfection.<\/span>\u00a0<\/span><\/p>\n

An early turning point was shifting trading decisions from experience-based judgement towards model-supported decision-making. The goal was not perfect\u00a0forecasts, but\u00a0signals that people could rely on under pressure. A second inflection came with the creation of an Advanced Analytics Centre of Excellence across the trading business. Not being a data scientist himself, Gaudenz focused on enabling specialists,\u00a0hiring for potential,\u00a0delegating\u00a0deeply\u00a0and aligning teams around clear outcomes. Building a data and analytics community that now spans around 300 of\u00a0Axpo\u2019s\u00a02,000 colleagues reinforced his belief that listening at scale is a core leadership skill, particularly in decentralised organisations.<\/span>\u00a0<\/span><\/p>\n

In 2024, Gaudenz became Chief Data and Analytics Officer, expanding his remit from advanced analytics to the full data value chain, including platforms,\u00a0governance\u00a0and adoption. Alongside his executive role, he teaches power markets at ETH Zurich, grounding his leadership in fundamentals while driving change at scale.<\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?<\/span><\/b>\u00a0<\/span><\/h3>\n

\u201cEffective data and AI leadership\u00a0isn\u2019t\u00a0a technical role in disguise.\u00a0It\u2019s\u00a0a leadership role that requires technology literacy: you need to understand what modern platforms, governance tooling, and AI methods can and cannot do\u00a0because your strategic options, risk posture, and the skill development you foster in the organisation all depend on it. But the differentiator is not writing the code;\u00a0it\u2019s\u00a0turning technical possibility into business capability.<\/span>\u00a0<\/span><\/p>\n

\u201cThe traits that matter most are: business judgment,\u00a0anchoring work in real decisions and value;\u00a0influence without authority,\u00a0aligning stakeholders across silos;\u00a0systems thinking,\u00a0platform, governance, adoption, and operating model as one system;\u00a0and trust-building,\u00a0delegating to specialists while holding the line on outcomes and standards.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cI also value a rare combination: being conceptual and pragmatic at the same time. You need a clear target state, but you\u00a0can\u2019t\u00a0wait for perfect information. The most effective leaders make no-regret\u00a0moves and\u00a0implement what creates optionality, test assumptions, learn fast, and pivot deliberately.<\/span>\u00a0<\/span><\/p>\n

\u201cIn my organisation, the most influential skills have been collaboration, listening at scale, and empowerment. In a decentralised setup, a big central team becomes a bottleneck;\u00a0so,\u00a0I\u00a0build mechanisms that scale alignment\u00a0(community, council, clear guardrails)\u00a0and enable teams to move safely and deliver outcomes.<\/span>\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n

Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?<\/span><\/b>\u00a0<\/span><\/h3>\n

\u201cThe non-traditional advice\u00a0I\u2019d\u00a0give is\u00a0lead as if your job is to make yourself unnecessary.\u00a0Repeat\u00a0relentlessly\u00a0what needs to become true for the company, not what your function wants to build. If data and AI only\u00a0\u2018work\u2019\u00a0because the CDAO is pushing,\u00a0it\u2019s\u00a0not a capability yet;\u00a0it\u2019s\u00a0a dependency.<\/span>\u00a0<\/span><\/p>\n

\u201cIn practice, that means designing for ownership and empowerment. Put decisions where the value is created, make responsibilities explicit, and build simple guardrails that let teams move safely without waiting for a central gatekeeper. Spend as much time shaping incentives, language, and trust as you do on platforms and models. Your best achievements will look boring on an org chart: teams adopting standards voluntarily, reusing assets without being told, and leaders demanding evidence-based decisions as a habit.<\/span>\u00a0<\/span><\/p>\n

\u201cIf you do it right, outcomes\u00a0won\u2019t\u00a0depend on a central leader\u2019s push;\u00a0they\u2019ll\u00a0emerge\u00a0from distributed ownership and repeatable practices.\u201d<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Gaudenz Koeppel is Chief Data and Analytics Officer T&S at\u00a0Axpo, where he leads the end-to-end data and analytics agenda across one of Europe\u2019s most complex energy trading environments.\u00a0 He began his career as an electrical engineer before spending around a decade in short-term power trading centred on hydro assets. Working in a system where decisions […]<\/p>\n","protected":false},"author":19,"featured_media":41570,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"100-designation":[1454],"100-search-term":[1456],"class_list":["post-40338","100-alumni","type-100-alumni","status-publish","format-standard","has-post-thumbnail","hentry","100-designation-100-brands-2026-europe","100-search-term-2026-europe-brands"],"acf":{"sort":"","rank":"","forename":"Gaudenz","surname":"Koeppel","fullname":"Gaudenz Koeppel","jobtitle":"Chief Data & Analytics Officer T&S ","organisation":"Axpo ","introduction":"","content_2":"","content_3":"","content_4":"","100_thumbnail_image":41570,"affinoid":""},"publishpress_future_action":{"enabled":false,"date":"2026-05-27 07:28:41","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"100-designation","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40338","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/100-alumni"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=40338"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40338\/revisions"}],"predecessor-version":[{"id":41571,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40338\/revisions\/41571"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/41570"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=40338"}],"wp:term":[{"taxonomy":"100-designation","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-designation?post=40338"},{"taxonomy":"100-search-term","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-search-term?post=40338"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}