{"id":40481,"date":"2026-01-28T10:42:58","date_gmt":"2026-01-28T10:42:58","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=100-alumni&p=40481"},"modified":"2026-02-09T12:12:01","modified_gmt":"2026-02-09T12:12:01","slug":"tom-spencer-director-of-customer-data-sciences-aviva-3","status":"publish","type":"100-alumni","link":"https:\/\/www.dataiq.global\/devstage\/dataiq100\/tom-spencer-director-of-customer-data-sciences-aviva-3\/","title":{"rendered":"Tom Spencer, Director of Customer Data Sciences, Aviva"},"content":{"rendered":"

Tom Spencer is Director of Customer Data Sciences at Aviva, where he leads teams spanning analytics, data science,\u00a0engineering,\u00a0and data product to deliver customer-focused insight and AI at scale.<\/span>\u00a0<\/span><\/p>\n

He began his career in retail and FMCG at dunnhumby, working close to the commercial frontline. That experience shaped a lasting belief that data is only valuable when it changes a decision for real customers. Early roles taught him both how easy it is to produce impressive analysis that\u00a0fails to\u00a0land, and how powerful data becomes when it earns trust by solving tangible problems.<\/span>\u00a0<\/span><\/p>\n

A key turning point in Tom\u2019s career came with the move from specialist roles into leading multi-disciplinary teams. His focus shifted from individual models to the broader system around them, including product thinking, engineering reliability,\u00a0governance,\u00a0and adoption. Through this transition, he developed a pragmatic view that better execution\u00a0almost always\u00a0delivers more impact than marginal improvements in algorithms.<\/span>\u00a0<\/span><\/p>\n

At Aviva, working in a highly regulated environment has further shaped his perspective on responsible AI. He sees it not as a compliance exercise, but as a core leadership discipline that must be embedded into how teams\u00a0operate\u00a0and make decisions.<\/span>\u00a0<\/span><\/p>\n

Tom\u2019s approach to data leadership is grounded in a simple set of principles: start with outcomes, design systems that scale, and invest in people and partnerships as seriously as technology. He consistently emphasises that technology is rarely the right starting point; impact comes from aligning data capability to\u00a0real business\u00a0needs and customer outcomes.<\/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

In my opinion, really\u00a0key traits for data and AI leadership are creating clarity, and credibility for delivering results.<\/span>\u00a0<\/span><\/p>\n

\u201cWe work in a complex,\u00a0rapidly changing\u00a0field.\u00a0It is easy to over-complicate. Those that create clarity\u00a0amid\u00a0that have a massive advantage. Simplify the complexity into practical choices: what problem we are solving, how we will measure success, and what we will not do. This protects focus and ensures teams work on what really matters.<\/span>\u00a0<\/span><\/p>\n

\u201cHowever, I\u00a0don\u2019t\u00a0think clarity alone is enough. You also\u00a0must\u00a0have built credibility;\u00a0a track record\u00a0of delivery and results. Sometimes there still needs to be a leap of faith, but\u00a0that’s\u00a0an easier sell if\u00a0you’ve\u00a0built up the credit in the bank with your stakeholders. When leaders trust you, they give you more freedom to move quickly.\u201d<\/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

\u201cI\u00a0don’t\u00a0know if\u00a0it’s\u00a0non-traditional, but one\u00a0really useful\u00a0piece of development advice I still use and share with my teams is\u00a0\u2018think a level up\u2019.<\/span>\u00a0<\/span><\/p>\n

\u201cIt’s\u00a0all too easy to get caught up in your own role, thinking about your own problems, stuck in your own perspective. However, if you can snap out of that and try to think like your manager (or whichever senior stakeholder is relevant in the situation) it can make a real difference to how you think about problems.<\/span>\u00a0<\/span><\/p>\n

\u201cThat zoomed-out perspective can really help your\u00a0development and\u00a0makes you so much more valuable to your manager and your organisation.\u00a0In my opinion, it’s\u00a0universal; it can be useful in your career from entry-level to CEO.\u201d<\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

Click here to read the full profile<\/a> and learnings, exclusively for DataIQ subscribers. Get in touch<\/a> to become a subscriber and access unique events, insights, and more.<\/p>\n","protected":false},"excerpt":{"rendered":"

Tom Spencer is Director of Customer Data Sciences at Aviva, where he leads teams spanning analytics, data science,\u00a0engineering,\u00a0and data product to deliver customer-focused insight and AI at scale.\u00a0 He began his career in retail and FMCG at dunnhumby, working close to the commercial frontline. That experience shaped a lasting belief that data is only valuable […]<\/p>\n","protected":false},"author":19,"featured_media":31483,"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-40481","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":"Tom","surname":"Spencer ","fullname":"Tom Spencer","jobtitle":"Director of Customer Data Sciences ","organisation":"Aviva ","introduction":"","content_2":"","content_3":"","content_4":"","100_thumbnail_image":31483,"affinoid":""},"publishpress_future_action":{"enabled":false,"date":"2026-05-27 07:34:34","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\/40481","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=40481"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40481\/revisions"}],"predecessor-version":[{"id":41001,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40481\/revisions\/41001"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/31483"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=40481"}],"wp:term":[{"taxonomy":"100-designation","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-designation?post=40481"},{"taxonomy":"100-search-term","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-search-term?post=40481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}