{"id":40512,"date":"2026-01-28T14:04:36","date_gmt":"2026-01-28T14:04:36","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=100-alumni&p=40512"},"modified":"2026-02-12T13:24:45","modified_gmt":"2026-02-12T13:24:45","slug":"pankaj-arora-group-director-of-analytics-decision-science-saga-plc","status":"publish","type":"100-alumni","link":"https:\/\/www.dataiq.global\/devstage\/dataiq100\/pankaj-arora-group-director-of-analytics-decision-science-saga-plc\/","title":{"rendered":"Pankaj Arora, Group Director of Analytics & Decision Science, SAGA PLC"},"content":{"rendered":"

Pankaj Arora is Group Director of Analytics and Decision Science at SAGA PLC, with a leadership perspective shaped by a series of defining turning points around value,\u00a0trust\u00a0and enterprise influence. His career began in roles with full P&L accountability, before moving into data and analytics. That experience instilled a clear principle that continues to anchor his approach today: data only creates value when it changes outcomes. As a result, he treats data as a commercial function, accountable for impact rather than activity.<\/span>\u00a0<\/span><\/p>\n

A second inflection point came from building enterprise data capabilities while working closely with executive boards. Through this, Pankaj learned that success at the top of organisations is driven less by analytical sophistication and more by compelling storytelling. Insight creates traction when it is framed in the language of strategy, growth,\u00a0trade-offs\u00a0and risk, rather than models and metrics.<\/span>\u00a0<\/span><\/p>\n

A third defining moment was leading one of the UK\u2019s earliest machine learning deployments in credit decisioning. While the programme delivered a 40% uplift in performance, the deeper lesson was about responsibility. Pankaj\u2019s experience reinforced that effective AI leadership is grounded in trust, not speed, and that explainability, fairness and governance must be embedded by design to enable scale.<\/span>\u00a0<\/span><\/p>\n

Most recently at SAGA, Pankaj has focused on making data the \u2018golden thread\u2019 that connects insight across multiple businesses. This work reinforced his belief that sustainable advantage comes from connected, enterprise-wide insight rather than isolated analytics.<\/span>\u00a0<\/span><\/p>\n

Together, these experiences underpin a clear philosophy: data leadership is about enterprise influence and decision-making, not the production of insight for its own sake.<\/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

The most important traits are judgment, commercial ownership of outcomes, and the ability to drive last-mile activation.<\/span>\u00a0<\/span><\/p>\n

\u201cJudgement is foundational. Data leaders\u00a0operate\u00a0in environments shaped by constraints such as tight investment, legacy technology, regulatory oversight, and competing priorities. Knowing what to prioritise, what to stop, and when to move fast versus slow matters more than technical sophistication alone. In my experience, strong judgment is what separates teams that experiment from those that deliver sustained impact.<\/span>\u00a0<\/span><\/p>\n

\u201cCommercial ownership is the second critical skill. Data functions only earn influence when they are explicitly tied to business outcomes. This means shaping the problem before solving it, partnering closely with\u00a0finance to measure value, and framing insight in the language of growth, cost, and risk. In my experience, this shift transforms data from a support function into a strategic one.<\/span>\u00a0<\/span><\/p>\n

\u201cFinally, last-mile activation is where value is won or lost. The greatest insights fail if they are not embedded in decisions and operations. Effective leaders design for adoption from day one, embed analytics into operations, and invest in storytelling to sustain sponsorship and trust.<\/span>\u00a0<\/span><\/p>\n

\u201cTogether, these skills enable data leaders to move beyond capability building and deliver impact that is visible and enduring.\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

\u201cMy advice to aspiring data and AI leaders is to build influence before chasing scale.<\/span>\u00a0<\/span><\/p>\n

\u201cEarly in a data leader\u2019s career, it is tempting to focus on bigger platforms, more models, and broader mandates. In practice, influence is earned through a small number of well-chosen wins that clearly change decisions and outcomes. Start narrow, deliver visibly, and let credibility compound.<\/span>\u00a0<\/span><\/p>\n

\u201cThis requires spending time where decisions are made, not just where data is produced. Understand incentives, pressures, and constraints, and adapt your approach accordingly. Be present after delivery,\u00a0observe\u00a0how your work is used, and be prepared to adjust when reality does not match intent.<\/span>\u00a0<\/span><\/p>\n

\u201cEqually important is the discipline to stop. Be willing to deprioritise initiatives that do not deliver measurable value, even if they are technically interesting. Focus creates momentum.<\/span>\u00a0<\/span><\/p>\n

\u201cFinally, communicate with intent. Consistent, simple narratives about progress and impact build confidence over time. When leaders trust your judgement, data naturally becomes part of how the organisation runs.<\/span>\u00a0<\/span><\/p>\n

\u201cThis is the pathway from technical authority to executive trust.\u201d<\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

Click\u00a0here to read the full profile<\/a> and learnings, exclusively for DataIQ clients. Get in touch<\/a> to become a client and access unique events, insights, and more.<\/p>\n","protected":false},"excerpt":{"rendered":"

Pankaj Arora is Group Director of Analytics and Decision Science at SAGA PLC, with a leadership perspective shaped by a series of defining turning points around value,\u00a0trust\u00a0and enterprise influence. His career began in roles with full P&L accountability, before moving into data and analytics. That experience instilled a clear principle that continues to anchor his […]<\/p>\n","protected":false},"author":19,"featured_media":14159,"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-40512","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":"Pankaj","surname":"Arora","fullname":"Pankaj Arora","jobtitle":"Group Director of Analytics & Decision Science","organisation":"SAGA PLC ","introduction":"","content_2":"","content_3":"","content_4":"","100_thumbnail_image":14159,"affinoid":""},"publishpress_future_action":{"enabled":false,"date":"2026-05-27 07:36:37","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\/40512","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=40512"}],"version-history":[{"count":3,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40512\/revisions"}],"predecessor-version":[{"id":41106,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-alumni\/40512\/revisions\/41106"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/14159"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=40512"}],"wp:term":[{"taxonomy":"100-designation","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-designation?post=40512"},{"taxonomy":"100-search-term","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/100-search-term?post=40512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}