Data Analytics speakers

Why organisations work with Timandra Harkness A working statistician on the Council of Trustees of the Royal Statistical Society who can speak fluently to technical and non-technical audiences in the same room. Author of two books that bracket the AI conversation: Big Data: Does Size Matter? on what data actually does and does not tell us, and Technology Is Not the Problem on why our cultural anxieties about tech are misdirected. A mainstream BBC Radio 4 presenter on FutureProofing, How To Disagree and Steelmanning, which means she handles complex, contested material on air for general audiences and brings that craft to a corporate room. Speaks regularly at institutions that take rigour seriously, including the Royal Society, the Alan Turing Institute, and the Royal Academy of Engineering, so the substance survives scrutiny from technical buyers. Comfortable as keynote speaker, panel moderator, or event host, which makes her useful across the full shape of a leadership offsite or technology summit. Biography highlights Author, Big Data: Does Size Matter? (Bloomsbury Sigma, 2016) and Technology Is Not the Problem (HarperCollins, 2024). Council of Trustees, Royal Statistical Society; chair of the editorial board, Significance journal. Writer and presenter, BBC Radio 4 series including FutureProofing, How To Disagree, Steelmanning, and Political School. Resident reporter across eight series of BBC Radio 4’s The Human Zoo. Speaker and lecturer at the Royal Society, Alan Turing Institute, Royal Academy of Engineering, RSA, Cheltenham Science Festival, and the Abel Prize for Mathematics. Contributor to the Telegraph, Guardian, Sunday Times, WIRED, New Statesman, UnHerd and BBC Focus. Biography Most arguments about technology in the boardroom are arguments about something else. Trust. Identity. What kind of company we want to be. Timandra Harkness, a statistician and broadcaster whose work spans both BBC Radio 4 and the Royal Statistical Society, is unusually well placed to name what is actually being discussed. Her first book, Big Data: Does Size Matter?, published by Bloomsbury Sigma in 2016, took the data revolution seriously without inflating it. Her second, Technology Is Not the Problem, published by HarperCollins in 2024, makes the harder case: that our anxieties about AI and social media are anxieties about ourselves, and that locating the problem in the technology is what stops us from acting on it. She presents BBC Radio 4’s FutureProofing, How To Disagree, and Steelmanning, programmes that work because they take competing arguments seriously rather than flattening them. That habit transfers. In a leadership room, she is the speaker who can hold a senior team’s attention on the awkward middle of an AI question, where the technical position and the cultural position have to be reconciled. She sits on the Royal Statistical Society’s Council of Trustees and chairs the editorial board of Significance, the Society’s journal. She has spoken at the Royal Society, the Alan Turing Institute, and the Royal Academy of Engineering. The technical rigour is real, and it is what stops the cultural argument from drifting into commentary. Key speaking topics Big data and what it does and does not reveal The cultural and ethical questions raised by AI Statistics, evidence and decision-making Human agency in an automated environment Identity, recognition and the social media question Technology and trust Disagreement and constructive argument inside organisations Ideal for Boards and executive teams making AI, data, and digital infrastructure decisions Technology and innovation summits seeking a credible non-vendor voice Leadership offsites where the AI conversation has to bridge technical and cultural ground CDO, CTO, and CHRO audiences working through the human consequences of new systems Audience outcomes A sharper sense of what data and AI systems can and cannot do, written by someone who works with statisticians A clearer language for the cultural and ethical questions executive teams keep deferring A defensible way to think about agency, recognition and trust as the technology landscape shifts Better-quality internal disagreement on contested technology decisions

Why organisations work with Ting Li A sitting professor at a top European business school whose work appears in Management Science, MIS Quarterly, and Information Systems Research, which means the frameworks she brings to a leadership room have already been tested against the strongest academic standards. Director of Digital Business Practice at the Erasmus Centre for Data Analytics, where research is run alongside large corporates and the Dutch government, so the recommendations are grounded in operating reality rather than slideware. One of the few academic voices working across digital pricing, online advertising, social and mobile analytics, and immersive technology, which lets her connect AI investment decisions to concrete commercial levers. Named to the Poets and Quants Top 40 Under 40 ranking of business school professors, an external signal that the teaching translates for senior executive audiences, not only doctoral students. Bilingual in English and Mandarin, with research and teaching ties across Europe, North America, and China, useful for boards working through digital strategy with operations on both sides of the rules-based order. Biography highlights Professor of Digital Business and head of the Information Systems group at Rotterdam School of Management, Erasmus University. Founding member and Academic Director of Digital Business Practice at the Erasmus Centre for Data Analytics, and head of the Immersive Tech and AI Lab. Visiting Professor positions at Wharton, MIT, Tsinghua, Temple, Arizona State, and City University of Hong Kong. Published in Management Science, MIS Quarterly, Information Systems Research, Nature Communications, Production and Operations Management, and Harvard Business Review. Named by Poets and Quants as one of the Top 40 Best Business School Professors Under 40 Worldwide. Research funded by the Dutch National Science Foundation and by industry partners including VIVAT, AllSafe, and the Dutch Police. Biography Most digital strategy advice on AI and platforms is opinion dressed as evidence. Ting Li works the other way around. Her research at Rotterdam School of Management uses econometrics, field experiments, and machine learning to test what digital technologies actually do to consumer behaviour, pricing, and competitive position, then translates the result for the people making the investment call. At RSM she holds the chair in Digital Business and runs the Information Systems group. She is also Academic Director of Digital Business Practice at the Erasmus Centre for Data Analytics, where she works with corporates and Dutch public bodies on live problems in digital advertising, platform design, and AI deployment. The Immersive Tech and AI Lab she leads gives her a working view of where AR, VR, and generative tools are starting to change customer experience rather than only marketing decks. Her academic record sits at the top of the field. Papers in Management Science, MIS Quarterly, Information Systems Research, and Nature Communications, alongside articles in Harvard Business Review, mean a board hearing her on digital pricing or online advertising is hearing the underlying evidence, not a secondhand reading of it. Visiting positions at Wharton, MIT, and Tsinghua give her a working network across the leading research centres on the digital economy. The Poets and Quants Top 40 Under 40 ranking captured what executive education clients already report: she is a researcher who can hold a senior room. That is what makes her useful when the question on the table is whether a specific AI or platform bet will move the business. Key speaking topics Digital strategy and the digital economy AI and decision-making in the enterprise Digital platforms and online advertising Pricing and revenue management Social and mobile analytics Digital privacy and algorithmic accountability Generative AI, AR, VR, and immersive technology Ideal for Boards and executive committees setting digital and AI investment priorities. Chief Digital, Data, and AI Officers moving from pilots to operating deployment. Chief Marketing Officers and revenue leaders working on pricing, platform, and advertising decisions. Strategy and innovation leads in retail, financial services, and consumer-facing platforms. Audience outcomes A clearer read on which AI and digital investments have evidence behind them and which do not. A sharper view of how digital pricing, advertising, and platform mechanics affect customer behaviour at scale. Practical reference points for governing AI use in commercial decisions. A working sense of how immersive and generative technologies are starting to reshape customer experience.

Why organisations work with Viktor Mayer-Schönberger His research on data-sharing directly shaped the EU’s Digital Markets Act – when he talks about regulatory risk, he is describing a landscape he helped design. Big Data (co-authored with The Economist‘s Kenneth Cukier) sold over one million copies and set the terms of the enterprise data conversation for a decade; organisations benefit from the intellectual framework behind the book, not just its conclusions. As a member of Germany’s Digital Council, he advised Chancellor Merkel’s cabinet on digital policy (2018–2021) – giving him a rare dual perspective on both corporate strategy and governmental decision-making. His 2024 book Guardrails (with Urs Gasser, hailed in Science journal) offers the most current framework available for deciding which decisions to delegate to AI and which to retain – a live, unresolved question in most boardrooms. He holds the Oxford Chair as one of three statutory professors at the Oxford Internet Institute, combining research authority with over 250 keynote engagements across Fortune 500 companies and government bodies. Biography highlights Professor of Internet Governance and Regulation at the Oxford Internet Institute – one of three statutory chairs Ten years on the faculty of Harvard’s Kennedy School of Government; continuing affiliate at Harvard’s Belfer Center for Science and International Affairs Member of Germany’s Digital Council; personal adviser to Chancellor Angela Merkel on digital policy (2018–2021) Delete (Princeton University Press, 2009) won the 2010 Marshall McLuhan Award and the 2010 Don K. Price Award for Best Book in Science and Technology Politics Big Data (2013, with Kenneth Cukier) – NYT and WSJ bestseller; over one million copies sold; translated into more than 20 languages Research cited in, and proposals adopted by, the EU Digital Markets Act; covered by the New York Times, Financial Times, BBC, The Guardian, Der Spiegel, Le Monde, and Nature Biography Data governance is not a compliance function. It is, as Viktor Mayer-Schönberger has spent three decades arguing, the central strategic question of the digital economy, and most organisations are still treating it as a footnote. Mayer-Schönberger holds the Chair of Internet Governance and Regulation at the Oxford Internet Institute, one of three statutory professorships at the OII. Before Oxford, he spent a decade at Harvard’s Kennedy School of Government and three years at the Lee Kuan Yew School of Public Policy in Singapore. That arc – from entrepreneurship to academic leadership across three continents – gives him a fluency in organisational, governmental, and market dynamics that few researchers in this space share. His books have not merely described the data economy; they have shaped it. Delete (Princeton, 2009) initiated the debate that produced the EU’s right to be forgotten. Access Rules (2022) and the earlier Reinventing Capitalism in the Age of Big Data proposed the data-sharing mandates that were subsequently enacted in the EU Digital Markets Act. His bestseller Big Data – co-authored with The Economist‘s Kenneth Cukier – sold over a million copies and remains the foundational text for enterprise data strategy. His most recent book, Guardrails (2024, with Urs Gasser), praised in Science journal, addresses the question now live in every boardroom: how to draw the line between AI delegation and human judgement. From 2018 to 2021 he served as a member of Germany’s Digital Council, advising Chancellor Merkel’s cabinet directly. He is a faculty affiliate at Harvard’s Belfer Center and has addressed senior audiences at IBM, SAP, Microsoft, and Roche, as well as regulatory and governmental forums across the EU, UK, and US. His advisory boards have included Microsoft and the World Economic Forum. Key speaking topics AI governance and the limits of delegation Data access, competition, and digital market regulation The strategic economics of data Information privacy and the right to be forgotten Human decision-making in the age of AI Digital policy and corporate regulatory risk The data-driven transformation of capitalism Ideal for Boards and executive leadership teams navigating AI governance and regulatory exposure Chief Data, Digital, and AI Officers building organisational data strategy General counsel, compliance, and risk functions in regulated industries Public policy and regulatory affairs teams across the EU and UK Audience outcomes A structured framework for distinguishing decisions that should be delegated to AI from those that must remain with humans Clarity on the regulatory direction of travel in the EU and UK, and what it means for data strategy An understanding of how data access and sharing mandates will reshape competitive dynamics in their sector Practical grounding in the governance failures that produce both legal exposure and strategic underperformance A shared language for board-level conversations about AI risk that moves beyond the abstract

Why organisations work with Vivienne Ming Her «Tax on Being Different» framework quantifies – in economic and productivity terms – the cost organisations pay when marginalised workers must expend extra effort just to be assessed as equivalent. This is a measurement argument with commercial implications, not a values one. As Chief Scientist at Gild, she built machine learning models trained on 122 million professionals, making her one of the few people who has directly operationalised predictive AI for talent assessment at scale, outside a purely academic context. She reframes diversity from a compliance question into an optimisation problem: one that can be modelled, measured, and corrected using the same tools organisations already apply to operational decisions. Her research spans workforce, education, and health, giving her an unusually broad evidence base for what actually predicts long-term human capability, and what doesn’t. Published in Nature and Neural Information Processing Systems, and holding academic appointments at UC Berkeley and UCL, her arguments carry institutional credibility that applied consultants and conference speakers typically cannot match. Biography highlights Author of Robot-Proof: When Machines Have All the Answers, Build Better People (Wiley, 2026), which argues that human capital – not AI capability – determines the outcome of human-machine collaboration PhD in Psychology from Carnegie Mellon University; joint postdoctoral fellowship at Stanford University and UC Berkeley’s Redwood Center for Theoretical Neuroscience Former Chief Scientist at Gild, where she built ML models trained on 122 million professionals to assess human capability beyond conventional credentials Founder and Executive Chair of Socos Labs, an independent research institute applying AI and neuroscience to workforce, education, and health challenges Research published in Nature, Neural Information Processing Systems, and Neural Computation Ranked #5 on the Financial Times / OUTstanding list of the world’s top LGBT executives (2017); named BBC 100 Women (2017) and Inc. Magazine’s «10 Women to Watch in Tech» Chair, UC Berkeley Neurotech Collider Hub; Honorary Professor, UCL Global Business School for Health; board member, RFK Human Rights Biography Vivienne Ming built her career on a discomforting empirical observation: the hiring signals organisations treat as reliable – degrees, interview performance, institutional pedigree – do not predict what they claim to predict. She tested this at scale. As Chief Scientist at Gild, she developed machine learning models trained on data from 122 million professionals, looking for what actually distinguishes high performers from credentialled ones. The gap was substantial. Most organisations were optimising for proxies, not capability. That finding runs through everything she has done since. At Socos Labs, the research institute she co-founded with her wife Dr. Norma Ming, she has extended the question into education and health, examining what psychological constructs genuinely predict long-term life outcomes, and publishing that research in Nature and Neural Information Processing Systems. The argument is consistent: measurement systems in organisations are systematically broken, and the cost is borne most heavily by those who don’t fit the template. Her book Robot-Proof: When Machines Have All the Answers, Build Better People (Wiley, 2026) extends that argument into AI, making the case that human capital – not model benchmarks – determines whether human-machine collaboration produces anything beyond substitution. Her «Tax on Being Different» framework gives that cost a number. Drawing on large-scale HR data, it quantifies the additional credential burden that women, people of colour, and LGBTQ+ individuals absorb to be evaluated on equivalent terms. This is not a cultural argument. It is a productivity and innovation argument: one that has been delivered to boards and leadership teams at Google, Deloitte, and Salesforce, and presented at the Royal Society. She holds a PhD in Psychology from Carnegie Mellon University, with joint postdoctoral appointments at Stanford and UC Berkeley. She chairs the UC Berkeley Neurotech Collider Hub, holds an honorary professorship at UCL’s Global Business School for Health, and serves on the board of RFK Human Rights. In 2017, she was ranked #5 on the Financial Times / OUTstanding list of the world’s top LGBT executives. Her research and commentary have been featured in the Financial Times, Business Insider, Fast Company, and CNBC. Key speaking topics AI and predictive modelling of human capability The economics of talent assessment and bias Neuroscience of decision-making in organisations Diversity, inclusion, and the productivity cost of difference Human-machine collective intelligence AI applications in education, health, and workforce development Innovation methodology: solving ill-posed problems Ideal for CHROs and talent acquisition leaders reviewing whether their hiring signals are predictive CEOs and boards integrating AI into people decisions and workforce strategy DEI leaders seeking evidence-based, commercially framed arguments for systemic change Technology and innovation functions examining the limits and applications of AI in human contexts Audience outcomes A quantified, data-grounded understanding of what broken talent systems cost organisations in productivity and innovation A concrete reframe of diversity: not a moral obligation but a measurement and optimisation problem Practical questions to apply to their own hiring, promotion, and development processes Clarity on where AI reliably improves human assessment – and where it perpetuates the biases already embedded in the data A new vocabulary for capability that moves beyond credentials and credentials-adjacent signals