Dataiku is The Universal AI Platform™, giving organizations control over their AI talent, processes, and technologies to unleash the
JOIN OUR SPEAKING SESSIONS: Wednesday, the 10th of September
from 11:45 to 12:15 in Lecture Hall 1 from 13:30 to 14:00 in Lecture Hall 6
5 Floor plan + partners
6 Exhibitor List
16 Interview (Marloes Pomp, EAIF)
28 Interview (Sander Kerstens, Vanderlande)
32 Interview (Louis de Roo, E-mergo)
40 Interview (Frans Feldberg, UvA)
9 Blog Cherish your bad data
13 Blog AI in the workplace: faster than policy can keep up
37 Blog Who is digging in my cookie jar?
43 Blogs from our exhibitors
54 Blog Do AI and data-driven work go hand in hand
58 Blog Ban the bonus?
WEDNESDAY 10 SEPTEMBER
10 Program Wednesday
14 Keynote Wednesday
18 Lecture program hall 1
19 Lecture program hall 2
20 Lecture program hall 3
21 Lecture program hall 4
23 Lecture program hall 5
24 Lecture program hall 6
25 Lecture program hall 7
26 Lecture program hall 8
27 Roundtable sessions
THURSDAY 11 SEPTEMBER
34 Program Thursday
38 Keynote Thursday
44 Lecture program hall 1
45 Lecture program hall 2
46 Lecture program hall 3
47 Lecture program hall 4
49 Lecture program hall 5
50 Lecture program hall 6
51 Lecture program hall 7
52 Lecture program hall 8
53 Roundtable sessions
OPENING HOURS
Wednesday 10 September 2025
10:00 - 17:00 hour
Thursday 11 September 2025
10:00 - 17:00 hour
LEARN MORE ABOUT:
Product development with AI
Data quality
Problem solving
DataOps in practice
Digitalization
Listen to roundtable discussions, in-depth interviews, and stories from experts with hands-on experience.
“We naturally want to ensure that we can stand on our own two feet when it comes to this kind of technology. And that we can also seize the opportunities it presents in terms of employment.”
Alexandra van Huffelen State Secretary for Digital Affairs
LISTEN TO THE EPISODES IN COLLABORATION WITH DATA EXPO
De Dataloog is a Dutch podcast where data scientists, consultants, technology companies, and researchers discuss various topics in the field of data. They also have various podcasts in English.
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EXHIBITOR STAND
A
Aarini Consulting 30 www.aarini.com
ABN AMRO Bank N.V. 128 www.abnamro.com
Agiliz 12 www.agiliz.com
AI4ALL 154 www.aifourall.org
AIC4NL 124 www.aic4nl.nl
AI Opener 166 www.aiopener.io
Aimable 131 www.aimable.ai
Altair Engineering 21 www.altair.com
www.cmotions.com
Codit - Proximus NXT 114 www.codit.eu/en
Collibra 35 www.collibra.com
Conify 115 www.conify.nl
Amazon Web Services 95 www.aws.amazon.com
Amsterdam Data Academy 87 www.amsterdamdataacademy.com
AT Computing 66 www.atcomputing.nl
Autoriteit Consument & Markt 120 www.acm.nl/nl
Autoriteit Persoonsgegevens 120 www.autoriteitpersoonsgegevens.nl
B BearingPoint 14 www.bearingpoint.com/en-nl
BI-Platform 52 www.biplatform.nl
BusinessWise 140 www.businesswise.nl
C CAS Software AG 142 www.cas-software.com
C-data 144 www.c-data.nl
Cibit Academy 66 www.cibit.nl
Circle One 19 www.circleone.nl
CloudPhilos
www.cloudphilos.io
Connected Data Academy, een Open Line bedrijf 13 www.connecteddataacademy.com
Conscia Nederland B.V. 2 www.conscia.com/nl
Considerati 174 www.considerati.com
Cribl 7 www.cribl.io
Ctac Group
www.ctacgroup.eu
D
DAMA-NL 177 www.dama-nl.org
Darktrace 7 www.darktrace.com
Data Expo Exhibitor Hub 97 www.data-expo.nl/nl/exposanten/ exposant-worden
Data Kitchen B.V. 143 www.datakitchen.nl
Data Spaces Support Centre (DSSC) 86 www.dssc.eu
85 www.databricks.com
Dataddo 72 www.dataddo.com Datadirigent 45
www.eraneos.nl
www.intodq2.com
High-quality data are essential for success with artificial intelligence and the multitude of other tools for data-driven work. Unfortunately, many data collections do not meet the required standards or become contaminated over time.
As a result, bad data has become the corpse in the closet that every organization is apprehensive about. Is this justified?
You can also look at it differently. Late last year I was at a meeting of DAMA NL, the association of data professionals. “Poor quality data are not worthless,” 70% of the attendees responded to a poll. In fact, you can clean up a data collection very well. So consider yourself lucky if your organization has a lot of data, even if some of it is “moldy” or out of sync.
Raising and maintaining the quality of data collections does, however, require time, attention and investment, for example in setting up a good data quality management
Author: Thijs Doorenbosch
system. Data professionals must convince their company management that those investments are worthwhile. And data management is not a sexy topic in the boardroom.
One way to solve that is to make the topic sexier. With greater insight and understanding of the entire process of data quality management, it is also easier for company executives to envision the importance of good data management. In an effort to create that understanding, DAMA’s Data Quality Working Group developed a flow chart inspired by subway line maps. They aptly named it “Metro Model . By following the lines, you visit the 30 stations and become familiar with all the aspects involved in data quality management, at both strategic and operational levels. You visit ‘Stakeholder Analysis and Compliance’ and pass ‘Roles and Responsibilities’. ‘Risk analysis’ is covered and of course ‘Data cleansing & Data issues’, but also ‘Data suppliers &
Data lineage’, just to name a few stations on the metro map.
The working group even created an interactive online diagram to go with it. By playing with it, you get an even better understanding of the relationships between the various elements.
Whether the subject really becomes “sexy” with a subway ticket remains to be seen, but which topics in the boardroom really deserve that designation? That said, discussing the topic with the boardroom is very important, now that everyone is talking about datadriven work . DAMA’s Metro model at least makes it more playful and ensures that all components of data quality management are on the agenda.
Scan and go to DAMA’s Metro model
Keynote
Page 14-15
10:45-11:30
The invisible engine: How data quality powers Picnic’s AI-driven supply chain
Daniel Gebler (Picnic)
Hall 1
Page 18
10:15-10:45
Unlocking Business Value with Agentic AI Governance
Wouter Mertens (Collibra)
12:00-12:45
Gaan ethiek en compliance hand in hand bij AI initiatieven?
Ger Janssen (Philips)
13:15-14:00
Van Big Tech naar Big Choice: pak de regie als organisatie terug
Marloes Pomp (European AI Forum)
11:00-11:30
Gen AI-powered BI Platform: Blending AI, BI, and DSML for Decision-making
Ashwinth Solomon (Zoho Corporation)
11:45-12:15
GenAI in the Age of AI Agents: Scalable, Trusted, & for All Users
Isidora Stojanac (Dataiku)
12:30-13:00
Building a Knowledge Graph to get your Enterprise Data AI-ready Niels De Jong (Neo4j)
Hall 2 Page 19
10:45-11:15
Succesvol opzetten van een selfservice analytics data platform voor sensor data
Hedzer Keulen (Heerema Marine Contractors)
Stefan Hulsbos (Digital Power)
11:30-12:00
Impact maken van datastrategie naar operatie
Eric Vanderfeesten (Ennatuurlijk)
Britt van Veen (Digital Power)
Hall 3
Page 20
10:15-10:45
SLM Playbook - A beginner’s guide to Small Language Models
Roberto Flores (Magnum Ice Cream)
11:00-11:30
Ontwikkeling en toepassing van meertalige en multimediale patiëntinformatie met generatieve AI - Stichting Health Base
Emma de Ruiter, Channah de Haas & Eric Hiddink (Stichting Health Base)
11:45-12:15 Navigating High-Risk AI: Practical Guidance and Forthcoming EU Legislation under the AI Act
Rimma Dzhusupova (European Commission)
13:45-14:15 How Sligro powers business operations with reliable access to data
Jan Boerlage (Sligro Food Group)
12:45-13:15
Van Warehouse naar Cloud DWH: een verrassende kijk op data-logistiek
Jürgen Volder (Ploeger Logistics) Jeroen Jacobs (Infotopics)
13:30-14:00
Ontwikkeling van een Alignementsmodel: ProRail als regisserend Asset Manager
Ruurd Tissing & Frank Vermeulen (ProRail) Joost Vermeulen (BearingPoint)
12:30-13:00 Rechters over Google Tag Manager en TC-string: bedrijven zitten klem tussen cookiewet en consent-paradox
Frank de Vries (DDMA)
13:15-13:45
Van Adolescentie naar Volwassenheid: Bouwstenen voor Datagedreven Marketing
Lucas Bos (DDMA)
14:00-14:30
Agentic AI Platform & Shifting Mindsets: The Dual Transformation at TBAuctions
Hall 4
Page 21
10:45 - 11:15
Escape the AI Agent Trap – Go Live or Go Home
Lee James (Domo)
Thijs van Wijngaarden (Metriqzz Netherlands B.V.)
11:30-12:00
Hoe zorgen we ervoor dat data daadwerkelijk waarde oplevert voor onze huurders?
Jorgen Stigter & Ricky de Haas (De Alliantie) Alex van der Leer (Esri)
12:45-13:15
This lecture will be given by AWS
13:30-14:00
The Lean AI Revolution: How to Build Intelligent Systems Without Breaking the Bank (or the Planet)
Surajeet Bhuinya (DataPebbles B.V.)
14:15-14:45 From Data Maze to Data Marketplace: Air France – KLM’s Data Marketplace journey
Roberto Bonilla & Bas Lucieer (TBAuctions)
14:15-14:45
Van gedeeld workstation naar 600+ gebruikers: DCMR’s reis naar schaalbare data science
14:30 - 15:15
Trustworthy AI: kan een onbetrouwbare technologie wel betrouwbaar zijn?
Noëlle Cicilia (Winnaar Responsible AI Leader Award 2024 & AI person of the year)
14:30-15:00
De top van de ijsberg waar niemand het over heeft: het harde werk om écht AI-ready te zijn
Mark Ebergen (Springbok Agency)
15:15-15:45
Toezicht op eerlijk en veilig data delen: hier let de ACM op
Manisha Sabajo (ACM)
Marjolein Daeter (Air France–KLM)
Martijn Severijns (Datashift)
15:00-15:30
Four become One: Impact of Agentic AI at ASN Bank
Steven Laan (ASN Bank)
14:45-15:15
Datagedreven Bescherming van de Maaswaterkwaliteit
Loek de Bonth (Schone Maaswaterketen)
Pieter Vreeburg (DCMR Milieudienst Rijnmond) David Kun (Functional Analytics)
15:00-15:30
Datagedreven werken is en blijft mensenwerk!
Nico de Roo (UWV)
15:45-16:30
AI in Nederland - Quo Vadis ?
Willem Jonker Bestuursvoorzitter (AIC4NL)
16:00-16:30 Datagedreven werken werkend maken
Tjeerd de Jong (Rijkswaterstaat)
15:45-16:15
30% energiebesparing dankzij slimme data – case Goudse Verzekeringen
Frank Visscher (VB Optimum BV)
16:30-17:00
This lecture will be given by ABN AMRO
15:30-16:00 Van handjeklap naar datagedreven: zo wordt data de motor van de tweedehands voertuigenhandel
Linda van der Heijden (BAS Group)
16:15-16:45 AI Act in Motion — Towards Responsible & Ethical AI
Michelle Fisser (VCO) Menno Weij (The Data Lawyers)
15:45-16:15
Van Homo Sapiens naar Robo Sapiens: Menselijk Leiderschap in het AI-tijdperk
Lizzy Prins (High Potential Factory)
16:30-17:00
This lecture will be given by IKEA
Hall 5
Page 23
10:15-10:45
Maak kennis met de innovatiekracht van Nederlandse AI startups
Arjan Goudsblom (AIC4NL)
11:00-11:30
45 000+ AI tools? Vind razendsnel de juiste met RankmyAI
Wilco Verdoold (RankmyAI)
Hall 6 Page 24
10:45-11:15
Transforming Analytics at Rituals from a Bottleneck to a Powerhouse
Rafne ten Oever, Jeroen Vlek & Doortje de Wiljes (Rituals)
11:30-12:00
This lecture will be given by AWS
11:45-12:15
Dromen over data? We zijn gewoon begonnen en het werkte.
Rienk Minderman (Van Meeuwen Lubrication)
12:30-13:00
Dijken in Beeld: De Kracht van Drones en AI voor Waterveiligheid
Erik Vastenburg (Hoogheemraadschap Hollands Noorderkwartier)
12:45-13:15
Niet nóg een dataplatform! Hoe je data een plek geeft in de organisatie
Louis de Roo (E-mergo)
13:30-14:00 Democratizing the power of AI, enabling KPN’s strategy
13:45-14:15
From gut feeling to data-driven: How we transformed sales & marketing at TomTom
Jeroen Brouwer (TomTom)
14:30-15:00
Automating the Developer Workflow: Lessons from Building with Gemini & AI Agents at Google
Erwin Huizenga (Google)
15:15-15:45 Als alles kan, wat doe je dan?
Maarten Mantje (The Only Constant)
Hall 7
Page 25
10:15-10:45
Real World Knowledge Graph Use Cases: Connecting Enterprise Data at Scale
Boris Shalumov (Altair)
11:00-11:30
Rise of the Agents: Van traditionele Rapportages naar Besluitvormende AI-Agents
Dik Pijl (Expert) Kai van den Berg (DTX)
11:45-12:15
Niet alleen regels, maar ruggengraat: Het nieuwe Datamanagement bij a.s.r.
Talita Terra & Maarten Kramer (a.s.r.)
12:30-13:00 Lezingenzaal 7 Toestemming gevraagd?! De grootste privacy-mythe ontkracht
Julia Buhrs & Bram Hoovers (Considerati)
Hall 8 Page 26
10:30-11:00
De virtuele Service MonteurArbeidsbesparende technologie voor een toekomstbestendige industrie
Jurjen Helmus (Hogeschool van Amsterdam)
11:15-11:45 Deploying Multi-Agent Systems on Google Cloud
Timothy van der Werf (Xebia)
12:00 - 12:30 Beyond data & analytics: how our analytics platform enables our data experts to work with GenAI
Jurrit de Vries & Milou Graas (Rabobank)
12:45 - 13:15
De AI-collega die je nooit vroeg, maar wél nodig hebt
Radboud Langenhorst (AI Opener)
13:30-14:00
Rituals: How to adapt and scale an Enterprise Data Platform over 10+ product teams.
Roundtable sessions Page 26
Roundtable 1 10:30-11:15 Hoe Rabobank digitale data op grote schaal beschermt
Lorenzo Hovenier & Stefan Leever (Rabobank)
Roundtable 1
11:30-12:15
Je hebt trek in AI. Maar heb je als organisatie de basis al op orde?
Anne Wever (Mobilee) Bas Pruijn (TMC)
Roundtable 2 10:30-11:15 AI & Agents in het MKB – kansen, ervaringen en de toekomst
Rob van de Star (Hogeschool Windesheim)
Roundtable 2
11:30-12:15 Hoe gaat ACM toezien op de Dataverordening? Ga in gesprek met de toezichthouder!
Michiel van Dijk (ACM)
16:00-16:30
Digitaliseren van ongestructureerde data en kennis; hoe de inzet van AI dit mogelijk maakt
Aad Meijburg (Hienfeld Verzekeringen)
Jan Verstegen (Dialog Group)
Sven van der Meer (KPN B.V.)
13:45-14:15 Unlock the Power of Warehouse-Native Analytics: From Data Silos to Business Impact
Dejan Dragusin & Zhengguo Gu (Optimizely) 14:15-14:45 The World of Web Crawling — And AI’s Groundbreaking Impact
14:30-15:00
Jorn Wierda (Dataprovider.com)
This lecture will be given by Wobby 15:00-16:30 AI & Strategie: 5 AI-tools voor meer datagedreven beslissingen
Ruben Nieuwenhuis (CupolaXS)
15:15-15:45
Datasoevereiniteit. Er valt niet aan te ontkomen.
Ruud Alaerds (Dutch Cloud Community)
16:00-16:30
Marketing Attributie bij Centraal Beheer: Waardevolle inzichten uit meer dan 10 Jaar testen en ontwikkelen
Tjaard Prins (Centraal Beheer)
Melle Boersma (Rituals) Jasper Ginn (Xebia)
14:15 - 14:45 This lecture will be given by Amsterdam Data Academy
Roundtable 1 13:15-14:00 AI & Data Integratie
Chris Botes & Peter Vos (Magic Software Benelux & Nordics)
Roundtable 2 13:15-14:00 Future-Proof Compliance: From data jungle to data asset in the Circular & Digital Economy with the ReSOLVE framework
Michelle Fisser (VCO)
Roundtable 1 14:15-15:00 Hyperpersonalisatie in actie: van datafundament naar resultaat
Guus Rutten & Rogier de Moel (GX Software)
15:00 - 15:30 This lecture will be given by Piano
Roundtable 1 15:15-16:00 AI Succes begint met Data Kwaliteit: Lessen en Resultaten uit de Praktijk
Roundtable 2
14:15-15:00 Is jouw organisatie klaar voor Quantum?
Leo van Schie (Quantum to Market)
Roundtable 2 15:15-16:00 Doorbreken van Weerstand tegen AI: Van Bezwaren naar Mogelijkheden
15:45 - 16:15 This lecture will be given by Xebia
Arjan Surstedt (PGGM Coöperatie U.A.)
Daniel Verloop (Gemeente Montferland)
2025 marks a turning point in AI adoption. While nearly half of all organizations are working with Generative AI assistants, fewer than 40% succeed in creating real value.
Download the Data & AI Monitor now and learn more about:
The 5 biggest barriers blocking AI implementation
Concrete governance strategies for responsible AI adoption and risk management
Skills gap analysis and practical approaches to developing AI expertise
The Data & AI Monitor is an initiative of:
European AI sovereignty and what this means for your technology partnerships
Download:
AI is no longer future music. It is a daily reality in the workplace. Yet more than half of Dutch organizations lack a clear AI strategy (Integron, 2025). While executives are still looking for frameworks, employees are taking the initiative. They are experimenting with tools like ChatGPT and Microsoft Copilot to do their work faster, smarter and more creatively. Good news, you might think. But in practice, this creates an elusive situation where AI use occurs without direction, supervision or support.
AI is quietly exploding
In the workplace, the use of AI is growing at lightning speed - even under the radar. According to research by Microsoft and LinkedIn (2024), more than half of users are deliberately keeping their AI use quiet. The reason? Uncertainty. About what the implications are for their own jobs, what is and isn’t allowed and how it fits within existing processes. The result is an atmosphere of complacency. Nearly 80% of intensive AI users work with self-selected tools, far out of sight of their managers. Bring Your Own AI (BYOAI) is the new normal. But this very form of shadow IT is now leading to a fragmented and insecure AI landscape.
No strategy? Then you’re vulnerable
At the other end of the spectrum are organizations, often without a clear strategy. Which AI tools are allowed? How is data protected? What do we mean by responsible use? In the absence of such guidelines,
Author: Sianie van Kouwen
ad hoc practices arise . Some departments experiment enthusiastically with AI, while others remain wait-and-see. Especially when AI is applied in the primary process, where consistency and control are crucial, organizations without frameworks run significant risks. At the same time, the need for direction is growing. Cybersecurity and data privacy are the biggest concerns of today’s leaders. And precisely these themes come under pressure when a clear and supported AI policy is lacking.
Leadership lags behind, employees lead the way
The real bottleneck? A mismatch between adoption and leadership. Employees often get ahead of policy. Not because they like to bypass rules, but because they simply aren’t there yet. Without policies, employees don’t know where they stand. Without openness, managers don’t know what’s happening on the shop floor. And without direction, a gray area is created where innovation is not driven, but just happens. That’s risky.
The reality is that AI has long been part of our work. If not from strategy, then from curiosity or necessity. And the gap between the laggards and the frontrunners is rapidly widening. According to Microsoft and LinkedIn (2024), you will find the real AI power users in companies where leaders do not stand on the sidelines, but actively participate. Where change is encouraged and employees are trained accordingly. There, AI is not used secretly, but rather consciously and strategically deployed.
AI requires direction, not restriction Want to structure the use of AI in the workplace? These five steps are important:
• Make AI negotiable. Encourage open conversations about the tools employees are using and why. Openness is the first step to safety.
• Establish workable guidelines. Not comprehensive policy documents, but practical principles. Start small. What is allowed, what is not?
• Offer secure alternatives. Reduce shadow IT and BYOAI. Offer reliable AI solutions that fit within IT and security frameworks.
• Invest in AI skills. Provide training that goes beyond technology. Also focus on ethical and strategic AI use and creative thinking.
• Acknowledge informal use. See informal use not as a threat, but as a starting point for innovation. Employees who embraced AI early on are pioneers; involve them in your strategy.
Finally: don’t slow down, but tune in AI is already here. Employees are using it. Customers expect it. But without vision, policy and leadership, the potential remains underutilized and the risks increase. What can you do? Instead of wanting to control what you don’t see, make visible what is already happening and actively manage it.
How open is your organization about AI? And who is taking the lead?
WEDNESDAY 10 SEPTEMBER 2025
Host of the day: Irene Rompa
10:45 - 11:30
Daniel Gebler CTO
12:00 - 12:45
Ger Janssen
AI Ethics & Compliance Lead
In a world where every decision is data-driven, quality is the difference between insight and noise. At Picnic, Europe’s fastest-growing online supermarket, data quality is not just a technical challenge—it’s the invisible engine behind an AIdriven supply chain that delivers millions of groceries on time, with zero food waste. In this keynote we will share how a relentless focus on data quality fuels innovation, drives operational excellence, and reshapes the customer experience. Discover how Picnic blends automation, culture, and cutting-edge tooling to turn data into a strategic asset—delivering impact at scale. Get inspired to rethink data quality as a catalyst for transformation—not just hygiene.
AI die waarde creëert is afhankelijk van goede en representatieve data. In deze keynote zal aan de hand van een aantal praktijkvoorbeelden duidelijk gemaakt worden hoe Philips hiermee omgaat en hoe AI ontwikkelteams ondersteund worden met behulp van een “bias risk assessment tool”. Daarnaast zal praktisch ingegaan worden op andere AI gerelateerde uitdagingen en Philips’ leermomenten over AI strategie m.b.t. regelgeving en hoe je tot een goed overzicht komt van alle AI die een organisatie gebruikt.
13:15 - 14:00
Marloes Pomp
Vice President
14:30 - 15:15
Noelle Cicilia
Winnaar Responsible AI Leader Award 2024 & AI person of the year
Europa staat op een kruispunt: blijven we volger van big tech, of kiezen we voor een eigen koers? Digitale soevereiniteit vraagt visie, leiderschap en moed. In deze keynote leer je hoe je een strategie ontwikkelt voor échte onafhankelijkheid – niet door big tech af te wijzen, maar door te begrijpen waarom autonomie cruciaal is en hoe je die stap voor stap realiseert, met Europese waarden als kompas.
15:45 - 16:30
Willem Jonker
Bestuursvoorzitter
Trustworthy AI: kan een onbetrouwbare technologie wel betrouwbaar zijn?
De term “trustworthy AI” klinkt steeds vaker. Terecht ook: AI-systemen worden een integraal onderdeel van onze samenleving en moeten daarom betrouwbaar zijn. Maar hier ligt een paradox: veel AI-systemen zijn juist inherent onbetrouwbaar. Ze maken fouten, hebben vooroordelen, of gedragen zich onvoorspelbaar. Dit roept een cruciale vraag op: hoe bouwen we een stabiele samenleving op een technologie die dit fundamentele betrouwbaarheidsprobleem heeft? Dat is de vraag waar we op ingaan in deze keynote!
De keynote geeft een overzicht van de belangrijkste AI uitdagingen waar NL voor staat en hoe die effectief te adresseren.
MARLOES POMP
Vice president of the European AI Forum.
Digital sovereignty is a top priority for Europe, according to Marloes Pomp, vice president of the European AI Forum.
She advocates decentralized infrastructure and better cooperation between member states.
The Netherlands lacks direction and ambition , partly due to a lack of political clout.
Wednesday Keynote
13:15 - 14:00
I speak to Marloes Pomp the day after the Schoof Cabinet fell. “As the Netherlands, we were a wellorganized digital country for a long time. We are quickly losing that status with zero ambition at the ministries. Now all files are also down.” State Secretary Zolt Szabó had ambitious plans in preparation, but to realize them, you need a multi-year process , support and money, Pomp argues.
What are your biggest concerns regarding digital development?
“That’s really digital sovereignty. We are just incredibly vulnerable within Europe. That applies not only to government, but to almost every business. That’s not to say we can never use Microsoft or AWS again, but a plan B is just not there right now. Without action, essential services, such as payments, ports and hospitals, are at risk.”
How should that digital sovereignty come about?
“I think we need to move toward a decentralized digital infrastructure. The European Commission is ambitious and is making good plans for this, but implementation is stalling in member states such as the Netherlands. France and Germany are much more proactive.
For example, I was recently in Mannheim where a large AI hub has been set up with government and business investment. Germany has a real ambition to build the largest AI hub in Europe and could take the lead in Europe in building a European digital infrastructure. France even has plans to build its own hyperscaler along the lines of the U.S. Web giants. Some say this involves state aid. I don’t think this is necessarily negative.”
“ In Europe we sometimes need to move away from rigid competition thinking a bit more if we want to compete on a global level.”
Is it mainly about a European infrastructure or also about digital services?
“I don’t think it’s about the services. The problem now is mainly in the infrastructure and a lack of direction We cannot rely on one country or party to solve that problem.
A coordinating body should direct those decentralized base layers. The dissolution of the Dutch Blockchain Coalition, late last year, is therefore a strategic miss. The discussion around blockchain has always focused too much on cryptocurrencies, but the blockchain community has a lot of experience in setting up decentralized, modular and scalable infrastructures. That knowledge is very relevant now but is in danger of being lost.”
Will that digital sovereignty come eventually?
“It’s really crucial to sort that out. Almost no organization in Europe can survive if we don’t get that right, but a lot of people don’t realize that. We’ve been in a war for a long time, only it’s not soldiers walking down the street, it’s taking place in the digital domain. The urgency is felt much more in Eastern Europe on the borders with Russia. In Romania, they see daily the importance of digital security as a result of autonomous drones getting lost from Ukraine. That leads to immediate action, for example, at the defense department there. In Croatia, the government has made a large-scale commitment to AI education from elementary school, and Lithuania has taken a serious approach to the security of physical and digital infrastructure. The Netherlands needs to get rid of its digital arrogance by thinking we are leading the way.”
“ We need more ambition and decisiveness, as well as investment. The Netherlands can really learn from what is taking place in Eastern Europe.”
“It would be great if we do get a new cabinet soon, that there will just be a serious department with a minister for digital affairs. We have to make meters here.”
Author: Thijs Doorenbosch
As enterprises accelerate AI adoption, the challenge lies not just in innovation but in governing AI effectively to maximize business value. This session explores how Agentic AI Governance provides a structured yet adaptive framework to drive measurable impact. Through realworld case studies, we’ll uncover success stories and challenges organizations face in balancing compliance, trust, and efficiency. Attendees will gain actionable best practices for implementing AI governance at scale while preparing for the evolving regulatory and enterprise landscape.
Gen AI-powered BI Platform: Blending AI, BI, and DSML for Decisionmaking
Business challenges that were once sporadic are now persistent and widespread—impacting everyone from business users and analysts to data engineers and scientists. Learn how the latest innovations in Gen AI are reshaping the BI landscape and unlocking actionable insights for every user. Here’s what we’ll cover: What defines a truly Gen AI-powered BI platform How businesses can empower every user with Gen AI How Agentic AI is shaping BI Live demos showcasing Gen AI and Agentic AI capabilities in BI
in the Age of AI Agents: Scalable, Trusted, & for All Users
In the GenAI era, enabling everyone to build with AI is critical. Learn how Dataiku, The Universal AI Platform™, empowers teams of all skill levels to build and deliver GenAI and agentic AI products with speed, control, and confidence. Uncover approaches for integrating GenAI and orchestrating AI agents in production across functions with the right controls, access, and oversight.
- 13:00
Building a Knowledge Graph to get your Enterprise Data AI-ready
Knowledge graphs are key to taking GenAI from proof of concept to production — making applications more reliable, transparent and secure. In this session we’ll show how to build one: connect siloed data, enrich it with semantics and structure it for GenAI. With real tools, examples and steps, you’ll see how graphs prepare enterprise data for AI and unlock faster, more trustworthy results.
- 14:15 14:30 - 15:00 15:15 - 15:45 16:00 - 16:30
How Sligro powers business operations with reliable access to data
Datagedreven werken. Wie wil het niet? Dat willen lukt wel. Aan strategie ontbreekt het niet. Maar het werkend krijgen? Dat is andere koek! Tjeerd de Jong vertelt over zijn ervaringen over ‘samen DGAM doen’ in de infrasector. 10:15 - 10:45
Sligro Food Group, Dutch market leader in food service, needed to centralise data to improve retail decision-making and stay competitive. Moving away from on-premise databases, they simplified integration to GCP. Join this session to learn why centralised data is key for Sligro, why enterprises choose Fivetran over legacy tools, and how to integrate data into the cloud for real-time analytics.
Powered by Fivetran
De top van de ijsberg waar niemand het over heeft: het harde werk om écht AI-ready te zijn
Je ziet ze overal: de jubelende succesverhalen over AI. Maar is het pad naar AI echt zo vlekkeloos? Of ben je al snel de AI clown die vooral mooie woorden orakelt? En verandert dit allemaal ook wel echt de core van je business? Bij Springbok weten we dat AI méér is dan glanzende posts. Het is ploeteren, zweten, vallen en opstaan. Ja, AI kan je core business veranderen, maar alleen met lef en doorzettingsvermogen. Want achter elk succesverhaal schuilt ook de rauwe realiteit van keihard bouwen.
Toezicht op eerlijk en veilig data delen: hier let de ACM op
Naast de Data Governance Act (DGA, sinds september 2023) geldt vanaf september 2025 ook de Dataverordening. De ACM houdt toezicht op deze regels. Deze Europese wetten moeten veiliger en eerlijker datadelen mogelijk maken. In deze presentatie bespreken we de belangrijkste regels, schetsen we de kansen voor bedrijven en geven we aan waar de ACM in haar toezicht de nadruk op legt. Meer info: https://www.acm.nl/nl/ digitale-economie/data
Datagedreven werken werkend maken
10:45 - 11:15 11:30 - 12:00 12:45 - 13:15 13:30 - 14:00
Succesvol opzetten van een self-service analytics data platform voor sensor data
Hoe maak je 100 miljoen sensormetingen per dag bruikbaar voor engineers en analisten? In deze sessie laten we zien hoe Heerema met een klein datateam een schaalbaar self-service data platform bouwde met Databricks en dbt, waarmee ruwe metingen worden omgezet in betrouwbare datamodellen voor verschillende analyses en teams.
Impact maken van datastrategie naar operatie
Wat kun je bereiken met ambitieuze datadoelen? Hoe breng je die tot leven in een organisatie die midden in de energietransitie staat? Ennatuurlijk deelt hoe zij data inzetten om duurzaamheidsdoelen te behalen en écht waarde te creëren voor hun klanten. Samen met Digital Power vertellen zij het praktische verhaal achter de strategie: hoe een scherpe data ambitie is vertaald naar een concreet dataplatform en tastbare resultaten. Van datastrategie tot het inrichten van een multidisciplinair datateam: ontdek hoe visie en uitvoering hand in hand gaan en wat voor uitdagingen dat met zich meebrengt.
Van Warehouse naar Cloud DWH: een verrassende kijk op data-logistiek
Veel organisaties worstelen met een versnipperd datalandschap vol scripts en ETL-tooling die alleen door experts begrepen wordt. Ploeger Logistics laat zien dat het anders kan. Samen met Infotopics migreerde de logistiek dienstverlener haar complete datalogistiek naar de cloud – zonder verlies van continuïteit. Het resultaat: één schaalbaar platform, met een beter datafundament voor de hele organisatie. Tijdens deze sessie ontdek je de keuzes, obstakels en impact van deze transformatie.
Jürgen Volder Business Controller Jeroen Jacobs Business Unit Manager - Data Engineering en Platform Services
Ontwikkeling van een Alignementsmodel: ProRail als regisserend Asset Manager
ProRail ontwikkelde samen met BearingPoint een integraal alignementsmodel op de goederencorridor Zee tot Zevenaar. Dit model biedt inzicht in het gedrag van de spoorinfrastructuur en de factoren die dit beïnvloeden. Digitalisering speelt hierin een sleutelrol en stelt ProRail in staat om een belangrijke stap te zetten richting strategisch, data-gedreven en risicogestuurd Asset Management.
14:15 - 14:45 15:00 - 15:30 15:45 - 16:15 16:30 - 17:00
From Data Maze to Data Marketplace:
Air France – KLM’s Data Marketplace journey
Air France KLM partnered with Datashift to tackle data silos, regulatory complexity, and fragmented access by building a central data marketplace. Using Collibra and Google Cloud, they now empower the organization with trusted, governed, and self-service data to support critical operations and decision-making.
Four become One: Impact of Agentic AI at ASN Bank
ASN Bank used Agentic AI to aid the process of unifying four brands. This presentation explores two key areas: Data Lineage: This agent efficiently traced data lineage across legacy systems, accelerating the integration and providing valuable insights. Policies: This agent analyzed internal policies, greatly simplifying the complex task of creating a unified policy framework. The presentation will discuss the architecture behind these agents, showcasing the different design patterns used.
30% energiebesparing dankzij slimme data – case Goudse Verzekeringen
Hoe bespaar je tot 30% energie met data, zónder verbouwing? Frank van VBoptimum laat zien hoe slimme sturing en algoritmes gebouwen optimaliseren. Aan de hand van de case Goudse Verzekeringen ontdek je hoe zelfs een oud pand klaar is te maken voor het energiesysteem van de toekomst.
This lecture will be given by ABN AMRO
10:15 - 10:45 11:00 - 11:30
Unlock efficient AI! This playbook explores Small Language Models (SLMs) as a cost-effective alternative to large LLMs. Learn to select, deploy (local/cloud), and utilize these powerful, often open-source models for high-value, targeted tasks.
The EU AI Act is the world’s first comprehensive legal framework for artificial intelligence, introducing a riskbased approach to regulate AI systems across all sectors, with a particular focus on high-risk applications in industry. This session will guide you through the practical steps for identifying, governing, and ensuring compliance for high-risk AI in industrial environments, supported by real-world case studies and actionable recommendations. WEDNESDAY
Ontwikkeling en toepassing van meertalige en multimediale patiëntinformatie met generatieve AI - Stichting Health Base
Ontdek hoe Health Base generatieve AI inzet om patiëntvriendelijke medicijninformatie toegankelijk te maken in meerdere talen en formats. Krijg een uniek kijkje achter de schermen: van vertaling en text-to-video met een eigen AI-omgeving tot kwaliteitscontrole en praktische toepassing in de zorg. Deze sessie laat je het volledige productieproces zien, van contentcreatie tot eindgebruik.
11:45 - 12:15
Navigating High-Risk AI: Practical Guidance and Forthcoming EU Legislation under the AI Act
12:30 - 13:00 13:15 - 13:45 14:00 - 14:30
Rechters over Google Tag Manager en TC-string: bedrijven zitten klem tussen cookiewet en consentparadox
Het cookieregime is vastgelopen: je hebt toestemming nodig om toestemming op te slaan. Recente uitspraken over Google Fonts, Google Tag Manager en TC-strings tonen de absurditeit van regels die niet meer passen bij moderne technologie. Ontdek waar de cookieuitzonderingen op toestemming stranden en waarom het tijd is voor modernisering.
Van Adolescentie naar Volwassenheid: Bouwstenen voor Datagedreven Marketing
Veel organisaties hebben de data en tools, maar missen volwassen regie. Volgens het DDMO 2025 blijven marketingteams steken in adolescentie: ambitieus, maar stuurloos. Lucas Bos laat zien hoe data door structuur, discipline en cultuur wel het gewenste resultaat oplevert – aan de hand van praktijkvoorbeelden en tips waar je direct mee aan de slag kunt.
Agentic AI Platform & Shifting Mindsets: The Dual Transformation at TBAuctions
As Europe’s top B2B used-goods auction platform, TBAuctions is entering the AI era. Roberto Bonilla, Lead Data Engineer, shows how Databricks, Azure, Terraform, MLflow and LangGraph unify to simplify complex AI workflows. Bas Lucieer, Head of Data, details the strategy and change management that bring a sales-driven organization along, ensuring adoption and lasting value. Together they show tech + strategy = marketplace edge.
14:45 - 15:15 15:30 - 16:00 16:15 - 16:45
Datagedreven Bescherming van de Maaswaterkwaliteit
In deze sessie nemen we je mee in onze zoektocht naar het realiseren van datagedreven bescherming van de Maaswaterkwaliteit. Hoe kunnen we de data, die cruciaal is om de waterkwaliteit van de Maas te beschermen, tussen 11 organisaties uitwisselen én vervolgens gericht en snel duiden en verrijken? Dat is namelijk niet zo makkelijk gedaan als gezegd…
Van handjeklap naar datagedreven: zo wordt data de motor van de tweedehands voertuigenhandel
Versnipperde data, tegenstrijdige rapportages en silo’s –herkenbaar? In deze sessie ontdek je hoe wij datakwaliteit verbeterden, een breed data literacy programma opzetten voor alle lagen van de organisatie, en business en IT effectief lieten samenwerken. Met praktische voorbeelden, leerpunten en heldere stappen om ook jouw organisatie datagedreven te maken.
AI Act in Motion — Towards Responsible & Ethical AI
Join Michelle Fisser, Chair of the VCO and Founder of Compliance in Motion, and Menno Weij, Tech & Privacy Lawyer at The Data Lawyers, for a high-impact keynote that bridges the gap between regulation, innovation, and ethics in AI.
10:45 - 11:15 11:30 - 12:00 12:45 - 13:15 13:30 - 14:00
Escape the AI Agent Trap – Go Live or Go Home
Most AI Agent projects stall before reaching production. This session shares lessons from real organisations in manufacturing and retail that have deployed operational Agents using live data, clear ownership, and trusted outputs. It focuses on the practical challenges, unexpected blockers, and what it takes to turn ideas into action.
Hoe zorgen we ervoor dat data daadwerkelijk waarde oplevert voor onze huurders?
De Alliantie is een vooruitstrevende woningcorporatie die innovatie, ondernemerschap en solidariteit omarmt. We geloven in de kracht van data en AI om slimmer en toekomstbestendig te werken. In deze presentatie laten we zien hoe we stuurinformatie toegankelijk maken op medewerkers- en gebiedsniveau. Zo kunnen we gebiedsgericht werken en datagedreven interventies doen die écht waarde opleveren voor onze huurders.
Lee James Director of Strategic Partnerships and Customer Adoption
Thijs van Wijngaarden Senior Consultant
Jorgen Stigter Manager Data Competentie Centrum
Ricky de Haas BI specialist
This lecture will be given by AWS
The Lean AI Revolution: How to Build Intelligent Systems Without Breaking the Bank (or the Planet)
Discover how lean AI enables building intelligent systems quickly and affordably without compromising data privacy or sustainability. Learn from real cases like automating ad-compliant PDFs, streamlining supply chains, and optimizing drug rollout using open-source models and minimal infrastructure. Perfect for tech and business leaders aiming to drive innovation, cut costs, and scale AI solutions responsibly.
14:15 - 14:45 15:00 - 15:30 15:45 - 16:15 16:30 - 17:00
Van gedeeld workstation naar 600+ gebruikers:
DCMR’s reis naar schaalbare data science
DCMR transformeerde in vijf jaar van een klein datateam naar een professionele data-organisatie. Pieter Vreeburg deelt hoe zij dit bereikten met Agile, DevOps en tooling zoals ownR – inclusief lessen, successen en uitdagingen.
Datagedreven werken is en blijft mensenwerk!
Datagedreven werken draait om feiten, maar vooral om mensen. Want hoe goed je techniek ook functioneert, zonder draagvlak en de juiste vaardigheden binnen de organisatie wordt datagedreven werken geen succes. Ook niet met AI. In mijn presentatie laat ik zien hoe UWV datagedreven werken binnen HRM implementeert met aandacht voor visie, educatie, communicatie en agile werken. Ook deel ik hoe de inzet van AI ons in de nabije toekomst kan helpen en met welke beperkingen we moeten zien te dealen.
Van Homo Sapiens naar Robo Sapiens: Menselijk Leiderschap in het AItijdperk
AI-transformatie zonder je mensen te verliezen De AI-trein rijdt. Spring je erop of word je overreden? Ontdek hoe je AI omarmt zonder je organisatie koud te maken. Leer de 3 grootste AI-fouten, krijg praktische eerste stappen en ontdek welke menselijke kwaliteiten goud waard worden. Voor leiders die hun team willen voorbereiden op de toekomst zonder technische achtergrond. AI neemt je baan niet over. Iemand die handig is met AI wel.””
This lecture will be given by IKEA
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- 10:45
Maak kennis met de innovatiekracht van Nederlandse AI startups
Nederland beschikt over een indrukwekkend ecosysteem van innovatieve AI-startups en scale-ups. Tijdens deze sessie leer je hoe de AI Coalitie voor Nederland (AIC4NL) deze bedrijven ondersteunt bij het realiseren van (internationale) doorbraken via het programma Breaking Barriers. Tevens maak je kennis met inspirerende pioniers op het gebied van dataconsolidatie, privacy vriendelijke data-analyse en digitale authenticiteitscontrole.
45 000+ AI tools? Vind razendsnel de juiste met RankmyAI
Er zijn 45.000+ AI-tools en evenveel ‘experts’ die ze vaak zonder onderbouwing ophemelen of afbranden. Wilco Verdoold (Hogeschool van Amsterdam) helpt met RankmyAI de juiste tool te vinden via ’s werelds grootste transparante dataset. Geen productdemo, maar inzichten: hoe ziet het Nederlandse AI-landschap eruit, welke trends spelen, en hoe kies je slim. Voor iedereen die verder wil kijken dan de volgende shiny tool.
- 12:15
Dromen over data? We zijn gewoon begonnen en het werkte.
Dromen over data zit vooruitgang vaak in de weg. Beginnen werkt! Van Meeuwen vertelt hoe de eerste resultaten zijn bereikt en de eerste klant van digitaal smeertechnisch onderhoud werd voorzien. Inmiddels smeert Van Meeuwen datagedreven meer dan 80.000 machines in de Benelux.
- 13:00 13:45 - 14:15 14:30 - 15:00 15:15 - 15:45 16:00 - 16:30
From gut feeling to data-driven: How we transformed sales & marketing at TomTom
TomTom’s Sales & Marketing teams made a fundamental shift — from intuitionbased decisions to data-driven steering. In this session, TomTom shares how commercial data was centralised, cleaned, governed, visualised, and translated into actionable insights, resulting in tangible impact on both culture and performance.
Automating the Developer Workflow: Lessons from Building with Gemini & AI Agents at Google
How do organizations move from predictive ML to impactful Generative AI? This session presents a strategic blueprint for this transition. It showcases how Google leverages Gemini and AI Agents to automate complex engineering workflows, achieving an 80% reduction in time spent on issue resolution. Gain a framework for fostering innovation, enabling teams, and driving measurable results with LLMs.
Dijken in Beeld: De Kracht van Drones en AI voor Waterveiligheid
AI verandert de inspectie van waterkeringen ingrijpend. Binnen HHNK’s programma Waterveiligheid 2030 ontwikkelen we met partners slimme algoritmes voor schadeherkenning via drones en beeldanalyse. Dit maakt inspecties efficiënter, objectiever en toekomstbestendig in een tijd van versnelde klimaatverandering.
Als alles kan, wat doe je dan?
Data, creativiteit en AI. De combinatie brengt onbegrensde mogelijkheden die veel te vaak verlammen. Maarten Mantje toont hoe experimenten en slim ondernemerschap sneller tot innovatie leiden dan eindeloze gepraat. Met voorbeelden van Stookers, zijn ginbedrijf, laat hij zien waarom ‘gewoon beginnen’ vaak de beste strategie is. Stop met praten, begin met doen!
Erik Vastenburg Programmamanager Waterveiligheid
Digitaliseren van ongestructureerde data en kennis; hoe de inzet van AI dit mogelijk maakt
Omdat Hienfeld speciale verzekeringen biedt, is de combinatie van flexibiliteit en specialistische kennis van groot belang. Om hierin te kunnen digitaliseren is de beschikbaarheid data (met name ongestructureerde data en kennis) cruciaal. Aad Meijburg en Jan Verstegen nemen je mee in hun ervaringen en oplossingen, met de hulp van AI.
Analytics at Rituals from a Bottleneck to a Powerhouse
We tell about our journey of going from a fragmented analytics landscape towards a platform with curated datasets and consolidated metrics for analytics within Rituals using the concepts of data domains, data contracts and defining everything-as-code.
Powered by Eraneos
This lecture will be given by AWS
Niet nóg een dataplatform! Hoe je data een plek geeft in de organisatie
Veel organisaties investeren fors in moderne dataplatformen, maar zien dat adoptie achterblijft. In deze sessie leer je hoe je vanuit ons 7-stappen model voor datagedreven werken (stap 2 - ‘Maak een plan’ en stap 6 - ‘Deel de kennis’) zorgt voor platformkeuzes die gedragen worden door de business. Inclusief tips om Azure, Databricks of Fabric niet alleen technisch, maar ook organisatorisch te laten landen.
deDoortjeWiljes
10:45 - 11:15 11:30 - 12:00 12:45 - 13:15 13:30 - 14:00 14:15 - 14:45 15:00 - 16:30
World of Web Crawling — And AI’s
While AI gets all the spotlight, the data powering it often goes unnoticed. This talk explores web crawling, how it works, how organizations leverage it, and how recent advances in AI are unlocking entirely new ways for companies with large volumes of structured data to make that information searchable, accessible, and genuinely valuable.
AI & Strategie: 5 AI-tools voor meer datagedreven beslissingen
Workshop voor ondernemers en leidinggevenden die AI willen inzetten voor strategie en besluitvorming. Leer hoe je tools als Claude, ChatGPT, Grok, Perplexity en NotebookLM gebruikt voor betere keuzes, marktanalyse en positionering. Ontdek hoe je AI structureel toepast binnen je organisatie en versterk je leiderschap met slimme, datagedreven inzichten. Praktisch, actueel en direct toepasbaar.
Democratizing the power of AI, enabling KPN’s strategy
To fully unlock the potential of AI within KPN, scaling is key. Therefore KPN focuses on 4 pillars: AI Literacy, Governance, end-to-end implementation with business, IT, data and AI, and the expansion of our technical infrastructure. Together, these elements support the democratization of AI capabilities across the organization. With the emergence of Generative AI—especially Agentic AI—broad enablement has become even more critical. In this session, KPN will share organizational opportunities and challenges related to AI adoption at scale, and how it utilizes Dataiku as the central Data Science platform to drive this transformation.
Powered by Dataiku
10:15 - 10:45 11:00 - 11:30 11:45 - 12:15 12:30 - 13:00
Real World Knowledge Graph Use Cases: Connecting Enterprise Data at Scale
This session explores practical knowledge graph use cases that help organizations connect enterprise data at scale. Learn how intelligent graphs turn complexity into advantage, enabling real-time impact analysis, adaptive AI, and decentralized decisions. Discover real-world examples and the tech stack behind this data-driven transformation.
Rise of the Agents: Van traditionele Rapportages naar Besluitvormende AIAgents
In de razendsnel veranderende wereld van data en informatie zijn technologieën zoals LLM (Large Language Models) de sleutel tot ‘praten met onze data’. Ontdek de transformatie bij Expert van traditionele rapportages naar Expert-Ise, de AI-Agent die actief helpt bij besluitvorming.
Niet alleen regels, maar ruggengraat: Het nieuwe Datamanagement bij a.s.r.
Bij a.s.r. bouwen we sinds twee jaar aan goed datamanagement. Geen sprint, maar een reis van verbinden en volhouden. Met een gedeeld beleid, betrokken data stewards en aandacht voor mensen boven techniek, groeit het besef dat datakwaliteit een gedeelde verantwoordelijkheid is. Wat begon als ‘ons onderwerp’, is nu erkend in het hoger management: betrouwbare data is cruciaal voor risico’s, innovatie en klantwaarde.
Powered by IntoDQ
- 14:15 14:30 - 15:00
Unlock the Power of Warehouse-Native Analytics: From Data Silos to Business Impact
Struggling with fragmented data and slow insights? Companies using warehousenative analytics experience 30% faster time-to-insight. In this session, discover how to eliminate data silos, build realtime dashboards, and drive data-driven experimentation directly from your warehouse. Learn how to transform your analytics infrastructure and achieve faster, more impactful results.
This lecture will be given by Wobby
Datasoevereiniteit. Er valt niet aan te ontkomen.
Jij bent als organisatie verantwoordelijk voor je vertrouwelijke en persoonlijke data. Maar hoe hoe je die controle? In deze presentatie krijg je alle aandachtspunten uitgelegd.
Toestemming gevraagd?!
De grootste privacy-mythe ontkracht
Toestemming lijkt dé manier om persoonsgegevens te verwerken, maar is vaak niet voldoende of toegestaan. Veel organisaties gebruiken het standaard, terwijl het juridisch niet altijd houdbaar is. Bram Hoovers en Julia Buhrs laten zien waarom toestemming geen vrijbrief is en hoe je AVG-proof werkt met andere grondslagen. Met praktijkvoorbeelden en inzichten krijg je grip op dataverwerking binnen jouw organisatie.
Dragusin Senior Product Manager Zhengguo Gu Senior Staff Statistician/Data Scientist
Marketing Attributie bij Centraal Beheer: Waardevolle inzichten uit meer dan 10 Jaar testen en ontwikkelen
Centraal Beheer werkt al meer dan 10 jaar met verschillende attributie modellen. Tijdens deze sessie neemt Tjaard je mee in de Marketing Attributie reis van Centraal Beheer. Wat zijn de lessons learned in de afgelopen 10 jaar? En wat zijn mogelijke valkuilen? Waar moet je beginnen? En hoe maak je de ‘juiste’ keuze in de totstandkoming van je modellen? Verschillende technieken komen aan bod, waaronder multi-touch attributiemodellen (MTA), marketing- of media-mix modellen (MMM) en experimenten, en we onderzoeken hoe deze methoden kunnen bijdragen aan het optimaliseren van marketingstrategieën. Een aantal mythes over Marketing Attributie zullen worden ontkracht en er wordt ingegaan op het belang van data, kennis en context bij het bouwen van effectieve modellen.
Director
10:30 - 11:00 11:15 - 11:45
De virtuele Service Monteur - Arbeidsbesparende technologie voor een toekomstbestendige industrie
De Nederlandse industrie kampt al jaren met een toenemend personeelstekort. Veel technici en onderhoudsmonteurs bereiken binnen enkele jaren hun pensioenleeftijd, terwijl er onvoldoende instroom is van jongere technici om hen te vervangen. Sleuteltechnologieën voor digitalisering, zoals generatieve AI, large language models (LLMs) en digital twins, bieden een oplossing in de vorm van de virtuele servicemonteur. Dit systeem combineert real-time data met storingsanalyses en documentatie om monteurs te ondersteunen bij het op afstand diagnosticeren en oplossen van problemen.
Powered by Xebia
Discover how to design and deploy powerful multi-agent systems on Google Cloud. Cloud & AI Consultant Timothy van der Werf demonstrates how agents collaborate, share tasks, and autonomously handle complex processes using technologies like Agentspace. This session delivers practical examples and insights for anyone looking to apply AI to data-driven innovation, customer engagement, or operational optimization.
12:00 - 12:30 12:45 - 13:15
Beyond data & analytics: how our analytics platform enables our data experts to work with GenAI
Rabobank scales GenAI through complementary approaches. First, with an analytics platform strategy: a lab platform for low-friction experimentation on non-sensitive data, and a lab platform for secure development and a governed promotion path to production. Second, the availability of re-usable GenAI building blocks that enable secure, responsible, standardized, and scalable development of GenAI solutions.
Powered by Xebia
De AI-collega die je nooit vroeg, maar wél nodig hebt
95% van business-AI-projecten levert geen resultaat (MIT, 2025). Radboud Langenhorst (AIO) laat zien hoe het wél kan: AI-agents als collega’s die veilig met je data werken, alles onthouden en direct waarde toevoegen. Geen losse experimenten, maar een complete aanpak – van platform tot begeleiding – waardoor data rendeert en AI impact maakt op mensen én P&L.
13:30 - 14:00 14:15 - 14:45 15:00 - 15:30 15:45 - 16:15
Rituals: How to adapt and scale an Enterprise Data Platform over 10+ product teams.
In this session Rituals Beaty & Tech will explain how they transformed their organisation from project based to product based tech teams and how they rebuild their enterprise data platform to fit the new product way of working laying the foundation for operational, analytical and AI workloads.
This lecture will be given by Amsterdam Data Academy
This lecture will be given by Piano
This lecture will be given by Xebia
10:30 - 11:15 11:30 - 12:15 13:15 - 14:00 14:15 - 15:00 15:15 - 16:00
Hoe Rabobank digitale data op grote schaal beschermt
Tijdens onze ronde tafel sessie gaan we aan de slag met hoe Rabobank zich beschermt tegen het verzamelen van slechte data afkomstig uit de website en de app. Door het toepassen van verschillende technieken leggen we de fundering voor solide data kwaliteit om ons klaar te stomen voor, onder andere, AI.
Je hebt trek in AI. Maar heb je als organisatie de basis al op orde?
Je wilt met AI aan de slag. Maar is je organisatie eigenlijk wel klaar? In deze interactieve ronde tafel ontdek je wat er nodig is om AI uit de koelkast te halen. Aan de hand van stellingen ga je met vakgenoten in gesprek over o.a. datakwaliteit, eigenaarschap en het technisch fundament. Waar sta jij met je organisatie? Mobilee en TMC begeleiden deze sessie. In 45 minuten krijg je scherpe inzichten en ontdek je of je AI bij jou uit de koelkast kunt halen of (nog) beter even kunt laten staan.
AI is hot, maar veel organisaties lopen al vast vóór het algoritme draait. In deze interactieve roundtable bespreken we de échte voorwaarden voor succesvolle AI: van datastromen tot integratie. Geen buzzwords, wel praktijkervaring, inzichten en herkenbare uitdagingen. Doe mee, deel je lessons learned en ontdek hoe je AI-projecten slimmer en duurzamer op de rails zet.
Hyperpersonalisatie in actie: van datafundament naar resultaat
Zet de stap van ‘One-to-All’ naar ‘One-to-One’: ontdek hoe hyperpersonalisatie je marketingefficiëntie verhoogt én je klanten blijer maakt.
AI Succes begint met Data Kwaliteit: Lessen en Resultaten uit de Praktijk
Veel organisaties willen aan de slag met AI, maar vergeten dat betrouwbare data de basis vormt. In deze sessie deelt PGGM hoe het datakwaliteit en data observability structureel heeft verbeterd. Wat werkte wel, wat niet? En hoe zorg je dat collega’s zich verantwoordelijk voelen voor data? Een sessie vol praktijkinzichten, concrete resultaten en eerlijke lessen.
AI & Agents in het MKB – kansen, ervaringen en de toekomst
De wereld van ondernemen verandert razendsnel. AI en slimme Agents zijn geen toekomst meer, maar praktijk. Wat betekent dit voor het MKB? In deze ronde tafel gaan 15 ondernemers in gesprek over hun ervaringen, kansen en uitdagingen met AI. Hoe staat jouw organisatie ervoor, welke hobbels kom je tegen en hoe ziet je bedrijf er over twee jaar uit? Deze sessie biedt inspiratie, kennisdeling en concrete handvatten om kansen te benutten en risico’s te beperken.
Hoe gaat ACM toezien op de Dataverordening?
Ga in gesprek met de toezichthouder!
Vanaf 12 september 2025 geldt de Dataverordening: data uit apparaten moeten tegen eerlijke voorwaarden beschikbaar zijn. Organisaties moeten makkelijker kunnen overstappen tussen clouddiensten en deze kunnen combineren. De ACM ziet toe op deze Europese regels. In deze sessie gaat u met de toezichthouder in gesprek: waar moet u op letten? Welke data wilt u? Waar loopt u tegenaan?
Na een korte introductie is het woord aan u. Meer info: https://www.acm.nl/nl/digitaleeconomie/data
Future-Proof
Compliance: From data jungle to data asset in the Circular & Digital Economy with the ReSOLVE framework
The EU regulatory landscape is a jungle. ESG rules like CSRD, CSDDD, RED III and EU Taxonomy overlap with AI Act, Data Act, NIS2 and EIDAS2 — creating a fastchanging data jungle with costly duplicate or “trash” data. Learn how the McKinsey ReSOLVE Framework turns this into a structured asset, meeting multiple compliance needs, reducing risk and cost, and aligning compliance, tech/data and business teams.
Is jouw organisatie klaar voor Quantum?
Quantum is hot. Vrijwel elke week is er nieuws over een nieuwe ontwikkeling die Quantum dichterbij brengt. Maar waar staan we nu? En wat gaat Quantum betekenen voor jouw bedrijf? Is het een bedreiging om dat het de encryptie kan kraken? Of juist een kans omdat het taken veel sneller kan uitvoeren? Praat mee tijdens deze roundtable session.
Doorbreken van Weerstand tegen AI: Van Bezwaren naar Mogelijkheden
AI-implementatie stuit vaak op weerstand. Deze interactieve roundtable behandelt praktische strategieën om patronen van tegenwerking te doorbreken. We gaan in gesprek over spanningsvelden, mitigatiestrategieën en hoe je bezwaren kunt omzetten naar constructieve dialoog. Breng je eigen uitdagingen mee - samen ontdekken we oplossingen.
SANDER KERSTENS Director Data & Analytics Vanderlande
As Director Data & Analytics, Sander Kerstens works every day to build a future-proof data organization within Vanderlande. “Vanderlande is a technically strong company with an enormous drive to innovate. But without good data and the right people to work with it, you won’t get anywhere. With a team of 100 central and decentralized data experts worldwide, we bring data to life and translate it into real business value. We do this across the organization, from supply chain to sales. That variety, the continuous search for opportunities and the desire to improve: that makes every day a learning day.”
Founded in 1949 as a technical trading company, Vanderlande grew into a global player in automated logistics systems. With solutions for airport baggage handling, warehouse automation and parcel logistics, Vanderlande supports customers such as Amsterdam Airport Schiphol, Albert Heijn and DHL. During Data Expo 2025, Sander will provide valuable insights into how to set up and deploy a modern data platform in a complex, international organization.
Data-driven work is not new Like many other organizations, Vanderlande has been working data-driven for years. That is not a recent development. The big difference from five years ago? Data is now connected much more broadly and intelligently - across applications. “Data used to be analyzed mainly within the boundaries of a single system, such as ERP, CRM or ticketing. Reports often remained silobased,” Kerstens says.
About 10 years ago, Vanderlande was already taking steps to bundle data from key applications into a central data warehouse
“IT managed and defined the logic, which led to valuable but static insights. With the adoption of our data platform, that has changed. The focus is now on flexible, domain-centric collaboration and dynamic data models that work across applications. That makes data not only more accessible, but also much more powerful as a strategic tool,” adds Kerstens.
Data that thinks ahead
Whereas five years ago it was mainly about insights from the “Big Five” such as ERP, HR and planning, the playing field is now much broader. Today, companies work with data from dozens of systems, sometimes as many as 50. That broadening requires a different approach. “Instead of one central data warehouse focused on fixed use cases such as financial reports or workforce management, we now work with a modular data platform. This allows us to switch faster, report more flexibly and answer ad hoc questions. Also outside the traditional frameworks,” Kerstens said. And with the arrival of AI, Vanderlande is taking the next step.
“ Whereas before we only answered known questions, now we can also proactively generate insights based on patterns we didn’t know ourselves yet. That’s the real difference between then and now: data that doesn’t just look back, but thinks ahead.”
The realization that change is necessary
Moving from a traditional data warehouse to a modern data platform starts with one crucial insight: your current setup is no longer adequate. “Within Vanderlande, the ambition grew to become truly data-driven. Faster, smarter, and insight-driven. But our existing data warehouse could no longer keep up with those ambitions,” Kerstens says. So how do you proceed?
Step 1: Realize that you need to change. That realization prompted a strategic data initiative within the company.
Step 2: Design a vision as well as a route. “What specifically does it mean to be a data-driven organization? How do you set up governance? What will be your operating model? What value do we want to deliver?” Those questions were central to the program that followed.
Step 3: From vision to practice. Kerstens: “Then you have an impressive strategic program on paper. But eventually you have to start implementing. And then it really begins. Use-case by use-case the platform was built, and the strategic ambition became business as usual.”
Learning by doing
“Over the past 4.5 years, we have found that some ideas turn out differently in practice than they were conceived on paper,” Kerstens says. “At the time, for example, the data mesh philosophy was on the rise. That really captured our imagination. But, translating the philosophy into practice is a quest.” He finds that very experience valuable. “We are now at the point where we dare to reconsider choices made back then. Things we held on to for a long time, we are letting go in the interest of scalability and practicality. And that’s where simplicity and effectiveness come together.”
Building a modern data platform requires hefty investments: in people, cloud, licensing, governance and tooling. But how do you make that value tangible?
“Investments in data are difficult to link to business results, and the lead time is also relatively long. Those are nice challenges,” Kerstens says. “Strategic programs last
11:45 - 12:15 Thursday Keynote
an average of a year with us. After that, something has to become business-as-usual. But in that time so much can happen, how do you keep the data program relevant, and how do you keep stakeholders involved and enthusiastic?”
So why does such a journey take a long time? Because it’s basically two development paths running in parallel. On the one hand, you work on the platform itself - the basic functions. “For example, we have added Natural Language Processing (NLP)-based functionality to the platform. This allows users to interact with data products in plain language. That suddenly makes the platform much more widely accessible,” Kerstens explains.
At the same time, you unlock more data sources step by step. Kerstens: “In the beginning you don’t have anything yet. So you choose five applications that are strategically important, for example, for supply chain planning or financial consolidation. That will keep you busy for the first two years, with fifteen to twenty applications. Only after that can you focus on new areas, such as learning analytics or sales forecasting.”
And then there is the reality of constant change. “We’re acquiring new applications at the same rate we’re unlocking the old ones,” Kerstens says. “Then another ESG tool comes along, or a company acquisition brings 25 new apps. It’s never finished.”
What is cutting edge today may be obsolete tomorrow. Vanderlande also experiences this reality on its data journey. “Four years ago, we weren’t doing AI at all,” Kerstens says. “AIOps felt like something from another world. By now it is an integral part of what we do. You’re constantly learning to adjust.”
“ You build something and a year later you’re already phasing it out. Kill your darlings, because the world changes at lightning speed.”
That speed requires agility. “We have learned to distinguish between what is core and what is not. The core must be robust and reliable. But around it, you want to stay flexible, so you can move quickly with it. Think analysis techniques, AI models or visualization tools. That’s the dynamic part.”
At the same time, security, privacy and compliance are becoming increasingly important and complex. The organization is also growing with that. It really is a never-ending journey. “We now have a solid, future-proof foundation in place - something we will reap the benefits of within Vanderlande for a long time to come. A wonderful result that we have achieved together,” Kerstens concludes.
Author: Thijs Doorenbosch
The world of data is changing at lightning speed. To move along with this dynamic, we have formed an advisory board at Data Expo: a team of data-driven experts who provide us with ideas, insights, and feedback. This think tank helps us stay close to the trends, developments, and needs of the market. They are our sparring partner, reality check, and source of inspiration all in one. Thanks to their input, we make Data Expo more relevant, future-proof, and even more valuable for both exhibitors and visitors.
Prof. Dr. Frans Feldberg Professor of Data-Driven Business Innovation, Vrije Universiteit Amsterdam
Duco Roolvink Solution Owner Delaware
René de Boer CEO Digital Power
Maarten van den Outenaar Chief Data Officer, Royal Schiphol Group
Walter van der Scheer Global Head of Marketing Cloud Xebia
Ruud Alaerds Managing Director Dutch Cloud Community
Peter Verkoulen Program Director, Centre of Excellence for Data Sharing & Cloud, TNO
Tjarda Voorneman Head of Strategic Partnerships
Arjan van Oosterhout Owner, Bureau AvO
“As a member of the advisory board for Data Expo 2025, it’s inspiring to work alongside diverse market specialists on creating an event that delivers maximum value for all parties involved. It’s great to contribute my expertise and experience to this initiative in this way.”
René de Boer
LOUIS DE ROO
Datastrateeg
| e-mergo
Many organizations fail to reap the benefits of data-driven working. This is often because management lacks sufficient insight into the business processes where data analysis really delivers a competitive advantage. Data strategist Louis de Roo of E-mergo explains in a presentation during Data Expo how you can achieve success with data-driven working using a 7-step plan, which may also be surprisingly beneficial. He gives us a sneak preview here.
In almost every organization, some thought has been given to introducing data-driven work. After all, no manager likes to manage on assumptions and feelings, with a few exceptions. Choices are best supported by facts and figures. In recent years, Dutch organizations have therefore invested heavily in infrastructure and services to enable data-driven working. However, recent research by consulting firm Gartner shows that only 22% of the surveyed organizations know how to get concrete financial added value from the use of data, analytics and the deployment of AI.
• Many organizations fail with data-driven work because the strategic goal is missing.
• Success starts with asking the right questions, not technology or data volume.
• Small, targeted applications deliver more than expensive data lakes with no focus.
• Employees and data quality are crucial; governance and training make the difference.
As a data strategist at E-mergo, Louis de Roo speaks to many companies and knows where the pain point is: “Management wants to collect all available data right away and then expects the benefits to surface automatically. The focus is often primarily on improving reporting.” With nice dashboards and reports, management thinks it can get a better grip on the money flows within the company and thus achieve efficiency gains. This is disappointing. “Apart from financial service providers for whom finance is the core process, in most organizations the administration is not the process by which they can distinguish themselves from the competition. You do that by increasing your own competitiveness or by achieving more margin. Ideally, of course, you do both.”
To reach that point, management must go back to the drawing board. With good questions and in-depth discussions, a clear picture emerges of the activities with which the organization actually adds something to what is available in the market. At Bol. com or Amazon, for example, it is clear that the size of the customer base allows these platforms to advise customers on a followup purchase with each purchase. “Those who bought this, often ordered that too,” it says under all shopping baskets. Not every store or manufacturing company has that capability, but company data can show, for
example, the minimum quantity of products delivered each month. This can be used to negotiate more advantageous purchases in contract negotiations with suppliers.
There is no universal rule; each company must get clear internally which insights from data can truly deliver strategic value Then the data sources must be sought for creating those insights. “Start small; it is not necessary to create an entire data lake to achieve demonstrable results,” de Roo argues. This sometimes results in embarrassing situations when it turns out that the investments previously made in cloud storage and applications were actually for naught. “Some organizations do indeed decide to phase it out, and I don’t yet know of a case where management later regretted it. Still, many organizations keep their expensive datalake on hoping to benefit from it someday,” is his experience.
“ It is not necessary to build a whole data lake to achieve demonstrable results.”
Data quality must fit the purpose Far more important than the amount of data collected is attention to the quality of the data. It should be kept in mind that 100% correct data does not exist.
NOT ANOTHER DATA PLATFORM! HOW TO GIVE DATA A PLACE IN THE ORGANIZATION. THE APT TITLE OF LOUIS DE ROO’S LECTURE. WOULD YOU LIKE TO KNOW MORE ABOUT THIS AND STEAMLINE DATA IN YOUR ORGANIZATION?
THEN COME ON WEDNESDAY FROM 12:45 - 13:15 TO LECTURE ROOM 6. DON’T MISS IT!
De Roo: “What matters is data quality that is good enough for the purpose. It makes a difference whether you need to create a construction drawing for a nuclear reactor based on data or whether you want to discover a trend in the sale of sports socks.” In the former case, the data must be correct to many decimal places; in the latter, it does not matter if it contains a few input errors. However, there must be a clear picture of the bandwidth within which each data stream should be allowed to vary. Establishing those bandwidths is a key focus for management early in the journey to successfully implementing data-driven work. “That’s part of the data governance policy. There should also be signaling when unexpected values occur so you can respond to that if necessary.” According to de Roo, there are enough statistical tools available to implement that data quality policy automatically.
Recognize role of employees
Technology has a clear role in data-driven work, but human creativity is leading the way. This is important not only at the beginning of the journey in answering the questions of what you want to find out by using data and why. However, datadriven working is an ongoing process to which everyone in the organization can contribute. Employees often see new possibilities and opportunities. That’s why management should pay close attention to training on how to use the tools and create the opportunity to experiment, de Roo believes. “It’s about democratizing the data. Familiarize employees with their own dashboards and show them how to add new features. That should also be part of onboarding new employees and periodic
12:45 - 13:15 Wednesday Hall 6
in-service training.” Democratizing data also means giving employees a responsibility in working with the data. “That too is part of data governance; that everyone knows how you handle personal data and other sensitive information. Especially now that more and more AI tools are becoming available with which you can easily perform very large operations. Then you can also do great damage. That awareness should always be addressed in training sessions.”
“ It’s about democratizing data. Familiarize employees with their own dashboards and show them how to add new functions.”
Good support for the data environment is also a prerequisite for the success of datadriven work. Responsibilities for managing the data and updating the software, replacing certificates and applying patches must be clearly assigned. “If you don’t think carefully about how you secure that, you
dig a trap. You then know that things will go wrong at some point, and if it turns out that the data has been incorrect for some time, the trust in the data within the organization is immediately gone. The more dependent people are on a dashboard, the shorter it takes them to go looking for a workaround.”
Despite good support for the data environment, crucial components can fail temporarily or for long periods of time, for example due to cybercrime or geopolitical developments. That’s why it’s important to properly assess the dependency of the systems . De Roo: “Actually, you then go back to the beginning: determining what the key process within the organization is. On that basis, you create a minimum viable product of a new system, first on paper. That means that from a data perspective you actually create a process that you can also do with pen and paper. In that case, life becomes extremely uncomfortable and unpleasant, but once you have a clear understanding of what the basic process is, what questions absolutely have to be answered in it and what decisions play a role in it, you also know how to do it without digital tools. You hope, of course, that it will never be necessary.”
Keynote
Page 38-39
10:45-11:30
AI: in de beperking toont zich de meester!
Frans Feldberg (Vrije Universiteit Amsterdam)
12:00-12:45
Beyond Boundaries: The convergence of Data, Analytics and AI to redefine the future
Vinay Kumar (AWS)
Tommaso Paracciani (HEMA)
Hall 1 Page 44
10:15-10:45
Connecting Culture with Knowledge Graph: The Rijksmuseum Collection Online
Peter Gorgels (Rijksmuseum) Erwin Verbruggen (Q42)
11:00-11:30
Modular Working with a Knowledge- and Data Platform: Building Blocks for a Flexible Ecosystem
Julian de Ruiter (Xebia) Teus Kappen (UMC Utrecht)
11:45-12:15
Van Keukenfabriek naar Datagedreven Organisatie: Hoe Bribus Data inzet voor Groei en Klantgerichtheid
Wim Diersen (Bribus Keukens)
12:30-13:00 Governance met een glimlach: automatiseren, verbinden en gewoon dóén
Joost van Kempen (Gemeente ‘s-Hertogenbosch)
13:15-14:00
Signal Over Noise: Elevating Data Quality to Unlock Customer-Centric Growth
James Topping (Uber)
14:30-15:15
Data science and AI in military operations
Roy Lindelauf (Ministerie van Defensie)
15:45-16:30
Stop met praten over AI en data. Dit is hoe je het doet.
Tom Pots (Gemeente Zaanstad)
13:45-14:15
FreeWheelen: AI voor een stad zonder drempels
Boen Groothoff & Vishruth Krishnan (Gemeente Amsterdam)
14:30-15:00
Predicting the Unpredictable: Machine Learning at the Heart of Crisis Simulations
Oleg Bogatchev & Victor Deplasse (Euroclear)
15:15-15:45
Van stagnatie naar succes: technologie-implementatie in de publieke sector
Jessica Entken & John Goedee (Optimale Samenwerking)
16:00-16:30
Transforming HR data into a strategic business asset at ASML
Hall 2 Page 45
10:45-11:15
Samen sneller personaliseren: marketing en data verbonden via een Composable CDP
Wouter Stolk (Digital Power) Max Meijer (Transavia)
11:30-12:00
10 Pitfalls of Developing Impactful AI Powered Products, and How to Prevent Them. Firsthand Experience from Wolters Kluwer Schulinck & Mozaik
Vincent Hoogsteder (Mozaik)
Dennis Maas (Wolters Kluwer Schulinck)
12:30-13:15
Hoe AI (sl)echt gebruikt wordt, lessen uit tientallen workshops
Maartje Vennema
Hall 3 Page 46
10:15-10:45
Improving Social Safety with Computer Vision: A Collaboration Between NS and KickstartAI
Sophia Zitman (KickstartAI)
11:00-11:30
Scaling a global data platform: 3 lessons learned
Hanna Mironchyk & Gavin Morgan (Rabobank)
11:45-12:15
Beyond theory: Practical lessons from 4 years of data platform evolution
Sander Kerstens (Vanderlande)
12:30-13:00
Rechters over Google Tag Manager en TC-string: bedrijven zitten klem tussen cookiewet en consent-paradox
Frank de Vries (DDMA)
13:45-14:30
Paneldiscussie: Model Collapse: Risico’s, Signalen en Oplossingen in AI
Maartje Vennema
13:45-14:15
This lecture will be given by Isala
Hall 4 Page 47
10:45-11:15
Automated insights: uncovering insights from customer data with the use of OpenAI.
Nelly Dua & Irin Otto (ABN AMRO)
11:30-12:00
Van versnipperd Datalandschap naar één Bron van de waarheid: de transformatie van Normec met Microsoft Fabric
Victor Oudsen (Normec Group) Rik Smink (9altitudes Business Analytics)
12:45-13:15
Solving for Urgency: How GenAI Accelerates Access to Life-Saving Trials
Marshall van Beurden (myTomorrows) Guy Kfir (AWS)
13:30-14:00
Data with Purpose: EBMT’s Approach to Sharing and Governing Insights
Jordy Vercammen (Agiliz) Ignacio Garcia (EBMT)
14:15-14:45 Van Data naar Daadkracht: 360° Klantinzicht met AI
14:30-15:00
Data Governance & AI : The Positive Feedback Loop
14:45-15:15
Transforming Dairy with Data: Insights from Lely’s Robotic Innovations
Simon Krejci (Lely)
15:30-16:00
Hoe Personal Branding en AI mijn carrière in tech versneld hebben
Femke Cornelissen (Dutch Women in Tech)
Maartje Potma (ASML)
16:15-16:45
This lecture will be given by Healthplus.ai
Paul Groth (University of Amsterdam)
15:15-15:45 Van data als een asset naar data als een product.
Marco Westhof & Robert Coret (ProRail)
Maurits van Slobbe (K-parts)
15:00-15:30
Meten = weten maar meten + analyseren = besparen
Ton van Ewijk & Udo Zwart (energy-IO BV)
15:45-16:15
Van strategie naar toepassing: genAI en ethiek in regionale datainfrastructuur
16:00-16:30 From Trams to Terabytes HTM’s Journey to a Smart Data-driven Future
Danny Meringa (Siemens)
Marcel van Velzen & Junior Marte Garcia (HTM)
Carlos de Matos Fernandes, Rudolf Simons & Rients Dotinga (DataFryslân)
16:30-17:00
This lecture will be given by Rainforest Alliance
Hall 5
Page 49
10:15-10:45
Chatten met het KVK
Handelsregister – Gen AI binnen KVK
Margot Bleeker & Waseem Sadiq (KVK)
11:00-11:30
Verantwoord waarde halen uit tientallen Petabytes aan politiedata via Datagedreven Samen Werken
Oscar Wijsman, Ruud Staijen & William Swaters (Politie)
11:45-12:15
Van Proof of Concept naar Productie: Het Meten en Sturen van GenAI in de Echte Wereld
Dennis Stolmeijer (Zonneplan)
12:30-13:00
De grootste managementuitdaging van de 21ste eeuw. AI gaat niet alleen over technologie.
Boudewijn Raessens (Fontys Economie & Communicatie)
Hall 6 Page 50
10:45-11:15
Streamlining End-to-End Processes with CRM-ERP Integration Powered by AI and Business Process Automation
Mircea Prelipceanu (CAS Software AG) Frederic Parmentier (Kerridge Commercial Systems)
11:30-12:00
Van Mossel: versnellen naar écht datagedreven werken
Koenraad Bruins (Van Mossel Automotive Group)
Hall 7 Page 51
10:15-10:45
Hoe Schiphol AI in zet om de passagiersbeleving te verhogen
Robbie Van Dam (Schiphol)
11:00-11:30
Responsible gambling in the gaming market: our data-driven approach
Thomas Rodenburg (Nederlandse Loterij)
11:45-12:15
De Generatieve AI Journey van Vesteda - van experiment naar productie
Ruben Sikkes (Vesteda) Tjerk van der Maten (Emixa)
Hall 8 Page 52
10:30-11:00
Van frustratie naar frictieloos: hoe KPN met data de klantreis transformeert
Carmen Boymans (KPN) Chantal Snijder (Digital Power)
11:15-11:45 “This does not make any sense…” – Ecommerce aanbevelingen in B2B Thijs Brugman (Kramp)
12:00-12:30
This lecture will be given by HEMA
Roundtable sessions Page 53
Roundtable1 10:15-11:00
Data Products: What They Really Are (And How to Build Them Right)
Ryan Dolley (GoodData)
Roundtable1
11:15-12:00 How to rapidly transform complex and diverse data into business analytics value.
Scott Leslie (Evidant Corporation)
Marco Hill (Conify Technologies)
13:45-14:15
Van Nu naar Morgen: Op naar een weerbare en toekomstbestendige brandweerzorg en crisisbeheersing
Amy Matser (NIPV)
14:30-15:00
GPT-NL: bouwen aan een soeverein en eerlijk taalmodel voor Nederland
Saskia Lensink (TNO)
15:15-15:45
AI in marketing: het DataDriven Marketing Onderzoek 2025
Brenda van den Berg (DDMA)
16:00-16:30
Operating Model Under Pressure: How Zilveren Kruis Is Redesigning Its Data & AI Model
Stefan Bakker (Zilveren Kruis) Steven Nooijen (Xebia)
12:45-13:15
Hoe Rotterdam The Hague Airport koos voor meer efficiëntie met een geautomatiseerd datawarehouse
Anne Marthe den Hartog & Wim Fieret (Rotterdam The Hague Airport)
13:30-14:00 Hyperpersonalisatie in Actie bij Odido
Jeffrey Ploeg (Odido) Guus Rutten (GX Software)
14:15-14:45 A journey towards a better data strategy
Marton Horvath (Nextwit) Matyas Miskolczi (OTP Bank)
12:30-13:00 Van documenten naar dialoog: zo maak je je data veilig bruikbaar met Generatieve AI Jeanine Schoonemann (Cmotions) Katinka SliedregtJobse (RVO)
12:45-13:15
Van Complexiteit naar Zelfredzaamheid: De weg naar een Self-Service Data Platform bij DNB
Oleksandr Murov (DNB)
13:30-14:00
13:45-14:15
Praktische toepassingen van generatieve AI in mediabedrijf DPG Media
Lars Anderson (DPG Media)
14:30-15:00 This lecture will be given by DSSC
15:00-16:30 AI & Strategie: 5 AI-tools voor meer datagedreven beslissingen
Ruben Nieuwenhuis (CupolaXS)
15:15-15:45 Behind the Scenes: How RTL Uses AI to Power Video Workflows
Prajakta Shouche (RTL Netherlands)
16:00-16:30 Fieldwork Future: Empowering Technicians with LLMs
Van Machine Learning naar Organization Learning: Hoe KWF Organisatie-brede AIAdoptie Succesvol Maakte Miloud Ourahou (KWF Kankerbestrijding)
14:15-14:45
Van idee tot implementatie: onze lessen uit het bouwen van twee AI-chatbots
Ahmed Nait Aicha (Gemeente Amsterdam)
15:00-15:30 Don’t Build on Sand: The Case for Solid Data Foundations (yes, with Excel)
Elise Teixeira & Ulf Stolzke (Vattenfall)
15:45-16:15
Dura Vermeer Bouwt aan Inzicht: Zo Wordt de Dagstart Datagedreven
Jan Landwaart, Tim Gerritsen & Sahan Yildiz (Dura Vermeer (Renovatie Midden West))
Thijs Nieuwdorp, Ronald Warmels & Reugene Balentina (VodafoneZiggo) 16:30 - 17:00 AI in de praktijk bij Centraal Beheer: V experiment naar impact
Livia Lablans & Maarten Mulder (Centraal Beheer Achmea)
Roundtable1 13:15-14:00
Van data naar impact: breng met het juiste data-narratief je stakeholders in beweging!
Jay Ramsanjhal (Schiphol)
Roundtable1 14:15-15:00
Van Hype naar Handen: Aan de Slag met AI
Vincent Everts Trendwatcher, Techvisionair & DataEvangelist
Roundtable 2
11:30-12:15
Champions League- ambities met data: de business als12de man
Emre Aydogan (PSV Eindhoven)
Roundtable 2 13:15-14:00
“Is Your Sponsorship Really Working?” –Cutting Through the Noise with AI
Henk-Frits Verkerk (Two Circles)
Roundtable 2 14:15-15:00 GenAI Monitoring & Evaluatie - Diep Duiken in de Praktijk
Dennis Stolmeijer (Zonneplan)
Roundtable1 15:15-16:00 This session will be given by Isala
Personalised ads, I am sick of them. Whereas ten years ago I was a fan of the idea. I thought it would be nice to stop getting adverts for nappies or panty liners as a middle-aged man. Nor am I interested in expensive cars or watches. And all-inclusieve trips to the sun don’t seem like anyting to me either.
But to get rid of that, I have to give unknown companies insight into everything that does interest me. By allowing cookies to be set, I consent to having my movements on the Internet - business and private - tracked. I have no idea what kind of data this collects, and I have no idea who or what is then done with it. At any rate, sifting through page-long, unnecessarily (and deliberately) complicated terms of use on every site is not among my interests. The only thing I noticed was that I was getting advertisements for stuff or services I had already ordered in the meantime, sometimes for weeks or months afterwards. Since then, I block every cookie I can block.
But suppose I could better understand the mechanisms by which an ad appears on my screen? What data goes to whom and why? And how does the algorithm that determines which ad I see work? The call for Explainable AI (XAI) is ringing in more and more industries. Medical specialists were the first to use AI for image analysis and then for a second opinion when making a diagnosis. Soon they wanted to know how the algorithms arrive at their opinions. In ethical issues, understanding the steps an algorithm makes is essential. This is also why XAI is indispensable for the use of artificial intelligence in police work, legal investigations and fraud detection. Without explanation, evidence does not hold up in court. But XAI is also important in aviation. Think of engineers and pilots who want to know how AI systems arrive at certain recommendations, for example, when testing systems in simulated environments before they are deployed operationally.
Full steam ahead, then, for XAI, you might think. Yet there are a few snags. Making AI explainable requires additional steps in an already energy-consuming process. Also, explainability increases the complexity of AI models, making implementation and maintenance more difficult. Moreover, the performance of models decreases as they become larger and more complex. And then the popular Large Language Models (LLM), on which basically all chat apps are based, are so-called black box models where creating transparency is inherently difficult. There are developments such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), but I am yet to encounter their results as consumers. Perhaps the European AI Act, with its stricter transparency requirements, is encouraging Web companies to integrate XAI methods into their LLMs. At any rate, until I know how the ads get on my screen, I’ll keep my cookie jar shut.
Author: Thijs Doorenbosch
Host of the day: Tom van ‘t Hek
10:45 - 11:30
Frans
Feldberg Professor
12:00 - 12:45
Vinay Kumar Principal WW Product Manager for Data and AI
Tommaso Paracciani Head of Data & Cloud Platform
Voor het succesvol en verantwoord toepassen van AI is het essentieel om te weten wat ieder type wél en wat het (nog) niét kan. Of zoals de grote wetenschapper en filosoof Goethe het in de 18e eeuw verwoordde: ‘In de beperking toont zich de meester’. In zijn keynote zal Frans langs het ‘waarom, wat en hoe’ de verschillende typen AI behandelen, gaat hij dieper in op de belangrijkste beperkingen van ieder type en hoe je hiermee om kunt gaan. Dit doet hij op basis van theoretische inzichten die aan de hand van inspirerende voorbeelden en aansprekende cases laagdrempelig worden uitgelegd. Zoals we van Frans gewend zijn biedt hij ook nu weer concrete handvatten voor het succesvol en verantwoord gebruik van AI in jouw organisatie.
AI is redefining the future. Technology is changing faster than ever; people have new ways of interacting with technology, and organizations are adapting and adopting this change. However, Trusted AI can only be built on trusted data. We will dive deep into how AWS is helping customers build a trusted data foundation as they embark on their AI journey to build outcomes that are tailored to their business needs. HEMA will present their journey towards a strong Data Platform and Data Governance strategy on AWS, and the business outcomes they achieved.
13:15 - 14:00
James Topping
Data Analytics Manager
14:30 - 15:15
Roy Lindelauf
Professor of Data Science in Military Operations
Uber Eats processes millions of daily orders across Europe, generating vast amounts of data. But only quality data drives real impact. In this session, we’ll explore how strong data foundations enable personalized experiences, boost retention, and fuel growth. Learn how to cut through the noise, avoid common pitfalls, and use highquality data to make smarter, faster, and more customer-centric decisions.
AI is playing an increasingly significant role in warfare – from combating disinformation and analyzing imagery to (semi-)autonomous systems. In this keynote, we explore how this technology is transforming the battlefield and how we can use AI responsibly.
15:45 - 16:30
Tom Pots
Programmamanager AI en Data
Stop met praten over AI en data.
Iedereen praat over AI en data, maar hoe kom je van ambitie tot resultaat? Inhoudelijk aansluitend op de keynote van Frans Feldberg laat Tom Pots zien hoe je datagestuurd werken en AI écht in beweging krijgt. Een inspirerend verhaal met successen, fouten en doorbraken vanuit de Gemeente Zaanstad – en vooral: wat jij morgen anders kunt doen.
FRANS FELDBERG Professor of Data-Driven Business Innovation.
As a professor, entrepreneur and co-founder of several data initiatives, Frans Feldberg knows better than anyone how data and AI can transform organizations. In his role at Vrije Universiteit Amsterdam and through initiatives like the Amsterdam Center for Business Analytics and Data Science Alkmaar, he builds a strong bridge between academic research and real-world practice.
Leading up to his keynote, we spoke with Frans about the opportunities as well as the challenges presented by today’s abundance of data and the rise of AI.
At first glance, it seems like a dream scenario: unprecedented amounts of data, with which you can make better decisions, get to know your customer better and develop and deliver new services. Practice paints a different picture. Research shows that 70 to 80 percent of data projects fail to achieve their intended goals. “Those are obviously worrisome numbers,” says Feldberg. “We have the technology, we have the data, but we apparently fail to fill in the necessary prerequisites for success.”
So how do we translate the concept of data-driven innovation into practical and successful implementation? Both in business, where business models are under pressure from digitization, and in the public sector, where data can help with social issues such as healthcare, security and sustainability. According to Feldberg, the key to success lies in developing a common story around data- and AI-driven innovation.
A story to which all relevant stakeholders, business, IT and data professionals alike, contribute. “When it comes to AI, people are quick to shout: we have to do something with AI! But why start with the ‘how’? Isn’t the ‘why’ more important? And, do you have a clear understanding of what AI can and cannot do? These are questions that need to be answered in such a story.
Data and AI are much more than technological developments. According to Feldberg, they touch every element of the business model, or task in the public sector. From how an organization creates value, to how it delivers that value to its target audience and how it manages to retain it internally.
“At its core, a business model is about three questions,” he explains. “How do you create value? How do you deliver it to the right target audience? And how do you ensure that value remains at the bottom line? Data and AI potentially affect all those aspects.”
Companies like Facebook, but also more and more innovative startups, have an
unprecedentedly detailed customer view, often sharper than many organizations themselves have of their own customers. Feldberg: “That confronts you as an organization with an important question: how do you stay relevant when someone else knows your customer better than you know yourself?” And it goes beyond customer relationships. Data and AI are also changing the relationships between people, businesses and governments. They influence internal processes, external collaborations and the expectations that customers, citizens and employees have of organizations and each other.
“ That confronts you as an organization with an important question: how do you stay relevant when someone else knows your customer better than you know yourself? ”
“That raises strategic questions,” says Feldberg. “What does this mean for your
relationships with your customers? With your employees? Your suppliers? Your business partners? And for collaboration between departments or teams within your organization?”
The tricky thing, and at the same time interesting, is that there is no blueprint for what the impact of data and AI will be. No universal answer or standard approach. Each organization operates in its own context, with its own data, dynamics and goals. And so it requires customization. To learn and dare to experiment. But, it also offers opportunities to differentiate yourself as an organization, both public and private.
A shared story is essential
Anyone who thinks the success of data and AI projects depends primarily on advanced technology will be disappointed. “People often think technology is important,” says Feldberg, “but it is rarely the deciding factor in whether or not a data project succeeds.”
Extensive research on critical success factors shows that it is an interplay between organization, people, governance, data management and technology.
AT THE
UNIVERSITEIT AMSTERDAM, WILL OPEN DATA EXPO ON THURSDAY, SEPTEMBER 11, WITH THE KEYNOTE “AI: IN THE LIMITATION THE MASTER SHOWS HIMSELF!”
DON’T MISS IT!
Keynote
“Developing a shared story is the solution. It can be that simple.”
Thursday
“Data projects are by definition multidisciplinary,” he explains. “You need business professionals, IT professionals and data professionals - and they often speak very different languages. If you don’t make sure these people understand each other, share knowledge, communicate well with each other and develop a shared story together, it’s almost impossible to make such a project a success.”
Research has shown time and again that this process, called “alignment”, is one of the most important prerequisites for success. “You can have the best infrastructure and the smartest algorithms,” says Feldberg, “But if the different teams don’t understand each other, then success is very far away. Developing a shared story is the solution. It can be that simple.”
Is AI fundamentally different from previous data projects? “Yes and no,” says Feldberg. “At its core, artificial intelligence is a datadriven technology. So many of the success and failure factors that apply to data projects also apply to AI projects. Data quality, for example, not sexy, but essential.”
The rise of generative AI (such as ChatGPT) has captured the imagination of the general public and business world. The speed at which new applications are emerging is unprecedented. This presents a new challenge for organizations: do you have
10:45 - 11:30
the absorptive capacity to move with them, to understand what is relevant and what is not? In this, understanding what AI can and cannot do is essential. How else can you judge what to do and what certainly not to do? And how to do this responsibly as well.
According to Feldberg, there is a great risk that organizations will get lost in the noise and make decisions based on incorrect expectations. “The messaging around AI is often ‘glaring’ and unsubtle: AI will take over your job, computers will become smarter than humans, within now and two years everything will be different. AI is presented as the Haarlemmer’s oil for all problems. But it certainly isn’t!”
To assess all these claims, Feldberg says it is very important to know what types of AI there are and what their limitations are. “If you know this, you can much better assess where the power of AI lies and deploy it successfully and responsibly.” He continues: “For example, AI cannot think. Thinking includes being able to estimate the purpose of an object or human being without being clear beforehand. This is called ‘intentionality.’ For now, this is one of the biggest challenges for AI. And so there are more. However, those who blindly follow the hype often create unrealistic expectations. Then the wrong discussions arise and disappointment follows. Not because AI does not work, but because it is not clear beforehand what AI can and cannot do for your organization.” In his keynote, Feldberg elaborates on this. Because, as the great scientist and philosopher Goethe put it in the 18th century, “In limitation the master shows himself.
Quickly uncover hidden relationships and patterns across billions of data connections.
WHY DATA GOVERNANCE SHOULD BE YOUR NEXT PRIORITY
IS YOUR ORGANIZATION ALREADY DIGITALLY MATURE?
DATA & AI TRENDS: WHAT SHOULD I ANTICIPATE TODAY?
HOW KNOWLEDGE GRAPHS COMPLEMENT CLOUD DATA PLATFORMS
CDMP CERTIFICATION: FROM DATA POLICY TO DATA PROFICIENCY
DATA GOVERNANCE AS A FOUNDATION FOR EXTRACTING MAXIMUM VALUE FROM DATA
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Chief Science Information Officer & Associate Professor
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- 16:30
Transforming HR data into a strategic business asset at ASML
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Owner Marketing Automation
Vincent Hoogsteder Medeoprichter & Partner
Head of Product
12:30 - 13:15 13:45 - 14:30
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Vennema
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Dairy with Data: Insights from Lely’s Robotic Innovations
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This lecture will be given by Healthplus.ai
and KickstartAI
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Zitman Director of AI Projects
Director Data & Analytics
10:15 - 10:45 11:00 - 11:30 11:45 - 12:15 12:30 - 13:00 13:45 - 14:15 14:30 - 15:00 15:15 - 15:45
This lecture will be given by Isala
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Data, wie is er niet groot mee geworden? Ook vandaag is data de brandstof van digitalisering. Maar hoe zorgen we dat we data kunnen vertrouwen, vinden én gebruiken? Bij ProRail zien we data als een product: met een bestemming, eigenaar, vorm en kwaliteit. In deze sessie nemen we dataprofessionals en digitale veranderaars mee in onze aanpak en horen we graag jullie ervaringen. Zo leren we samen verder in de digitale transformatie.
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- 16:30
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Groth Professor of Data Science
10:45 - 11:15
Automated insights: uncovering insights from customer data with the use of OpenAI.
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14:15 - 14:45 15:00 - 15:30 15:45 - 16:15 16:30 - 17:00
Van Data naar Daadkracht:
360° Klantinzicht met AI
Ontdek hoe K-parts uitgroeiden tot internationale speler door data slim te koppelen aan klantbeleving én magazijnautomatisering. Met 80% geautomatiseerd pickproces, AI-gedreven personalisatie en een geïntegreerd klantbeeld, laten we zien hoe je met lef, focus en een klein team groot kunt worden.
Meten = weten maar meten + analyseren = besparen
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This lecture will be given by Rainforest Alliance
Triple A solu�on:
Access/Audit/Analyse Connect to any source, apply intelligent indexing, inventorise your data, and more
Answer/Access Bring GenAI to life: Find and interrogate your data, including PII, and more Ac�on Informa�on Workflow: Protect, Comply, Redact, Dispose, De-Duplicate, Archive, and more
www.EDRM-CONSULTANCY.nl
info@edrm-consultancy.nl +31625598006
10:15 - 10:45 11:00 - 11:30 11:45 - 12:15 12:30 - 13:00
Chatten met het KVK
Handelsregister – Gen AI binnen KVK
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Verantwoord waarde halen uit tientallen Petabytes aan politiedata via Datagedreven
Samen Werken
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Van Proof of Concept naar Productie: Het Meten en Sturen van GenAI in de Echte Wereld
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De grootste managementuitdaging van de 21ste eeuw. AI gaat niet alleen over technologie.
Terwijl de AI-ontwikkelingen razendsnel gaan, is het aanpassingsvermogen van organisaties traag. Veel AI-projecten mislukken. AI in organisaties is geen IT-project, het is een verandertraject. Enerzijds zijn er overdreven verwachtingen van AI. Begin met vraag 0. Welk probleem los ik op met AI en kan het ook zonder AI? Anderzijds is er te vaak te weinig aandacht voor de veranderbereidheid bij de medewerkers. Deze lezing geeft handvatten om de ‘gap’ tussen de snelle AI-ontwikkelingen en het langzame aanpassingsvermogen in organisaties te overbruggen.
Ruud Staijen Landelijk Programmadirecteur Datagedreven Samen Werken
William Swaters Omgevingsmanager Programma Datagedreven Samen Werken
13:45 - 14:15 14:30 - 15:00 15:15 - 15:45 16:00 - 16:30
Van Nu naar Morgen:
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GPT-NL: bouwen aan een soeverein en eerlijk taalmodel voor Nederland
GPT-NL biedt een transparant en verantwoord alternatief voor bestaande taalmodellen. Het model wordt ontwikkeld met kwalitatieve, rechtmatig verkregen Nederlandse data, waarbij auteursrechthebbenden eerlijk worden beloond. In deze sessie deelt Saskia Lensink de voortgang van het project, inclusief de eerste prestatie-evaluaties en de roadmap voor verdere ontwikkeling, ondersteund door use cases met publieke en private partners.
AI in marketing: het Data-Driven Marketing Onderzoek 2025
Hoe ver zijn we in Nederland met AI in marketing? Laat je meenemen in de nieuwste inzichten uit het DDMO 2025: hét jaarlijkse onderzoek van de DDMA naar datagedreven marketing. Met een scherpe analyse van waar de sector staat en waar kansen liggen om te versnellen. In deze sessie ontdek je waarom de kloof tussen voorlopers en achterblijvers groeit. En hoe je voorkomt dat jouw organisatie de aansluiting mist.
Operating Model Under Pressure: How Zilveren Kruis Is Redesigning Its Data & AI Model
Generative AI and AI tools are democratizing data and blurring lines between business, data, and IT, making traditional operating models obsolete. Zilveren Kruis, the largest health insurer in the Netherlands, is modernizing by enabling self-service, implementing AI productivity suites, fostering collaboration across departments, and redefining data roles to drive rapid, compliant innovation.
10:45 - 11:15 11:30 - 12:00 12:45 - 13:15 13:30 - 14:00
Streamlining End-to-End Processes with CRM-ERP Integration Powered by AI and Business Process Automation
Ready to unlock the full potential of your operations? By integrating CRM with ERP and powering it with AI and business process automation, you create a seamless, intelligent system that connects every part of your business— from customer touchpoints to backend processes.
Van Mossel: versnellen naar écht datagedreven werken
Van Mossel Automotive Group groeide tussen 2018 en 2024 razendsnel van €1,9 naar €6,2 mld omzet. CIO Koenraad Bruins vertelt hoe data cruciaal werd bij het oplossen van complexe IT-uitdagingen en inefficiënte processen en hoe Van Mossel nu versnelt richting een écht datagedreven organisatie.
Powered by C-data
14:15 - 14:45 15:00 - 16:30
A journey towards a better data strategy
Discover how data management maturity assessments can spark group-wide excellence in data-driven decision making. We will explore the interview-based approach of OTP Group supported by DAMA Hungary, share how its results can be turned into practical value, and give a glimpse into OTP Group’s journey. You’ll also get a teaser of a potential AI-powered agent designed to make assessments smarter and faster, and a look at our vision for future innovation.
AI & Strategie: 5 AI-tools voor meer datagedreven beslissingen
Workshop voor ondernemers en leidinggevenden die AI willen inzetten voor strategie en besluitvorming. Leer hoe je tools als Claude, ChatGPT, Grok, Perplexity en NotebookLM gebruikt voor betere keuzes, marktanalyse en positionering. Ontdek hoe je AI structureel toepast binnen je organisatie en versterk je leiderschap met slimme, datagedreven inzichten. Praktisch, actueel en direct toepasbaar.
Hoe Rotterdam The Hague Airport koos voor meer efficiëntie met een geautomatiseerd datawarehouse
Hoe zorg je ervoor dat je data sneller, betrouwbaarder en met minder handmatige werkzaamheden beschikbaar is voor analyse? Anne Marthe den Hartog (Aviation & Business Intelligence specialist) en Wim Fieret (Data & Analytics specialist) van Rotterdam The Hague Airport vertellen hoe zij de overstap maakten van hun oude manier van datavoorbereiding naar een low-code datafundament met TimeXtender. Tijdens deze sessie delen ze hun ervaringen en praktische voordelen die deze nieuwe werkwijze oplevert.
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Personalisatie is geen luxe, maar een verwachting van de klant. In deze lezing ontdek je hoe Odido met een krachtig personalisatiemodel, real-time data en AI hyperpersonalisatie in de praktijk brengt en zorgt voor écht relevante klantinteracties die resultaat opleveren.
Hoe Schiphol AI in zet om de passagiersbeleving te verhogen
Schiphol verwerkt jaarlijks ruim 66 miljoen passagiers en 450.000 vliegbewegingen. Data en AI spelen hierbij een steeds grotere rol. In deze sessie deelt Robbie (Data Lead) hoe Schiphol data inzet om processen te verbeteren, waaronder een AI-model dat voorspelt wanneer bagage op de band verschijnt. Ook komt de organisatie van het datateam aan bod, de impact op reizigers én geleerde lessen rondom datakwaliteit en samenwerking.
- 11:30
Responsible gambling in the gaming market: our data-driven approach
As the largest provider of the gaming market in the Netherlands, Nederlandse Loterij has a leading role in promoting responsible gambling. During this presentation, you will discover how Nederlandse Loterij uses data and innovation to encourage responsible play. We continuously work on strengthening our data capabilities to better identify risks and respond effectively to problematic gambling behaviour.
Powered by Eraneos
- 14:15
Praktische toepassingen van generatieve AI in mediabedrijf DPG Media
This lecture will be given by DSSC
De Generatieve AI Journey van Vesteda - van experiment naar productie
Ontdek hoe Vesteda generatieve AI succesvol inzet: van de eerste experimenten tot een volwassen productieomgeving. In deze sessie delen we lessen, valkuilen en praktische inzichten uit onze AI-reis. Een verhaal over visie, samenwerking en impact.
Behind the Scenes: How RTL Uses AI to Power Video Workflows
- 13:00
Van documenten naar dialoog: zo maak je je data veilig bruikbaar met Generatieve AI
Van documenten naar dialoog: Ontdek hoe RVO en Cmotions generatieve AI veilig toepassen op interne data. Bouw zelf een zoek- en chatomgeving waarmee je collega’s veilig en flexibel waarde laten halen uit rapporten, memo’s en meer. Alles in eigen beheer, volledig controleerbaar én aanpasbaar aan jouw IT-landschap.
- 16:30
At VodafoneZiggo, we’re building digital LLM tools that provide instant information, automate repetitive tasks, and will ultimately serve as a digital buddy. This talk explores how our projects enhance efficiency and transform fieldwork, paving the way for a more effective and informed technical workforce. 10:15 - 10:45
Sinds de stormachtige opkomst van generatieve AI bij het verschijnen van de eerste versie van ChatGPT in 2022, zien we ook bij mediabedrijven een grote drang naar het inpassen van AI in hun organisatie. Welke stappen heeft DPG Media genomen? Hoe verandert dat de werkwijze en de journalistiek? Welke tools hebben ze gebouwd op basis van AI die journalisten helpen in hun werk of lezers helpen met vindbaarheid van artikelen of het genereren van samenvattingen?
Fieldwork Future: Empowering Technicians with LLMs
RTL processes a high volume of video content across platforms—from streaming dramas to breaking news. In this session, we’ll share how we use AI to automate repetitive tasks like subtitling, thumbnail selection, and audio separation, helping teams work more efficiently. We’ll also take a look at the modular, open-source infrastructure behind these workflows— and how it integrates AI into production with flexibility.
THURSDAY 11
10:30 - 11:00 11:15 - 11:45
Van frustratie naar frictieloos: hoe KPN met data de klantreis transformeert
Wat doe je als klanten afhaken in je digitale kanalen en de klantenservice overbelast raakt? In deze sessie nemen we je mee in hoe KPN met data, experimenten en customer journey teams de regie terugpakte op de klantreis en zo de online beleving verbeterde.
12:45 - 13:15
Van Complexiteit naar
Zelfredzaamheid: De weg naar een Self-Service Data Platform bij DNB
Hoe hou je grip op een complexe cloudomgeving en stimuleer je tegelijk autonomie en innovatie? Ontdek hoe bij DNB werkt binnen een gefedereerd cloudlandschap met een schaalbaar en veilig self-service data platform. Leer hoe je een self-service data platform opzet, de juiste balans vindt tussen autonomie en centrale governance, en kosten beheersbaar houdt. Dit inspirerende voorbeeld laat zien hoe technologie, governance en samenwerking samenkomen in een toekomstbestendige data-oplossing.
“This does not make any sense…” –Ecommerce aanbevelingen in B2B
We gaan door Kramp.com heen om pragmatisch te kijken naar onze reis met betrekking tot Ecommerce Recommenders. Het onderwerp varieert van simplistische popularity modellen tot zoekmachine aanbevelingen door middel van text-embeddings; met als leidraad waarom uit alle modellen zoveel onzin komt –maar ook waarom ze conversie laten toenemen.
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Thijs Brugman Manager Data Science
13:30 - 14:00
Van Machine Learning naar Organization Learning: Hoe KWF Organisatie-brede AI-Adoptie
Succesvol Maakte
AI Officer Miloud Ourahou onthult hoe KWF’s meest impactvolle AI-innovaties ontstonden buiten de ITafdeling. Ontdek concrete strategieën om verborgen innovators te activeren en een realistische AI-cultuur op te bouwen die échte waarde creëert.
12:00 - 12:30
This lecture will be given by HEMA
14:15 - 14:45
Van idee tot implementatie: onze lessen uit het bouwen van twee AI-chatbots
Afgelopen jaar ontwikkelden we twee AI-chatbots: één voor gestructureerde en één voor ongestructureerde data. We delen de technische én organisatorische uitdagingen, het veranderende ontwikkelproces, en de lessons learned. Uiteraard sluiten we af met een live demo.
15:00 - 15:30 15:45 - 16:15 16:30 - 17:00
Don’t Build on Sand: The Case for Solid Data Foundations (yes, with Excel)
This session is for teams still working in Excel, showing how to move from spreadsheet chaos to clean, structured data. By understanding your data - and using tools like Power Query, data modeling, and VBA - you can bridge silos, make better decisions, and lay the groundwork for real digital transformation.
Dura Vermeer Bouwt aan Inzicht: Zo Wordt de Dagstart Datagedreven
Van Excel naar inzicht: met de digitale dagstart zet Dura Vermeer een stap richting datagedreven bouwen. Dagelijkse observaties worden slim vastgelegd, gekoppeld aan leveranciersbeoordelingen en omgezet in stuurinformatie. Minder administratie, meer grip op kwaliteit en samenwerking.
AI in de praktijk bij Centraal Beheer: Van experiment naar impact
Hoe Centraal Beheer AI inzet om organisatiebreed waardevolle toepassingen te realiseren — met concrete voorbeelden van use cases, technische bouwblokken en adoptie-aanpak.
Data Products: What They Really Are (And How to Build Them Right)
Everyone talks about data products, but what are they really? Join GoodData’s Ryan Dolley for an interactive talk on defining, building, and scaling data products for real business value. Learn key principles, common pitfalls, and share real-world wins. Whether you’re in engineering, product, or analytics, you’ll leave with a clear framework to succeed with data products.
How to rapidly transform complex and diverse data into business analytics value.
80% of analytics effort goes into preparing data due to poor goal setting, data complexity, and lack of best practices. This roundtable covers engaging business teams, aggregating disparate sources, moving to online collaboration, and empowering analysts to build transformations without programmers. Essential for anyone in operational analytics.
Ryan Dolley VP of Product Strategy
Scott
Leslie Chief Architect Marco Hill Managing Director
Van data naar impact: breng met het juiste data-narratief je stakeholders in beweging!
Zet jouw data-inzichten om in verhalen die aanzetten tot actie. In deze interactieve sessie leer je hoe het INSPIRE-model helpt om stakeholders te raken, draagvlak te creëren en de echte businesswaarde van data zichtbaar te maken. Ontdek praktijkvoorbeelden en krijg direct toepasbare tools om jouw dataverhaal krachtiger te maken.
Van Hype naar
Handen: Aan de Slag met AI
In deze interactieve sessie ontdek je praktische AI-tools die je vandaag nog kunt gebruiken. Geen theorie, maar direct aan de slag met slimme toepassingen die tijd besparen en je werk verbeteren.
Jay Ramsanjhal Strategic Advisor
Vincent Everts Trendwatcher, Techvisionair & Data-Evangelist
10:15 - 11:00 11:15 - 12:00 13:15 - 14:00 14:15 - 15:00 15:15 - 16:00 11:30 - 12:15 13:15 - 14:00 14:15 - 15:00
Champions League-ambities met data: de business als 12de man
Data speelt een steeds grotere rol binnen het voetbal, niet alleen op het veld, maar ook in de business van de club. Hoe zorg je ervoor dat afdelingen zoals marketing, ticketing en commercie mee kunnen in deze datagedreven aanpak? In deze sessie gaan we in gesprek over hoe je de business binnen een voetbalorganisatie meeneemt in data en AI.
“Is
Your Sponsorship Really Working?” – Cutting Through the Noise with AI
Too many sponsorships still rely on gut feeling and vanity metrics. In this session, we challenge the status quo and show how data—powered by AI—can uncover the real impact of sponsorships, especially on social media. Learn how to move beyond impressions and logos, and start measuring what actually matters to your brand, your partners, and your fans.
Emre Aydogan Data Engineer
This session will be given by Isala
GenAI Monitoring & EvaluatieDiep Duiken in de Praktijk
In deze kleine groepsessie gaan we echt de techniek in. Breng je concrete vragen over GenAI monitoring mee - hoe bepaal je of het werkt, welke metrics tellen, hoe ga je live? Geen presentatie maar échte discussie waar we van elkaar leren. Als Head of Development deel ik onze aanpak, maar ben minstens zo benieuwd naar jullie ervaringen. Kom maar door!
Verkerk Client Services Director
Stolmeijer Head of Development
The world is changing fast and AI is making us rethink how we structure our work. New software companies are popping up everywhere, and it seems like everyone is using, and selling, the latest AI tools. But let’s take a step back and look at how we collect, use and predict with data. You might be thinking, “I want to use AI to make predictions but my organization isn’t data-driven yet. So where do I start?”
After reading this article, you’ll be able to answer one key question: do you need BI to get started with AI?
I see BI (business intelligence) as applying and deploying your business logic. You want insight into your business. Using data, you can recognize what’s working and what’s not. You want to be able to copy and paste that in an easy way for your business. It is estimated that 90% of all data in the world was generated only in the last two years. Bizarre that those volumes are increasing so much! Of course, you want to take advantage of that in a smart way.
So one is BI, repeating the business logic and making it insightful. So BI can also be automating certain processes. That can be with a digital robot that categorizes your emails or keeps your Sharepoint tidy. It can also be mathematical analysis to get those insights. Perhaps you already use dashboards, so you always have insight into how your business is doing. These dashboards provide insight into your capacity or quality, for example. This allows you to define very targeted areas for improvement and actions. Data-driven work at it’s finest. Okay, great analyses and dashboards. Do I need them to be able to predict?
In my opinion, you don’t necessarily need BI to start forecasting. But, you do need to know your numbers. Prediction, or the use of AI, can also predict a certain action. A certain outcome. Think about whether a particular machine will break down in 6 months. Or what the amount of your sales will be. Or which drug is best for that patient. But ask yourself: is all this really AI?
Did you know there is a distinction between AI and data science? AI is data science, but not every data science solution is AI. AI means intelligence. An AI model learns from its own process. It improves as the model is used more often. More data and more frequent use means the model becomes more intelligent. Think of a self-driving car. The more data there is (namely, driver behavior), the better the car learns to “predict” that behavior. Because that’s basically what that car’s AI does: predict human driving behavior to turn that outcome into automated actions.
There are different techniques within AI and data science that you can employ, depending on the type of issue and the type of data.
If you want to predict a value, you need to know what kind of data you have:
• Nominal: categories without order (e.g., product types or gender)
• Ordinal: categories with order (e.g., customer satisfaction: low, medium, high)
• Discrete: countable values (e.g., number of purchases)
• Continuous: measurable values with many possible outcomes (e.g. temperature or turnover)
Depending on the type of data, choose an appropriate analysis technique. An AI model that does text classification works differently than a model that predicts sales. And different from a model that recognizes an image or converts speech into text. That’s why it’s so important to have a good understanding of what your problem is before you start working with AI.
Because let’s face it: many organizations want to “do something with AI,” but don’t know exactly what they want to solve. And then disappointment lurks. Or, even worse: you spend budget on a smart AI
solution when you might have just solved the problem with BI. Yet in practice, I often see that organizations already have a lot of valuable data, but that it is scattered across departments or systems. That is precisely where the profit lies: before you start with AI, you can achieve a lot by centralizing and cleaning up your data. Consider a first step like making a data inventory: what data do you actually already have? Where is it located? Who manages what? That may sound boring, but without good data foundations, you are building a house on loose sand. Moreover: by setting this up properly, you immediately work on a data culture within your organization. And that is perhaps even more valuable than any model.
The honest answer: no. You don’t necessarily need BI to apply AI. You can develop an AI solution without dashboards or data models. But you do need data as well as a clear picture of the problem you want to solve. In that sense, BI often provides a good foundation: it teaches you to look at your processes,
to structure data, to extract insights from what you already know. It makes you look at your numbers in an innovative way. And only then can you think further: “What could I predict?” or “What could I automate?”
Sometimes an issue you thought you’d have to solve with AI can be handled just fine with BI. For example: you want to know which department receives the most complaints. You can see that in a dashboard. No AI required. But do you want to automatically classify complaints and predict which ones lead to customer loss? Then you come more toward AI.
So it’s not about the tools, it’s about the question. What do you want to solve? What do you want to achieve? And what technology fits that best? So don’t let the AI hype fool you. Start with the basics: know your data, understand your processes, ask a sharp question. Whether you then build a dashboard or train an AI model - that is the right order.
Author: Rianne van der Stelt
Create a better future with data.
Strategy that drives clear direction.
AI innovation that delivers value.
Compliant, trusted & secure by design. Modernization built to scale with your business.
I would rather not have my favorite supermarket products on sale. The reason: the shelf is almost always empty. When I express my dissatisfaction about this to friends, I sometimes hear the comment that it is a marketing ploy. You come to the store for the sale, the shelf is empty, and then you buy another, more expensive product. I refuse to believe that. Empty shelves and missed special offers frustrate customers and create a bad experience. As a business, you don’t want dissatisfied customers. Yet it happens time and time again.
As a consumer with some understanding of business, purchasing and logistics chains, I assume that supply is considered far in advance when planning promotions. With artificial intelligence, an accurate estimate of expected demand can be made based on past customer behavior. After all, the phenomenon of offers was not suddenly invented last month. Manufacturers make sure there is inventory, and distributors and carriers schedule deliveries. Where does it go wrong?
Assuming enough good planning tools exist by now, it must go wrong with the data on which the schedules are based. The smartest algorithms are worthless if the quality of the data on which they are unleashed is poor.
The amount of data available to make decisions is growing rapidly, but its quality is still a problem. The offers at the supermarket are just one example. Finance departments, marketing teams, logistics planners and industrial operators also face poor data in a world where data-driven work is the norm.
Data Observability is an emerging discipline that can help improve data quality by continuously tracking data streams using metadata from processes. There are several tools available, such as Monte Carlo, Bigeye, Databand and Datadog, that integrate well with popular data platforms.
Data Observability assumes that tools monitor five aspects:
• Freshness: Check how recent the data is and avoid outdated insights that create inventory problems, for example.
• Distribution: Analyze data patterns (such as mean and standard deviation) to detect anomalies. For example, a sudden spike or drop in customer interaction with the website may be a warning sign of a problem with the data collection process or an underlying system error.
• Volume: Monitor the amount of data flowing through systems. Unexpected
increases or decreases in data flow can cause a problem in processing.
• Schema: Check changes in data structure to avoid problems later in the pipeline. Changing a data type, or renaming a column, can cause unexpected errors in linked systems.
• Lineage: Trace the origin and transformations of data to detect errors faster. When anomalous values are found, data lineage provides insight into where in the pipeline the cause of the anomaly occurred.
The crux of Data Observability lies in the continuous and automated nature of monitoring data quality rather than a periodic evaluation. However, it is important to define in advance what criteria the data must meet so that it is aligned with business goals. Tools that identify problems are only the beginning of a solution. Setting up a timely and adequate follow-up is also essential to gain maximum benefit from the tools.
Now just hope that soon there will be enough shelf-fillers available to place the offers delivered in the right quantities on the empty shelves. And that they will be ready when I want to stock my favorite product at a bargain price.
Author: Thijs Doorenbosch
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