Digital transformation, datagedreven werken en de ‘informatie-economie’ zijn populaire termen in de boardroom. Ongeacht wat ze betekenen, deze termen houden in de basis allemaal hetzelfde in: de organisatie wil meer met data doen. Data moet breder, efficiënter en effectiever ingezet worden om bedrijfs- en beslissingsprocessen te verbeteren en te versnellen en hun competitieve kracht te vergroten. Technisch gezien betekent dit dat nieuwe vormen van datagebruik ingezet worden, zoals data science, selfservice BI, embedded BI, edge analytics en klantgedreven BI.
Helaas zijn de bestaande systemen die data leveren, zoals het datawarehouse en de transactionele systemen, dikwijls niet meer toereikend voor deze nieuwe, intensere en zwaardere vormen van datagebruik. De rek is uit de data-architectuur van bestaande data delivery systemen. Sommige van deze systemen zijn ook al meer dan twintig jaar oud en hebben veel veranderingen en extensies ondergaan. Ze kunnen de immer toenemende groei aan datagebruik qua schaalbaarheid en snelheid niet meer aan. Daarbij zijn ze log en inflexibel geworden waardoor het implementeren van nieuwe rapporten en het uitvoeren van analyses zeer tijdrovend is geworden. Kortom, de data-architectuur kan de huidige ‘speed of business change’ niet meer aan.
De conclusie van veel organisaties is dan ook: het is tijd voor een nieuwe, toekomstbestendige data-architectuur. Echter, dit is gemakkelijker gezegd dan gedaan. Een data-architectuur ontwerpen doe je immers niet regelmatig. Welke nieuwe technologieën zijn er momenteel beschikbaar? Wat is de invloed van o.a. Hadoop, NoSQL, big data, datawarehouse automation, data-streaming op de architectuur? Welke nieuwe architectuurprincipes zijn er eigenlijk? Hoe gaan we om met de steeds strengere regels voor data-opslag en analyse? En wat is de invloed van cloud platformen?
Dit tweedaagse seminar geeft antwoord op veel vragen die gesteld worden tijdens het ontwerpen van een moderne data-architectuur. Richtlijnen, stappenplannen, ontwerpcriteria, tips, ontwerpregels, use cases en praktijkvoorbeelden worden uitgebreid behandeld. Concepten en technologieën als data lakes, big data, datavault, cloud, datavirtualisatie, Hadoop, NoSQL, datawarehouse automation en anonimisatie van data komen hierbij ruim aan bod. Het seminar is gebaseerd op praktijkervaringen die de laatste jaren zijn opgedaan bij het uitdenken en realiseren van nieuwe data-architecturen. Daarnaast wordt aandacht besteed aan de organisatorische consequenties van een moderne data-architectuur en aan onderwerpen als data kwaliteit, data governance, data-strategie en de migratie naar een nieuwe architectuur.
In dit seminar met Rick van der Lans krijgt u antwoord op de volgende vraagstukken:
Dit seminar is voor een ieder bestemd die vanuit zijn functie te maken heeft met de ontwikkeling van nieuwe of het aanpassen van bestaande data-architecturen, waaronder BI-architecten, business-analisten, datawarehouse- en databaseontwerpers, database-experts, consultants, technology planners, projectleiders, CTOs en systeemanalisten.
Bovenaan de pagina bij Downloads kunt u de volledige PDF brochure downloaden.
Van der Valk Hotel Utrecht
Winthontlaan 4-6
3526 KV Utrecht
Telefoon 030 8000 800
The hotel is very well accessible by public transport. From busstop ‘Kanaleneiland Zuid’ it is only a three-minute walk. You can take buses 63, 65, 66, 74 and 77 from Utrecht Central Station and you also take the tram line 20 or 21 from the train station and get off at stop ‘Kanaleneiland’. Please consult www.9292.nl (door-to-door journey planner, also available in English) or call 0900-9292 (travel advice by phone, € 0.70 p/m).
Van der Valk Hotel Utrecht is also located next to the highway A12, exit 17 (Utrecht / Jaarbeurs / Kanaleneiland).
Although the hotel has a large parking garage, we cannot guarantee parking spots. We therefore advise you to go by public transport.
For those who would like to arrive the day before, there is the possibility of staying at the Van der Valk Hotel Utrecht. However, the hotel does not provide special discounts for attendees of events. Therefore, when interested in an overnight stay, please consult Van der Valk directly to make a reservation.
More information about the hotel and the location can be found on their website www.vandervalkhotelutrecht.nl.
Detailed Workshop Outline
1. Introduction
Overview of Workshop Goals: Explain the importance of data as an asset and why organizations must move beyond treating data as just a service.
Solar System Metaphor: Introduce the concept of the data organization as a solar system, with data teams, governance, and accountability as key planetary bodies that need alignment for optimal performance.
Key Points:
2. Data Accountability: Creating a Culture of Ownership and Responsibility
Why Data Accountability Matters: Without clear accountability, data quality, security, and data availability suffer.
Practical Steps to Ensure Accountability:
Activity: Scenario-based discussion where participants identify where accountability is lacking in a fictional data-driven organization, and propose solutions for creating accountability.
Key Learning: Participants will gain insights into what data accountability entails, ensuring each team member knows their role in maintaining data quality and governance.
3. Data Governance Models: Federated Governance and Distributed Authority
Introduction to Data Governance: Why data governance is essential to manage risk, ensure compliance, and drive effective data use.
Federated Data Governance: What it is and how it works – balancing centralized oversight with distributed ownership across data hubs.
Key Components of a Data Governance Framework:
Activity: In groups, participants will design a federated governance model for a hypothetical organization, ensuring alignment between distributed teams and central governance.
Key Learning: Participants will learn how to implement a federated data governance model that balances control with autonomy, ensuring alignment across the organization.
4. Structuring Data Teams: Balancing Centralized and Distributed Needs
Discussion: Challenges in organizing data teams.
Activity: Group exercise where participants design an ideal data team structure that addresses both distributed and centralized organizational needs.
Key Learning: Participants will learn how to create a data team structure that is flexible enough to meet both innovation-driven and operational demands.
5. Navigating Long-Term Sustainability: Lessons from NASA’s Mars Global Surveyor
Reflection: Insights from NASA’s Mars Global Surveyor and NASA’s Mars Climate Orbiter.
Key Learning: Participants will leave with strategies for ensuring long-term sustainability and scalability in their data governance and team structures.
6. Wrap-Up and Key Takeaways
Summarizing the Journey: Recap of the solar system metaphor and how the workshop’s concepts apply to real-world data challenges.
Key Takeaways:
Q&A and Next Steps: Open the floor for final questions and discussions about how participants can implement the lessons in their own organizations
Detailed Workshop Outline
1. Introduction
Overview of Workshop Goals: Explain the importance of data as an asset and why organizations must move beyond treating data as just a service.
Solar System Metaphor: Introduce the concept of the data organization as a solar system, with data teams, governance, and accountability as key planetary bodies that need alignment for optimal performance.
Key Points:
2. Data Accountability: Creating a Culture of Ownership and Responsibility
Why Data Accountability Matters: Without clear accountability, data quality, security, and data availability suffer.
Practical Steps to Ensure Accountability:
Activity: Scenario-based discussion where participants identify where accountability is lacking in a fictional data-driven organization, and propose solutions for creating accountability.
Key Learning: Participants will gain insights into what data accountability entails, ensuring each team member knows their role in maintaining data quality and governance.
3. Data Governance Models: Federated Governance and Distributed Authority
Introduction to Data Governance: Why data governance is essential to manage risk, ensure compliance, and drive effective data use.
Federated Data Governance: What it is and how it works – balancing centralized oversight with distributed ownership across data hubs.
Key Components of a Data Governance Framework:
Activity: In groups, participants will design a federated governance model for a hypothetical organization, ensuring alignment between distributed teams and central governance.
Key Learning: Participants will learn how to implement a federated data governance model that balances control with autonomy, ensuring alignment across the organization.
4. Structuring Data Teams: Balancing Centralized and Distributed Needs
Discussion: Challenges in organizing data teams.
Activity: Group exercise where participants design an ideal data team structure that addresses both distributed and centralized organizational needs.
Key Learning: Participants will learn how to create a data team structure that is flexible enough to meet both innovation-driven and operational demands.
5. Navigating Long-Term Sustainability: Lessons from NASA’s Mars Global Surveyor
Reflection: Insights from NASA’s Mars Global Surveyor and NASA’s Mars Climate Orbiter.
Key Learning: Participants will leave with strategies for ensuring long-term sustainability and scalability in their data governance and team structures.
6. Wrap-Up and Key Takeaways
Summarizing the Journey: Recap of the solar system metaphor and how the workshop’s concepts apply to real-world data challenges.
Key Takeaways:
Q&A and Next Steps: Open the floor for final questions and discussions about how participants can implement the lessons in their own organizations
Practically all of our seminars and workshops can be offered as an In-house course for your company exclusively. We can tailor with extra focus on specific topics that apply to your organization. Also available in online format or in face-to-face format with live video stream.
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