Conference Outline

The conference starts at 9:30 am and ends at 5:15 pm on both conference days. Registration commences at 8:30 am.

Daily schedule:

1. Fast Data: The Next Frontier of Big Data (Dutch spoken)
Rick van der Lans, Managing Director, R20/Consultancy

In the first generation of big data systems, the focus was primarily on storing and analyzing very large amounts of data. The focus was entirely on volume. Currently, organizations have entered the second phase of big data: fast data. Fast data is about streaming and instant analysis of large amounts of data. This is the world of the Internet of Things (IoT), where interconnected devices communicate with each other over the Internet, but also of machine-generated sensor data and weblogs. Everything revolves around speed. Fast data is clearly the next frontier of big data systems. And most organizations will have to deal with this now or in the future, from the most traditional financial institutions to manufacturers and online gaming companies.

In this massive flow of data, valuable business insights are often deeply, very deeply hidden. The business value of fast data is in the analysis of all this streaming data. Unfortunately, the analysis of fast data is different from the analysis of enterprise data stored in data warehouses, in that it is using data visualization tools. For example, fast data can be very cryptic in nature, so it often has to be combined with enterprise data, which in turn is stored in the data warehouse. And to be able to do something useful with it, this data should be analyzed in real-time, because an immediate response is expected. Sometimes the data has to be analyzed even before it is stored. The world of fast data is a new world. This session discusses the architectural aspects of fast data, provides guidelines for adopting fast data and explains how fast data can be integrated within the existing BI environment.

• What is the relationship between fast data and the classic world of business intelligence and data warehousing?
• A new architecture is necessary for processing fast data
• Technologies and products are needed for analyzing fast data
• How should fast data be integrated in the enterprise data warehouse?
• The challenge of real-time responding to incoming fast data
• What is the relationship between big data and the IoT?

2A. Organizing the Data Lake: How to Extend Data Management beyond the Data Warehouse
Mark Madsen, President and founder of Third Nature

Building a data lake involves more than installing and using Hadoop. The focus in the market has been on all the different technology components, ignoring the more important part: the data architecture that the code implements, and that lies at the core of the system. In the same way that a data warehouse has a data architecture, the data lake has a data architecture. If one expects any longevity from the platform, it should be a designed rather than accidental architecture.
What are the design principles that lead to good functional design and a workable data architecture? What are the assumptions that limit old approaches? How can one integrate with or migrate from the older environments? How does this affect an organization’s data management? Answering these questions is key to building long-term infrastructure.
This talk will discuss hidden design assumptions, review some design principles to apply when building multi-use data infrastructure, and provide a conceptual architecture. Our goal in most organizations is to build a multi-use data infrastructure that is not subject to past constraints. This conceptual architecture has been used across different organizations to work toward a unified data management and analytics infrastructure.

You Will Learn:

• Data architecture alternatives that are able to adapt to today’s data realities
• New ways of looking at technology that can be applied to address new problems inherent in today’s uses and scale of data
• Methods and techniques to migrate from older data architecture to new ones that resolve today’s problems and prepare for the future

2B. The Road to the Amsterdam Smart City Infrastructure (Dutch spoken)
Rutger Rienks, Program Manager Datapunt, Gemeente Amsterdam.

This session will be about one of the most modern data infrastructures in de the world. Using open source components and a scrum/agile way of working the Amsterdam smart city infrastructure is realized.
The hurdles that need to be taken in a huge governmental organization as well as the personal and technical challenges will be covered. Also, given actual smart city cases the potential of information led decision making and decision support in the smart city will be discussed.

• Why a local government should transform into an intelligence driven organisation?
• How to influence an organization to kick-start the movement?
• Some examples of increased well-being of citizens and improved governmental activity.
• How to mobilize city workers to abide by advice presented by theoretical models
• Insights in the ambitions of the smart city datainfrastructure

3A. Rocking Analytics in a Data Driven World (Dutch spoken)
Bart Baesens, professor at the KU Leuven and lecturer at the University of Southampton (UK)

Companies are being flooded with tsunamis of data collected in a multichannel business environment, leaving an untapped potential for analytics to better understand,
manage and strategically exploit the complex dynamics of customer behavior.  In this presentation, we will start by providing a bird’s eye overview of the analytics process model and then illustrate how to fully unleash its power in some example settings. We will review data as the key ingredient of any analytical model and discuss how to measure its quality.  We will zoom into the key requirements of good analytical models (e.g. statistical validity, interpretability, operational efficiency, regulatory compliance etc.) and discuss emerging applications.  Throughout the presentation, the speaker will extensively report upon his research and industry experience in the field. Attendees will learn:

• The impact of data quality on analytical model development
• The key requirements for building successful analytical applications
• The trade-off between accurate and interpretable analytical models
• Emerging analytics applications and accompanying challenges
• State-of-the art research and industry insights on Big Data & Analytics.

3B. Agility through Data virtualization: from Data Vault to SuperNova to a Logical Data Warehouse (Dutch spoken)
Jos Kuiper, IT Enterprise Architect, Volkswagen Pon Financial Services

How can we improve agility in preparing data for end-users and for information products, like reports, dashboards etc.?  For this purpose a proof of concept with a data virtualization solution was performed.
Given a number of challenges in a traditional BI architecture, Jos will dive into the merits of a data virtualization solution. In a proof of concept a data virtualization solution was tested,  on top of a Data Vault Data Warehouse. Also, the capability of the data virtualization solution to combine historic data, stored  in the Data Vault, with live data stored in back-office systems, was subject of investigation. In this presentation there will also be a brief introduction to the data modelling methods Data Vault and SuperNova. This session will provide insights on:

• What is the occasion for data virtualization?
• How the proof of concept was conducted, with a brief introduction to the data modelling methods Data Vault and SuperNova
• The benefits of a data virtualization solution
• The fit of data virtualization in a BI/data warehouse architecture
• And an unexpected benefit …

4. Agile Project Management for Data Warehouse and Business Intelligence Projects
William McKnight, President McKnight Consulting Group

5. Logical Data Lake and Logical Data Warehouse: two sides of the same Coin (Dutch spoken)
Rick van der Lans, Managing Director, R20/Consultancy

6A. Strategies for Consolidating Enterprise Data Warehouses and Data Marts into a Single Platform
William McKnight, President McKnight Consulting Group

6B. The renewed BI-landscape of the Erasmus MC using the Scrum Framework and Data Virtualization  (Dutch spoken)
Kishan Shri, Advisor Business Intelligence and Scrum Master at Erasmus Medical Center (MC)

7A. IoD: Internet of Data (Dutch spoken)
Pieter den Hamer, Lead Big Data, Business Intelligence & Analytics, Alliander

• How the ‘Internet of Things’ leads to the ‘Internet of Data’: the growing need for agile data sharing and integration
• Beyond the semantic web: from idealism to pragmatism
• State-of-the-art technology & tools
• The problem of semantics: why IoD may fail (as well) and how AI may come to the rescue
• From enterprise data warehouses to enterprise linked data networks: the Intranet of Data
• IoD in the public sector and smart cities.

7B. Implementing the Enterprise Data Delivery Platform using Data Vault Modelling (Dutch spoken)
Erik Fransen, Management consultant at Centennium

Organizations continue to struggle with the challenges they face in delivering Data and Analytics solutions to their customers. Many still have the classic reporting factory in place, originating from the 90’s datawarehouse and business intelligence architectures. And let’s face it: although it did deliver value in creating the standard reports, delivering data in a fast, integrated and bespoke way for interactive analysis was never its true intention. This is where these data architectures now fail in the new era of Data & Analytics where instant analysis, any data access, data integration, fast and easy delivery is crucial in satisfying the user demands. Users demand real time delivery of both enterprise data and data from other sources, fast implementations, big data access, BI and ETL self service, impact analysis and lineage insights. These next generation Enterprise Data Delivery Platforms (EDDP) should make use of modern ensemble modelling methods like Data Vault to become more agile, flexible and transparant in adapting to any data source, in a uniform, consistent way while using data virtualization technology for fast delivery to the user.

• Data & Analytics challenges: more, faster, better, cheaper and easier
• Trimodal Data & Analytics streams: innovative predictability versus predictable innovation
• Short history of Data Vault: from modelling the classic EDW to modelling the next generation EDDP
• Data Vault and the Enterprise Data Delivery Platform: implementing the logical datawarehouse
• Data Vault Use cases

8. Beer, Diapers and Correlation: A Tale of Ambiguity
Mark Madsen, President and founder of Third Nature

The story of the correlation of beer and diaper sales is a common one, still used to discuss the value of analytics in retailing and marketing. Rarely does anyone ask about the origin of this story. Is it true? Why is it true? What does “true” actually mean?
The latter question is the most interesting because it challenges beliefs about the usefulness and accuracy in analytic models. Many people believe that data is absolute rather than relative, and that all analytic models produce an answer rather than a range of answers.
This is the history of the beer and diapers story, explaining its origins and truth (or falsehood), based on repeated analysis of retail data over the intervening decades. It will explain how one can have multiple, contradictory results and how they can all be simultaneously true. This brings up the real question: how does one apply analytics in business when the data does not give you an unambiguous answer?

• Lessons learned with applying analytics and data science
• Is the notorious beer diaper example really true?
• Data science is one half of the solution, interpreting the results is the other half
• There is no single version of the truth
• It’s not the insight, but what you do with it, that matters.

Cases

 

Case: Data driven management: How data scientists employ an advanced data platform and self-service analytics in the DJ & Entertainment industry (Dutch spoken)

Edwin Witvoet, Chief Executive Jibe Company
Mischa van Werkhoven, Principal Solution Architect

Festivals, DJs and artists make use of a range of networks to ensure growth of their ‘media company’. This enables them to keep building on fan relations, market share, sponsors, sales etc. However, with that much data, understanding how these networks contribute to the company goals, is becoming more and more complex. Find out how Jibe uses Qlik to offer Data Management & Intelligence.

 

Case: Change your current data warehouse to an agile data warehouse by adding more complexity
Jos Driessen, founder BI Demystified

But how can adding more complexity make you more agile? Experts can tell you how to build an agile data Warehouse. Use a Data lake and concepts like Logical data Warehouse, Data Vault and Supernova.
The answer is data warehouse automation:  In this session, learn how programming robots can generate a data warehouse for you.  You retain control. You decide what to build. But you don’t have to dive into the details. Robots will do that for you.
This will change your projects into initiatives and enable Business analytics.

Case: Continuous Integration in Business Intelligence. Innovation driven, culturally inspired.

Martin Pardede, BI Delivery Manager, Bestseller
Lukas Ames, IT consultant, Cimt

Bestseller is experiencing strong growth in its E-commerce department, the division is going through a process from being in essence a successful start-up to becoming a future leader in fashion e-commerce.
To have a BI system that is suitable to cope with this rapid expansion not only the technical aspects were considered but also a process that ensures quality of the system. This session will offer you a view of how Bestseller combines company-culture with best-practices from the developer-community. We will talk about how we created a way of working with: Scrum, Jira, Talend, Bitbucket and TeamCity.

 

Case: Instrument your business with an enterprise-ready data lake (Dutch spoken)

Rens Weijers, Director Data & Performance Management, Nuon Vattenfall
Rixt Altenburg, Manager Customer Insights, Nuon Vattenfall

It is often clear to  analists and other data professionals: the traditional BI environment is insufficiently capable of making use of all available data.  A data  lake is required to process large amounts of data, store new data formats and get the necessary tools swiftly and efficiently to combine and analyze the data. Making this step is required to generate new customer insights or to improve internal processes.  The question is how to realize a swiftly implemented data lake? In a corporate environment, with lots of various stakeholders, this is not an easy step. Furthermore, because of the subject being unknown to the organization, there will be a lot of uncertainties about how to get it fully operational.

Subjects that will be discussed are:

• The cooperation with IT
• How to get senior management on board
• Implementing a data lake and the operational challenges
• Lessons learned from the Nuon case