DATA-DRIVEN BUSINESS VALUE
BUSINESS CHALLENGES YOU CAN SOLVE – WITH DATA VAULT
Data Dreamland is the perfect event for business and technical oriented participants.
Whether you are new to the field or have a lot of experience, a visit to Data Dreamland is a great way to learn new things, get to know tools, make new contacts, or participate in discussions.
Travel with us to Data Dreamland and explore great opportunities for your business!
Learn
… how to solve business challenges of various scales, e.g. by using Data Vault.
Listen
… to how others have overcome their difficulties and find out what the best approaches are for your business challenges.
See
… which tools can be helpful, how they work and how they can support you in the best possible way.
Discuss
… best practices with experts and learn from the mistakes of others to avoid wasting time and money on trial and error yourself.
Join Data Dreamland and explore the many ways you can master your business challenges
AGENDA
VIEW FULL AGENDA
8:15 am - 8:45 am
REGISTRATION
8:45 am - 9:00 am
EVENT KICK-OFF
9:00 am - 10:00 am
KEYNOTE: SCOTT W. AMBLER - "AGILE DATA WAREHOUSING & BI: ADDRESSING THE HARD PROBLEMS"
10:30 am - 11:30 am
MORITZ HEIMPEL - "LEVERAGE DATA VAULT IN A DATA MESH ARCHITECTURE: A SIEMENS DATA STORY"
12:00 pm - 1:00 pm
MARK VAN DER HEIJDEN - "TRANSITION FROM BI BACKEND DEVELOPER INTO A (DATA) ENGINEER"
2:00 pm - 2:30 pm
JAFFAR AHMAD & MARC FINGER - "DATA VAULT IS NOT PERFECT – WHY IT'S STILL THE MOST FUTURE PROOF METHODOLOGY"
2:45 pm - 3:15 pm
HASAN LODHI & MARKUS LEWANDOWSKI - "SALESFORCE INTEGRATION WITH SNOWFLAKE"
3:30 pm - 4:30 pm
DIRK VERMEIREN & JONAS DE KEUSTER - "EVERYTHING IS A REPEATABLE PATTERN"
5:00 pm - 6:00 pm
DALE ANDERSON - "BUILDING AN AGILE DATA LAKE"
7:00 pm - 9:00 pm
EVENING ACTIVITY
8:30 am - 8:45 am
REGISTRATION
8:45 am - 9:00 am
WELCOME SESSION
9:00 am - 10:00 am
KEYNOTE: FRANCESCO PUPPINI - "TACKLING DATA POLLUTION"
10:30 am - 11:00 am
VOLKER NÜRNBERG - "DATA VAULT FOUNDATION PRINCIPLES AND THEIR (BUSINESS) VALUE"
11:00 am - 11:30 am
STEPHANIE SCHRADER & JAN ESCHWEILER - "DATA-QUALITY AUTOMATION @AXA BASED ON DATA VAULT, WHERESCAPE AND JIRA"
12:00 pm - 1:00 pm
LASSE WEBERG & CHRISTIAN AASTRUP - "HOW TO LEVERAGE WEALTH DATA MODELLING USING DATA VAULT 2.0" (NYKREDIT)
2:00 pm - 3:00 pm
CORNÉ POTGIETER - " THE ACCIDENTAL ARCHITECT - HOW I FUMBLED MY WAY TO A WELL-BUILT WAREHOUSE"
3:30 pm - 4:00 pm
DR. NICOLAS FRITZ & DR. ALEXANDER BRUNNER - "MADE OF STEEL - REBUILDING A HISTORICALLY GROWN DATA WAREHOUSE" (SUCCESS STORY)
4:15 pm - 5:15 pm
PAUL RANKIN - "DATA MESH & DATA VAULT - CAN THEY REALLY WORK TOGETHER?" (ROCHE)
5:30 pm - 6:00 pm
MICHAEL OLSCHIMKE - "FAIL HARD FAST: BETTER LEARN FROM THE AUDIENCE"
6:00 pm - 6:30 pm
OFFICIAL CLOSING
Sponsors
SPEAKERS
Fail Hard Fast: Better Learn From the Audience
This session is all about mistakes: what challenges did others have in their data-driven projects or other contexts…and how they bombed it.
This is an interactive, but guaranteed anonymous session with members of the audience.
Learn how NOT to do it and how to tackle challenges.
This session is all about mistakes: what challenges did others have in their data-driven projects or other contexts…and how they bombed it.
This is an interactive, but guaranteed anonymous session with members of the audience.
Learn how NOT to do it and how to tackle challenges.
The world moves at a rapid pace, and your organization must be able to respond to changing conditions. Your DW/BI team is being asked to help end users answer new questions to gain new insights at an increasing pace. They need to become agile, but are struggling to do so.
In this session Scott Ambler addresses a series of difficult questions that DW/BI must have answers to if they are to learn how to work in a work in an agile manner:
- How can we proceed without modeling everything up front?
- What can we do when our users can’t tell us what data they need?
- How can we easily respond to changing requirements?
- How do we implement “vertical slices” of value?
- It takes weeks to analyze a legacy data source, how does that fit into a two-
week sprint?
- How can we realistically deal with the quality problems of all the data
sources that we work with, and usually aren’t responsible for?
- Our end users want updates in hours or days, how do we do that in two-week
sprints?
- Is it realistic to evolve production data sources?
- How can we deliver new changes quickly into production?
- And more.
Organizations around the world have successfully adopted agile and lean ways of
working (WoW) on the DW/BI teams.
You can too.
Data Quality Framework:
Learn how AXA used Data Vault, WhereScape and Jira for their data quality automation.
Learn how AXA used Data Vault, WhereScape and Jira for their data quality automation.
Moritz Heimpel will talk about his experiences and the use of Data Vault in a Data Mesh Architecture at Siemens.
Siemens is innovating its Analytics ecosystem using Data Mesh. Distributed domains with individual responsibilities co-create and collaborate within one ecosystem to transition Siemens into a Data Driven Company. A major challenge is to ensure consistency across domains. Data Vault place a vital role in mastering this challenge.
Siemens is innovating its Analytics ecosystem using Data Mesh. Distributed domains with individual responsibilities co-create and collaborate within one ecosystem to transition Siemens into a Data Driven Company. A major challenge is to ensure consistency across domains. Data Vault place a vital role in mastering this challenge.
Tackling Data Pollution
You have “Data Pollution” when you create more and more copies of the same data: you increase the space on the disk, but you don’t add any new information.
Thanks to the new cloud technologies, we can reduce pollution, because we can “query the data where it lives”.
But I will share more tricks and techniques.
Data pollution is not only about saving space on disk, but also (and especially) about reducing confusion among the people searching for information.
You have “Data Pollution” when you create more and more copies of the same data: you increase the space on the disk, but you don’t add any new information.
Thanks to the new cloud technologies, we can reduce pollution, because we can “query the data where it lives”.
But I will share more tricks and techniques.
Data pollution is not only about saving space on disk, but also (and especially) about reducing confusion among the people searching for information.
Let’s talk about the modernization of cloud-based data engineering solutions.
Dale Anderson presents his ‘Data-as-a-Solution’ or DaaS, which codifies the integration and best practices for Data Governance, Workflow Automation, Metadata Driven Frameworks, Plug-n-Play Business Rules, and the Datapoint Repository. His ‘Agile Data Lake’ approach offers a full view of the Blue-Print and Data Solution Accelerators using Talend, Snowflake, and Data Vault 2.0.
Dale Anderson presents his ‘Data-as-a-Solution’ or DaaS, which codifies the integration and best practices for Data Governance, Workflow Automation, Metadata Driven Frameworks, Plug-n-Play Business Rules, and the Datapoint Repository. His ‘Agile Data Lake’ approach offers a full view of the Blue-Print and Data Solution Accelerators using Talend, Snowflake, and Data Vault 2.0.
Data Quality Framework:
Learn how AXA used Data Vault, WhereScape and Jira for their data quality automation.
Learn how AXA used Data Vault, WhereScape and Jira for their data quality automation.
Are there simply too many conflicts in ownership and boundaries?
In this session, we will present how Roche has managed to successfully adopt Data Vault whilst maintaining data product ownership and boundaries. If during a data product development there are some contributions to the Business Data Vault by the data product team, and there are contributions to the model by the Core team, how will the ownership be reassigned once the implementation is over?
Will the use case team own everything that came out of this collaboration?
Will a central team own it?
We will take you through our journey and demonstrate how we managed to transition the more traditional centralized ownership of our Data Vault model to a more distributed domain ownership where intersections and responsibilities are clearly defined.
In this session, we will present how Roche has managed to successfully adopt Data Vault whilst maintaining data product ownership and boundaries. If during a data product development there are some contributions to the Business Data Vault by the data product team, and there are contributions to the model by the Core team, how will the ownership be reassigned once the implementation is over?
Will the use case team own everything that came out of this collaboration?
Will a central team own it?
We will take you through our journey and demonstrate how we managed to transition the more traditional centralized ownership of our Data Vault model to a more distributed domain ownership where intersections and responsibilities are clearly defined.
In this presentation you will learn how Nykredit will use DV 2.0 to make the Wealth data modelled in a structured and efficient way and use the methodology to handle classic challenges with trades, pension and asset management.
Dillinger had built an "on-demand data warehouse" for its production data with many independent tables using its own ETL applications in Java over two decades. With a very small team, maintaining the warehouse took up most of the available time. After a careful evaluation of options, Dillinger partnered with Scalefree to construct a new DV2.0-based data warehouse from scratch. Using the WhereScape ETL toolchain allowed us to start the migration process with only a small number of people and continually expanding it while keeping the old data warehouse alive. In this case study, we describe the construction of a technical data warehouse based on Data Vault 2.0 and WhereScape ETL automation, the challenges, how we addressed them and the feasibility of our approach.
What you will learn:
· All about the case study of a DWH project in the heavy plate industry
· How to build and operate a DV2.0 DWH with a small team
· How to overcome challenges and avoid pitfalls
· How to get a DWH up and running in a successful combination of an internal team with external experts
What you will learn:
· All about the case study of a DWH project in the heavy plate industry
· How to build and operate a DV2.0 DWH with a small team
· How to overcome challenges and avoid pitfalls
· How to get a DWH up and running in a successful combination of an internal team with external experts
Are you holding off a Data Vault implementation, because you lack the skill set or don‘t know how to enable yourself and your team?
Do you fear losing control of Data integration by using automation tools or are you uncertain about which technologies to use or do you simply struggle to choose the right business keys?
Do you fear losing control of Data integration by using automation tools or are you uncertain about which technologies to use or do you simply struggle to choose the right business keys?
Challenges you may encounter during your Data Vault implementation and how you might want to address them.
Salesforce is a leading cloud-based CRM platform that integrates with an amazing number of applications and Snowflake is one of them. Organizations that want to combine Salesforce and Snowflake don't have to struggle with slow data movement.
Preparing, converting, and exporting CRM data manually is very complicated and belongs to the past. That's why Salesforce and Snowflake are working together to help customers load data directly from Salesforce to Snowflake. This presentation will focus on the integration of Salesforce with Snowflake.
Preparing, converting, and exporting CRM data manually is very complicated and belongs to the past. That's why Salesforce and Snowflake are working together to help customers load data directly from Salesforce to Snowflake. This presentation will focus on the integration of Salesforce with Snowflake.
During the last two decades we have witnessed software development teams adopt a high standardisation on infrastructure, tools and processes, while their business intelligence counterparts have been applying more bespoke manual processes. Now more than ever, we have run out of excuses and have transitioned to more of an engineering process. What does that mean? What is the impact on your existing team? Does it require changes to your ecosystem? Tune into our session to learn more.
Building the Raw Data Vault is about not breaking the DV2 patterns. VaultSpeed brings a no-code solution for this. But how do you build on top of the raw vault, and how can you automate?
This is where we give users the keys to our automation engine:
This is where we give users the keys to our automation engine:
Join the session and learn more about our template studio.
How well you define a problem determines how well you solve it. When you use a template-driven approach to data modeling, you are forced to solve the problem conceptually first rather than programmatically. That is definitely one of the reasons why the automation bug bit me.
The temptation is always there to quickly go and fix something directly in the code. You can solve almost any problem by coding your way out of it, but it is not sustainable to have 100s of bespoke coding patterns.
By being forced to automate repetitive tasks, you will become better at defining patterns, and you will solve your issues by conceptually solving them first.
WhereScape is a complete data automation solution that provides the flexibility needed to quickly meet your needs today and is readily able to adapt for the future.
The temptation is always there to quickly go and fix something directly in the code. You can solve almost any problem by coding your way out of it, but it is not sustainable to have 100s of bespoke coding patterns.
By being forced to automate repetitive tasks, you will become better at defining patterns, and you will solve your issues by conceptually solving them first.
WhereScape is a complete data automation solution that provides the flexibility needed to quickly meet your needs today and is readily able to adapt for the future.
Remembering some of the basic principles, why they are good and how to check your current or upcoming data platform projects.
e.g.:
• Changing by adding (only)
• Lot‘s of tables - complex or plain?
• Following source changes quickly
• Pushing deeply downstream, solve issues lalest
e.g.:
• Changing by adding (only)
• Lot‘s of tables - complex or plain?
• Following source changes quickly
• Pushing deeply downstream, solve issues lalest
Get your ticket now
Be part of the live event and enjoy the full 2 days of the Data Dreamland Conference 2022
