CONFERENCE PROGRAM OF 2022

Please note:
On this site, there is only displayed the English speaking sessions of the TDWI München digital. You can find all conference sessions, including the German speaking ones, here.

The times given in the conference program of TDWI München digital correspond to Central European Time (CET).

By clicking on "EVENT MERKEN" within the lecture descriptions you can arrange your own schedule. You can view your schedule at any time using the icon in the upper right corner.

Thema: Architecture

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  • Montag
    20.06.
  • Dienstag
    21.06.
  • Mittwoch
    22.06.
, (Montag, 20.Juni 2022)
13:45 - 15:00
Mo 2.2
ROOM E101/102 | Datenlöschen als Damoklesschwert über der BIA-Architektur
ROOM E101/102 | Datenlöschen als Damoklesschwert über der BIA-Architektur

Fast jeder Beitrag zu moderner BIA fängt mit dem Satz an 'Noch nie wurden so viele Daten wie heute gesammelt'. Es gilt als Daumenregel: Willst du Machine Learning machen, musst du viele Daten sammeln. Da wird man schon fast zum Spielverderber, wenn man das Thema Datenlöschungen anspricht. Erfahren Sie, warum es trotzdem wichtig ist, dieses eher unliebsame Thema als Spezialfall einer Data Governance auf die Tagesordnung zu setzen.

Zielpublikum: CDOs, CISOs, IT-Leiter, Datenschutzverantwortliche
Voraussetzungen: Grundlegendes Verständnis von Datenintegrationen und Datenschutz
Schwierigkeitsgrad: Einsteiger

Christian Schneider ist der Director Data & Analytics bei der QuinScape GmbH. Als Consultant und Projektleiter war er langjährig in internationalen Großprojekten tätig und kennt die vielfältigen Herausforderungen unterschiedlichster Integrations- und Analytikszenarien aus der praktischen Arbeit. Als Speaker und in Publikationen beleuchtet er die Aspekte des Aufbaus von nachhaltigen Dateninfrastrukturen mit einem im späteren Betrieb überschaubaren Kostenrahmen durch eine zielorientierte Data Governance.

ROOM E101/102 | The creation of a data culture nurtured by data governance
ROOM E101/102 | The creation of a data culture nurtured by data governance

The setup of a decentral function-based data governance requires time, shapes a continuous learning organisation and grows data capabilities and competence in the functions. Through these means a sustainable data culture is established and anchored, which plays a particular role in realising the strategic corporate goals, such as the digital transformation of processes.

Target Audience: Data Governance Manager, Data Passionist, CDO, CIO, Data Analytics Specialist
Prerequisites: Basic knowledge of the Data Governance
Level: Basic

Leonie Frank has worked in data management for the past 10 years for companies like Google and Swarovski and supported others in her role as a consultant. Her passion is to drive activities related to data management not only to roles and responsibilities, standards and guidelines but also to data architecture, data quality and data performance. Leonie’s goal is to enable teams that help to increase data maturity allowing to safeguard and utilise data as a company asset. She holds a degree in International Business Administration, a master in International Political Economy from the University of Warwick in the UK, a certificate in Statistics and one in Applied Information Technology from ETH in Switzerland. Leonie lives in Zurich and loves fine cooking and dining as well as mountaineering.

Christian Schneider
Leonie Frank
Christian Schneider

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Leonie Frank
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13:45 - 15:00
Mo 5.2
ROOM K4 | KI-Lösung ist das Ziel - mit ML Engineering erreichen Sie es
ROOM K4 | KI-Lösung ist das Ziel - mit ML Engineering erreichen Sie es

Künstliche Intelligenz ist schon längst dem Pionierzeitalter entwachsen. Doch um mit dem Einsatz von KI einen echten Mehrwert für das Unternehmen zu schaffen, kommt es auf die qualitativ hochwertige Bereitstellung von Daten an. Hier kommt ML Engineering ins Spiel - ein Konzept zur Bewältigung der hohen Komplexität von Daten bei der Entwicklung von KI-Systemen. Im Vortrag wird eine ML Engineering Roadmap vorgestellt, mit der dieses häufig unterschätzte und doch so kritische Konzept erfolgreich eingesetzt werden kann.

Zielpublikum: Data Engineer, Data Scientist, Unternehmer mit praktischem KI-Interesse
Voraussetzungen: Interesse an KI- und ML-Themen, Grundlagen- bis fortgeschrittene Kenntnisse in den Bereichen Data Science und/oder Data Engineering
Schwierigkeitsgrad: Fortgeschritten

Lars Nielsch ist als Principle Solution Architect Analytics & Cloud bei Adastra tätig. Nach seinem Studium der Angewandten Informatik an der TU Dresden ist er seit 1998 in der BIA-Beratung tätig. Seine besonderen Interessen liegen in den Themen Enterprise BI, Large Databases, Data Engineering (ETL-Design), Data Science (MLOps) und Big-Data-Architekturen (Data Vault, Data Lake, Streaming).

ROOM K4 | One Size Does Not Fit All: Make The Right Data Mesh For You
ROOM K4 | One Size Does Not Fit All: Make The Right Data Mesh For You

As the data mesh paradigm takes the industry by storm, the conversation deep dives into the architecture, neglecting the socio-organizational element. Data driven organizations must invest not only in infrastructure but also data organization and culture. 

Target Audience: Executive, senior business managers
Prerequisites: None
Level: Basic

Jennifer Belissent joined Snowflake as Principal Data Strategist in early 2021, having most recently spent 12 years at Forrester Research as an internationally recognized expert in establishing data and analytics organizations and exploiting data's potential value. Jennifer is widely published and a frequent speaker. Previously, Jennifer held management positions in the Silicon Valley, designed urban policy programs in Eastern Europe and Russia, and taught math as a Peace Corps volunteer in Central Africa. Jennifer earned a Ph.D. and an M.A. in political science from Stanford University and a B.A. in econometrics from the University of Virginia. She currently lives in the French Alps, and is an avid alpinist and intrepid world traveler.

Lars Nielsch
Jennifer Belissent
Lars Nielsch

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Jennifer Belissent
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, (Dienstag, 21.Juni 2022)
09:00 - 10:15
Di 3.1
ROOM K3 | Data Architecture: Data Lake vs Lakehouse vs Data Mesh
ROOM K3 | Data Architecture: Data Lake vs Lakehouse vs Data Mesh

In order to succeed in creating a data driven enterprise it is clear that choosing the right data architecture is now critical. This session explores the evolution of data and analytics architecture and looks at what is needed to shorten time to value and create a data driven enterprise. It looks at the pros and cons of data lake, lakehouse and data mesh architectures and asks: Is there a best approach? Is a lot more than this needed to succeed?

Target Audience: Data architects, CDOs, CAOs, enterprise architects, data scientists, business analysts
Prerequisites: Basic understanding of data architectures used in supporting analytical workloads
Level: Advanced

Extended Abstract:
In many companies today the desire to become data driven goes all the way to the boardroom. The expectation is that as more and more data enters the enterprise, it should be possible to understand and use it to quickly and easily drive business value. In order to succeed in creating a data driven enterprise it is clear that choosing the right data architecture is now critical. However, data and analytics architecture has been evolving over recent years to a point where now there are multiple options. Is it a data lake that is needed? Is it a lakehouse? Or is it a data mesh? Should this be the focus or is it just vendor hype to fuel their own interests?  What are the pros and cons of these options? Is there a best approach? Is a lot more than this needed to succeed? This session explores the evolution of data and analytics architecture and looks at what is needed to shorten time to value and create a data driven enterprise.

  • Data and analytics - where are we?
  • Data and analytics architecture evolution
  • Architecture options and their pros and cons - data lake Vs lakehouse Vs data mesh
  • The shift to data fabric, DataOps, and MLOps to industrialise pipeline development and model deployment
  • Using a data and analytics marketplace to putting analytics to work across the enterprise

 

Mike Ferguson is Managing Director of Intelligent Business Strategies and Chairman of Big Data LDN. An independent analyst and consultant, with over 40 years of IT experience, he specialises in data management and analytics, working at board, senior IT and detailed technical IT levels on data management and analytics. He teaches, consults and presents around the globe.

Mike Ferguson
Mike Ferguson
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10:45 - 12:00
Di 3.2
ROOM K3 | Data Lakehouse: Marketing Hype or New Architecture?
ROOM K3 | Data Lakehouse: Marketing Hype or New Architecture?

The data lakehouse is the new popular data architecture. In a nutshell, the data lakehouse is a combination of a data warehouse and a data lake. It makes a lot of sense to combine them, because they are sharing the same data and similar logic. This session discusses all aspects of data warehouses and data lakes, including data quality, data governance, auditability, performance, historic data, and data integration, to determine if the data lakehouse is a marketing hype or whether this is really a valuable and realistic new data architecture.

Target Audience: Data architects, enterprise architects, solutions architects, IT architects, data warehouse designers, analysts, chief data officers, technology planners, IT consultants, IT strategists
Prerequisites: General knowledge of databases, data warehousing and BI
Level: Basic

Extended Abstract:
The data lakehouse is the new kid on the block in the world of data architectures. In a nutshell, the data lakehouse is a combination of a data warehouse and a data lake. In other words, this architecture is developed to support a typical data warehouse workload plus a data lake workload. It holds structured, semi-structured and unstructured data. Technically, in a data lake house the data is stored in files that can be accessed by any type of tool and database server. The data is not kept hostage by a specific database server. SQL engines are also able to access that data efficiently for more traditional business intelligence workloads. And data scientists can create their descriptive and prescriptive models directly on the data.  

It makes a lot of sense to combine these two worlds, because they are sharing the same data and they are sharing logic. But is this really possible? Or is this all too good to be true? This session discusses all aspects of data warehouses and data lakes, including data quality, data governance, auditability, performance, immutability, historic data, and data integration, to determine if the data lakehouse is a marketing hype or whether this is really a valuable and realistic new data architecture.

Rick van der Lans is a highly-respected independent analyst, consultant, author, and internationally acclaimed lecturer specializing in data architectures, data warehousing, business intelligence, big data, and database technology. He has presented countless seminars, webinars, and keynotes at industry-leading conferences. He assists clients worldwide with designing new data architectures. In 2018 he was selected the sixth most influential BI analyst worldwide by onalytica.com.

Rick van der Lans
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14:30 - 16:00
Di 3.3
ROOM K3 | How to Design a Logical Data Fabric?
ROOM K3 | How to Design a Logical Data Fabric?

A popular new architecture for offering frictionless access to data is the data fabric. With a data fabric, existing transactional and data delivery systems are wrapped (encapsulated) to make all of them look like one integrated system. A data fabric enables all data consumers to access and manipulate data. Technically, data is accessed and used through services. But data fabrics cannot be bought, they need to be designed and developed. This session discusses key guidelines for designing data fabrics.

Target Audience: Data architects, enterprise architects, solutions architects, IT architects, data warehouse designers, analysts, chief data officers, technology planners, IT consultants, IT strategists
Prerequisites: General knowledge of databases, data warehousing and BI
Level: Advanced

Extended Abstract:
Companies are becoming increasingly dependent on data. Having access to the right data at the right time is essential. This implies that users need frictionless access to all the data, wherever it is stored, in a transactional database, a data warehouse, or a data lake. It does not matter to users where data comes from as long as it meets all their requirements. Users do not want to be hindered by all the data delivery silos. They want one system that gives them access to all the data they need.

The solution to provide frictionless access cannot be data warehouse-like, wherein all the data is copied (again) to one big central database. In this second era of data integration, integration must be achieved without copying. A new solution must be based on a single universal entry point to access all data. Where and how the data is stored, whether it is stored in various databases, must be completely hidden from data users.

A popular new architecture that supports this approach is data fabric. With a data fabric, existing transactional and data delivery systems are wrapped (encapsulated) to make all the independent systems look like one integrated system.  

A data fabric is formed by a software layer that resides on top of all the existing transactional silos and data delivery silos, enabling all data consumers to access and manipulate data. Technically, data is accessed and used through services.  

A real data fabric supports any type of service, whether this is a more transactional or analytical service. And especially the second group of services is complex to develop. Maybe analytical services based on predefined queries are not that complex to develop, but how are such services developed that need to deal with ad-hoc queries?

This session explains the need for data fabrics that support all types of services and discusses key guidelines for designing data fabrics. Technologies are discussed that help with developing such services.

  •  What a data fabric is, and why you need one
  • How you can architect a service-centric fabric to gain flexibility and agility
  • The data management and integration capabilities that are most relevant
  •  Where to start your journey to data fabric success
  •  What is logical data fabric?

 

Rick van der Lans is a highly-respected independent analyst, consultant, author, and internationally acclaimed lecturer specializing in data architectures, data warehousing, business intelligence, big data, and database technology. He has presented countless seminars, webinars, and keynotes at industry-leading conferences. He assists clients worldwide with designing new data architectures. In 2018 he was selected the sixth most influential BI analyst worldwide by onalytica.com.

Rick van der Lans
Rick van der Lans
Vortrag: Di 3.3
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, (Mittwoch, 22.Juni 2022)
09:00 - 10:30
Mi 2.1
ROOM K4 | Transforming Retail with Cloud Analytics - Petrol Case Study
ROOM K4 | Transforming Retail with Cloud Analytics - Petrol Case Study

Petrol is Slovenian company that operates in 8 countries in SEE with 5BEUR annual revenue. As traditional publicly-owned company, Petrol has faced necessity for transformation to stay ahead in highly competitive market. Use of BIA was mainly reactive, but in recent years it has transformed into competitive advantage by using cloud technologies and industry specific analytical models and focusing on the content and creating business value. This value is now being leveraged as competitive advantage through proactive use of data and analytics. 

Target Audience: Decision Makers, Data Architects, Project Managers 
Prerequisites: None 
Level: Basic 

Extended Abstract: 
Petrol is Slovenian company that operates in 8 countries in SEE with 5BEUR annual revenue. Main business activity is trading in oil derivatives, gas and other energy products in which Petrol generates more than 80 percent of all sales revenue and it also has a leading market share in the Slovenian market. Petrol also trades with consumer goods and services, with which it generates just under 20 percent of the revenue. Use of BIA was mainly reactive, but in recent years it has transformed into competitive advantage by using cloud technologies and industry specific analytical models and focusing on the content and creating business value. This value is now being leveraged as competitive advantage through proactive use of data and analytics. Presentation will cover main business challenges, explain technology architecture and approach and discuss results after three years of system development and use. 

Andreja Stirn is Business Intelligence Director with more than 20 years of experience working in the Oil & Energy and Telco industry. Skilled in Data Warehousing, Business Intelligence, Corporate Performance Management, Market Research and People Management.

Dražen Orešcanin is Solution Architect in variety of DWH, BI and Big Data Analytics applications, with more than 25 years of experience in projects in largest companies in Europe, US and Middle East. Main architect of PI industry standard DWH models. Advised Companies include operators from DTAG, A1 Group, Telenor Group, Ooredoo Group, Liberty Global, United Group, Elisa Finland, STC and many companies in other industries such as FMCG and utilities.

Andreja Stirn, Dražen Oreščanin
Andreja Stirn, Dražen Oreščanin
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11:00 - 12:30
Mi 6.2
ROOM F111 | Ten Practical Guidelines for Designing Data Architectures
ROOM F111 | Ten Practical Guidelines for Designing Data Architectures

Often, existing data architectures can no longer keep up with the current 'speed of business change'. As a result, many organizations have decided that it is time for a new, future-proof data architecture. However, this is easier said than done. In this session, ten essential guidelines for designing modern data architectures are discussed. These guidelines are based on hands-on experiences with designing and implementing many new data architectures. 

Target Audience: Data architects, enterprise architects, solutions architects, IT architects, data warehouse designers, analysts, chief data officers, technology planners, IT consultants, IT strategists 
Prerequisites: General knowledge of databases, data warehousing and BI 
Level: Advanced 

Extended Abstract: 
Many IT systems are more than twenty years old and have undergone numerous changes over time. Unfortunately, they can no longer cope with the ever-increasing growth in data usage in terms of scalability and speed. In addition, they have become inflexible, which means that implementing new reports and performing analyses has become very time-consuming. In short, the data architecture can no longer keep up with the current 'speed of business change'. As a result, many organizations have decided that it is time for a new, future-proof data architecture. However, this is easier said than done. After all, you don't design a new data architecture every day. In this session, ten essential guidelines for designing modern data architectures are discussed. These guidelines are based on hands-on experiences with designing and implementing many new data architectures. 

  • Which new technologies are currently available that can simplify data architectures? 

  • What is the influence on the architecture of e.g. Hadoop, NoSQL, big data, data warehouse automation, and data streaming? 

  • Which new architecture principles should be applied nowadays? 

  • How do we deal with the increasingly paralyzing rules for data storage and analysis? 

  • What is the influence of cloud platforms? 

Rick van der Lans is a highly-respected independent analyst, consultant, author, and internationally acclaimed lecturer specializing in data architectures, data warehousing, business intelligence, big data, and database technology. He has presented countless seminars, webinars, and keynotes at industry-leading conferences. He assists clients worldwide with designing new data architectures. In 2018 he was selected the sixth most influential BI analyst worldwide by onalytica.com.

Rick van der Lans
Rick van der Lans
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