Hinweis: Die aktuelle TDWI-Konferenz finden Sie hier!

PROGRAMM

Die im Konferenzprogramm der TDWI München digital 2021 angegebenen Uhrzeiten entsprechen der Central European Time (CET).

Per Klick auf "VORTRAG MERKEN" innerhalb der Vortragsbeschreibungen können Sie sich Ihren eigenen Zeitplan zusammenstellen. Sie können diesen über das Symbol in der rechten oberen Ecke jederzeit einsehen.

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Track: Analyst Track

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  • Montag
    21.06.
  • Mittwoch
    23.06.
10:10 - 10:50
Mo 3.1
The need for a unified big data architecture

Big data is not the biggest change in the IT industry but data usage. To become more data driven and to succeed with their digital transformation, organizations are using their data more extensively to improve their business and decision processes. Unfortunately, it is hard for current data delivery systems to support new styles of data usage, such as data science, real-time data streaming analytics, and managing heavy data ingestion loads. This session discusses a real big-data-ready data…

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Rick van der Lans
11:20 - 12:30
Mo 3.2
Data virtualization in real life projects

Data virtualization is being adopted by more and more organization for different use cases, such as 360 degrees customer views, logical data warehouse, democratizing data, and self-service BI. The effect is that knowledge about this agile data integration technology is available on how to use it effectively and efficiently. In this session lessons learned, tips and tricks, do's and don'ts, and guidelines are discussed. And what are the biggest pitfalls? In short, the expertise gathered from…

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Rick van der Lans
14:00 - 15:30
Mo 3.3
DataOps: using data fabric and a data catalog for continuous development of data assets

As the data landscape becomes more complex with many new data sources, and data spread across data centres, cloud storage and multiple types of data store, the challenge of governing and integrating data gets progressively harder. The question is what can we do about it? This session looks at data fabric and data catalogs and how you can use them to build trusted re-usable data assets across a distributed data landscape.

Target Audience: CDO, Data Engineer, CIO, Data Scientist, Data Architect,…

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Mike Ferguson
16:00 - 17:10
Mo 3.5
No more metadata, let's talk context and meaning

Metadata is a long-time favourite at conferences. However, most attempts to implement real metadata solutions have stumbled. Recent data quality and integrity issues, particularly in data lakes, have brought the topic to the fore again, often under the guise of data catalogues. But the focus remains largely technological. Only by reframing metadata as context-setting information and positioning it in a model of information, knowledge, and meaning for the business can we successfully implement…

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Barry Devlin
17:30 - 18:10
Mo 3.6
Der Mörder ist immer der Gartner

Ein sehr ernster humorvoller Blick auf die Modewellen der IT, ihre Auswirkung auf die Informationsprodukte im Unternehmen und ein methodischer Umgang mit dem Hype Cycle.

Der Mörder ist immer der Gartner, weil er bestimmt, wann ein Hype zu Ende ist und damit die Methode tötet. Typische Aussage fachfremder Menschen mit und ohne Budgetverantwortung: 'Warum machen wir das noch? Gartner sagt, dass ist nicht mehr aktuell.' Ungeachtet der Tatsache, dass die dahinterliegende Methode aus den 70ern…

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Michael Müller, Oliver Cramer
09:00 - 10:30
Mi 4.1
Is there life beyond hadoop – should data lakes be drained?

The past year or two has seen major changes in vendor support for the extended Hadoop ecosystem, with withdrawals, collapses, and mergers, as well as de-emphasis of Hadoop in marketing. Some analysts have even declared Hadoop dead. The reality is more subtle, as this session shows, through an exploration of Hadoop's strengths and weaknesses, history, current status and prospects. Discussion topics include plans for initiating new Hadoop projects and what to do if you have already invested,…

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Barry Devlin
11:00 - 12:10
Mi 4.3
Data science workbenches and machine learning automation – new technologies for agile data science

This session looks at how data science workbenches and machine learning automation tools can help business analysts to become data scientists and so meet the demand of business.

Target Audience: CDO, Head of Analytics, Data Scientist, Business Analysts, CIO
Prerequisites: Basic understanding of Data Science
Level: Advanced

Extended Abstract:
The demand for analytics is now almost everywhere in the business. Analytics are needed in sales, marketing and self-service, finance, risk, operations,…

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Mike Ferguson
14:30 - 18:10
Mi 4.4
Best practices in DataOps for analytics

Analytics team are struggling to create, publish, and maintain analytics to meet demand. Many analytics projects fail to meet expectations and deliver value. DataOps is the new approach combining tools and approaches to simplify the development of analytics and ensuring high quality data. DataOps shortens the life cycles, reduces technical debit and increases analytics success. This session covers the best practices for the analytics team to deliver DataOps.

Target Audience: Data scientists,…

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Deanne Larson

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