In der schieren Menge an Daten, die in vielen Unternehmen schlummern, steckt ein enormes Wertpotential für das Business. Doch wie versetzen sich Organisationen in die Lage, diesen Datenschatz zu heben? Datensilos und heterogene Datenbestände scheinen oftmals eine unüberwindbare Hürde für eine nahtlose Integration zu sein – und die IT wird schnell zum Spielball aufwändiger und teurer Legacy Integrations-Technologien. Im Vortrag erfahren Sie von Robert Auerochs, wie die ING-DiBa diese Hürden in der Datenintegration und -nutzung überwindet und hierauf innovative und gewinnbringende Use-Cases aufsetzt.

Zielpublikum: Data Engineers, Data Scientist, Data Architects, Data and Analytics Manager, Chief Data Officer(CDOs), Chief Information Officer (CIOs), Data Analysts, Heads of Data Integration etc.
Voraussetzungen: Basic knowledge of data and analytics, especially data warehousing and data transformation processes and understanding of these in the larger organizational context to achieve the business goals
Schwierigkeitsgrad: Fortgeschritten

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

Innovative IT ermöglicht Unternehmen neue Kooperationsformen mit verschiedenen Partnern in offenen Netzwerken. Hierbei entstehen kooperative Datenräume auf der Basis IoT-basierter Digitaler Zwillinge, deren erfolgreiche Umsetzung leistungsfähige BIA-Infrastrukturen voraussetzen. 

Zielpublikum: Management, BIA-Verantwortliche, Chief Information Officer / Chief Digital Officer 
Voraussetzungen: keine
Schwierigkeitsgrad: Einsteiger

Factories of tomorrow are built based on digitized and modular elements and systems. 
In order to make sure (worker’s) safety is still given, reliable confirmations at runtime for autonomous processes and its dependencies as part of digital twins have to be deployed. 
The speech further will outline the way trustworthiness of datapoints ensure a resilience and productive operation as part of an enablement.

Target Audience: Infrastructure decision maker, CDO, COO, CEO, business owner, production manager
Prerequisites: Basic knowledge of data analysis - production methods - manufacturing technologies - supply chain
Level: Basic

Als integralen Bestandteil der Unternehmensziele formulierte der DKV Mobility (DKV) 2020 eine auf Use Cases gestützte Datenstrategie. Seit Anfang 2021 wird diese mit den drei folgenden Schwerpunkten operationalisiert: Der Aufbau der Abteilung Data Intelligence & Analytics, die Einführung einer cloud-basierten analytischen Plattform und die Umsetzung des Data Governance Programms.

Die wesentliche Säule des Data Governance Programms war dabei die Einführung des Data Catalogs, realisiert durch die Softwarelösung D-QUANTUM. In diesem Vortrag erläutert Sönke Iwersen gemeinsam mit Wolf Erlewein die Erfahrungen und Mehrwerte durch die Nutzung von D-QUANTUM. Durch die große Flexibilität der Software konnte das benötigte Business Glossary einfach und unkompliziert an die sich stetig entwickelnden Anforderungen angepasst werden, um die fachlichen Metadaten wie Kennzahlen, Geschäftsobjekte sowie deren Zuständigkeiten abzubilden.

Um die Entwicklung der analytischen Plattform durch den Data Catalog zu begleiten, wurden Snowflake und Microsoft Purview mit D-QUANTUM Connect angebunden.

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 a lot of the time. After a careful evaluation of options, Dillinger partnered with Scalefree to construct a new Data Vault 2.0-based data warehouse from scratch. Using the WhereScapes ETL tool chain allowed us to start the migration process with a few people and continually expand it while keeping the old 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: · Case study of a Data Warehouse project in the heavy plate industry · Build and operate a Data Vault 2.0 Data Warehouse with a small team · Challenges and tackles · Successful combination of internal team with external experts to get the Data Warehouse up and running

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.

This session introduces AI-driven automation and looks at the building blocks needed to automate operational tasks and decisions.

It discusses ground-breaking innovation that opens up the next stage in data and analytics for the data driven enterprise. It provides key information on how to use data, analytics and AI to automate decisions to significantly shorten time to reduce costs, reduce risks and seize opportunities to grow revenue.

Target Audience: Chief Analytics Officer, Chief Data Officer, Data Architects, Data Scientists, Business Analysts
Prerequisites: Basic knowledge of analytics and machine learning
Level: Advanced

Extended Abstract:
According to some analysts, the hyper-automation software market opportunity in 2025 will be as much as $850Bn. There is no doubt that if you can scale automation it will help companies significantly reduce costs and outperform their competitors in revenue growth. There are many things that can be automated including tasks, processes, IT and DevOps operations. However, there are so many things needed to make this happen. Given this opportunity, this session introduces AI-driven automation, looks at the building blocks needed to make it happen.  

• What is AI-driven automation and what can it do?  
• Size of the AI marketplace
• Use case opportunities
• Automating human tasks using AI-driven robotic process automation
• Intelligent document processing
• The power of human and AI-driven orchestration
• Automation building blocks
• Business goals
• Process mining  
• Data Events, business conditions and event detection
• Data integration
• Machine learning model services to predict outcomes
• Decision services
• Action services (skills that can be automated)
• Intelligent Orchestration - goal driven closed loop automation  
• The AI-driven automation process - capture, prepare, analyse, decide, act, optimise
• Human and AI-driven decision and action automation
• Getting started - what needs to be considered?

Dieser Vortrag handelt von Self-Service Analytics, Daten Demokratisierung und der Rolle des Modernen Analytics Teams. 

Eine Live-Demo von Veezoo, der State-of-the-Art Lösung für Conversational Interfaces, soll eindrücklich demonstrieren, wie Self-Service Analytics im 21-Jahrhundert auszusehen hat.

Digital transformation is in a way a never-ending journey. Recent trends put high expectations on AI technologies for process automation, insight generation and decision support. In Hilti, we see information generated from our data as substantial contribution to the success of our business. 
We will describe how we put the user and the usage of information in the center of our initiative of an information enabled company. Hilti’s journey towards process, data and system consolidation serves as an excellent foundation for that. We present the foundational technologies we put in place to manage the increasing amount and variety of data, as well as our “Integrated Information Management” approach. We will especially focus on advanced analytics and AI and give examples for successful implementations, but also highlight challenges, especially when it comes to change management and taking the organization along.

You've heard why companies use data warehouse automation (DWA) software, but what is the long-term impact on the business?

Maximilian Vollmer from  Volkswagen Commercial Vehicles explains practically and from personal experience how the team has changed its way of working since the introduction of WhereScape DWA over 5 years ago and how this has affected the business.

Bringing advanced analytics to end users through well-established reporting tools can be challenging. The solution we present in this talk enables business users to directly receive machine learning (ML) predictions for their custom selections in Tableau. We use Big Data and AWS (PySpark) technologies for our ML pipeline. The ML models are then deployed to MLflow and two-way communication to Tableau is ensured through APIs.

DOUGLAS ist führender Anbieter für Beauty Produkte mit über 2.000 Ladengeschäfte in Europa. In den letzten Jahren etablierte das Unternehmen eine sehr erfolgreiche Online-Beauty-Plattform mit mehreren Onlineshops. Erfahren Sie in diesem Vortrag direkt vom Principal Project Manager Data Thomas Borlik wie es DOUGLAS geschafft hat, mit einer modernen Datenarchitektur diesen Ausbau zu unterstützen. Dank zentralisierter Daten haben die Mitarbeiter und das Management schneller als je zuvor nun die Chance, anwendbare Erkenntnisse aus den Daten zu gewinnen. Durch diesen Schritt konnte insbesondere der manuelle Aufwand bei der Reporterstellung stark reduziert werden, sodass Analysten nun noch tiefer in die Daten einsteigen können. Wir freuen uns auf Ihre Teilnahme!

Embracing a holistic analytics across an organization can make it more innovative, agile, and successful. For instance, data democratization makes analytics more accessible and this leads to better-informed decisions. Modernizing data warehouses reduces complexity and improves reporting. Using hybrid cloud deployment strategies can increase the ability to analyze data no matter where it resides.

In this presentation, we will discuss how companies across different verticals are planning and implementing data warehouse modernization, data democratization, and hybrid cloud migration, improving revenue and efficiency to ensure market leadership.   

In his talk Paul Dietrich describes what 'United by Data' means to Collibra's customers and ways to support those 66% of companies which still struggle to turn their data into useful insights.

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?

Got great ML, analytics, and engineering talent, but need to increase the adoption of the ML and analytics solutions your team produces? Wondering how to design decision support applications and data products that actually get used and generate business value?  If you're tired of making 'technically right, effectively wrong' data products that don't get used, this session will help! 

Before you can generate business value, your data product first has to be used and adopted. That success boils down in part to the UX you afford users. After all, the UX is the perceived reality of your data product. However, the skills for designing a great UI/UX are different than those required to do the technical side of analytics, AI/ML, and engineering. Users don't want your data outputs; they want clear answers and actionable decision support—and that’s what we’ll learn how to do together. 

The workshop is a reduction of my full 8 week training seminar. In the ½ day workshop, we will focus on learning 3 main skills via a mixture of lecture, peer discussion, and active exercises/participation. You will learn:

• How to measure your data product’s utility and usability so that everyone on the team has a shared understanding of what a “good UX” is and how it will lead to business value 
• How to use 1x1 interview research to uncover hidden stakeholder and user needs before it’s too late (and your solution can’t be easily changed)
• How to use low-fidelity prototyping and sketching as a means to get aligned with your users and stakeholders and avoid building an incorrect “requirements-driven” solution 

MAXIMUM ATTENDEES: 48

MATERIALS YOU WILL NEED:

• A laptop is required for participation
• Willingness to participate in activities that require pair learning
• Willingness to be open and share with your table and the room when called upon to contribute
• For best results, you should have some sort of strategic decision support application, data tool, or data product in mind to which you hope this training can be applied when you return to work. Design is best learned through doing, and having a real project to apply it to will accelerate that learning. 

Target Audience: Directors, VPs, and principal data product leaders building custom enterprise data products and decision support applications for which adoption is critical to success and the generation of business value. Participants often come from ML and software engineering, analytics, and data science domains, yet also have a responsibility to ensure solutions are useful, usable, and valuable to the business. The training will not help you if you're interested in only working alone on implementation, coding, statistics, modeling and making outputs without any regard for whether they serve the audience they are intended to help.

Prerequisites: You're ready to approach your data work differently, with a human-first, data-second approach. You don't think that the reason that data tools/apps/dashboards go unused is because the users aren't 'smart enough' to understand them. You believe it's more interesting, fun, and valuable to produce data products that actually get used, produce value, and change people's lives. You're curious and open to non-analytical approaches to problem solving.

Level: Expert (you can be a design novice but should be a leader in your core field)

Extended Abstract:

Want to increase the adoption of your enterprise data products? 

It's simple: your team's AI/ML applications, dashboards, and other data products will be meaningless if the humans in the loop cannot or will not use them.

Yes, they may have asked your team for those ML models or dashboards.  

Unfortunately, giving stakeholders what they asked for doesn't always result in meaningful engagement with AI and analytics -- and data products cannot produce value until the first hurdle is crossed: engagement.  

Until users actually use, trust, and believe your ML and analytics solutions, they won't produce value.  

'Just give me the CSV/excel export.' How many times have you heard that -- even after you thought your team gave them the exact ML model, dashboard, or application they asked for?
No customers want a technically right, effectively wrong data product from your team, but this is what many data science and analytics teams are routinely producing -- because the focus is on producing outputs instead of outcomes. The thing is, the technical outputs are often only about 30% of the solution; the other 70% of the work is what is incorrectly framed as 'change management' or 'operationalization' -- and it all presumes that the real end-user needs have actually been surfaced up front.  

If you want to move your team from 'cost center' to 'innovation partner,' your team will need to adopt a mindset that is relentlessly customer-centered and measures its success based on delivering outcomes. However, this is a different game: it's a human game where ML/AI and analytics is behind the scenes and customers' pains, problems, jobs to be done, and tasks are at the forefront. Enter human-centered design and data product management: the other skills that modern data science and analytics teams will need if they want to become indispensable technology partners to their business counterparts.  

This talk is for data product leaders who have talented technical teams, but struggle to regularly deliver innovative, usable, useful data products that their customers find indispensable.  
You've heard for 20 years how Gartner and other research studies continue to predict limited value creation from enterprise data science and analytics engagements, with 80% of projects on average failing to deliver value.  

MIT Sloan/BCG's 2020 AI research shows that companies who are designing human-centered ML/AI experiences that enable co-learning between technology and people are realizing significant financial benefits. 
 
Leaders aren't repeating yesterday.  

If your data science and analytics requires human interaction before it can deliver any business value, you won't want to miss this session with Brian T. O'Neill -- the host of the Experiencing Data podcast and founder of Designing for Analytics. 

The Data Lake is dead, long live the Data Mesh! But what is a Data Mesh exactly? And how does Real-Time fit into it?

In this talk you’ll learn what a Data Mesh approach brings to the table, as well as the why and how of creating a Real-Time backbone for your Data Mesh architecture.

Bei allen Trends, die rund um ‘Data Management’ kommen und gehen ist eins klar: es ist vielmehr ein kontinuierlicher Weg, als ein finales Ziel, das zählt. Um bestehen zu können, brauchen Organisationen einen agilen Ansatz, der die Grundlage dafür legt, den Berg- und Talfahrten von technologischen Trends zu folgen und das mit geringem Aufwand.
Als ein führender Anbieter diskutieren wir mit Ihnen unseren ‘Agile Data Fabric’ Ansatz und zeigen, wie verschiedene Trends umgesetzt werden können.
 

DB Regio Bus, a subsidiary of Deutsche Bahn, has been executing a data strategy to support digitization and automation in a very decentralized organization. In this presentation, Asha, Marcus, and Christian will provide insights, methods, and lessons learned about the data strategy at DB Regio Bus and their experiences related to its successful execution.

Target Audience: Chief Data Officers, Chief Digital Officers, Data Manager, Data Governance Manager, Data Governance Directors
Prerequisites: None
Level: Basic

SWICA historically runs a data warehouse built by a centralized team and in parallel, multiple isolated solutions for domain specific analyses, which afford high maintenance and an extensive effort to stay compliant.

Modernizing our analytical environment, we are building a collaborative platform on MS Azure, utilizing the Data Mesh paradigms of data domain and data product.

We aim to deliver a managed data marketplace for all data domains to provide their data products on a modern platform with low maintenance and built-in security & compliance.

Target Audience: Data Analysts, Data Engineers, Project Leaders, Decision Makers
Prerequisites: Basic understanding of the data mesh concept, data warehouse architectures and the challenges of diverse analytical use cases from multiple lines of business
Level: Advanced
 

"Die Transformation der Banken & Versicherungen ist in vollem Gange. Neue Services und Geschäftsfelder sollen sowohl das Kundenerlebnis deutlich verbessern, als auch das Wachstum und die Zukunftssicherheit der Unternehmen sichern.
Klassische Big Data & AI Ansätze halten mit den neuen Anforderungen schon kaum Stand - von zukünftigen Entwicklungen ganz abgesehen.
Wie kann z.B. die persönliche Versicherungsmaschine der Zukunft (McKinsey) zu jedem Zeitpunkt in Echtzeit die optimale Entscheidung treffen, wenn wir noch immer über Datensilos, Datensümpfe und DWH Strukturen auf Host-Rechnern sprechen?
Niko Dyundev und Oliver Börner beschreiben einen Lösungsansatz aus der Praxis für die Praxis! Welches Konzept ist zukunftssicher? Wie schaffe ich den Spagat zwischen Kompatibilität mit der Organisation und moderner Services? Was ist eine Architektur der Zukunft, die schon nachweislich funktioniert?"

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. 

Zum Management einer Bonus Club Karten Lösung mit mehreren Geschäftspartnern musste binnen kürzester Zeit eine BIA Lösung aufgebaut werden.
Im Vortrag wird gezeigt wie die Anbindung der Geschäftspartner über Cloud und OnPrem Komponenten erfolgt und mittlerweile seit Beginn dieses Projektes 16 individuelle Partner DWH Lösungen inkl. einer Unified DWH Lösung aufgebaut wurden.
Die DWH Lösungen selbst wurden On Prem implementiert. Die Reporting Anbindung der Geschäftspartner inkl. der Data Mart Schicht liegt dann wieder in einer Cloud Umgebung. Im Vortrag wird auf die Herausforderungen und Lösungsansätze im Zuge der Umsetzung dieser komplexen hybriden Architektur eingegangen. 

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? 

To improve business outcomes, SaaS software provider Frontify aimed to empower all its 200 employees with fast, self-service insights from one version of data truth. To this end, Frontify’s data team migrated from its legacy infrastructure to a low-latency, modern data stack comprising Snowflake, Fivetran, and ThoughtSpot, and is on a mission to drive adoption and business impact. - “Data for all” is now well within reach.

In this joint session presented by Ramesh Shurma, CEO, Orion Governance and Timm Grosser, Senior Analyst Data & Analytics at BARC, we will share the newest 2022 BARC findings. 

For highly distributed data landscape with growing data volumes, extensive data movement is not a viable concept. Data warehouse, data lakes, data lakehouses approaches are reviewed and experts discuss modern concepts such as data fabric. These have the objective of reducing the complexity and scope of data processes while increasing flexibility and agility. It also aims to simplify data access and use, especially for business users.

Our survey (BARC Data Black Holes, 2021) concludes that it is mainly a lack of available documentation and explicit knowledge  that stands in the way of achieving these goals. Such explicit knowledge is available extensively in the organization, namely in the form of metadata, but collecting metadata is a difficult and time-consuming process. Without a smart and automated approach, it is doomed to fail. This is where Agile Data Intelligence comes in. Similar to how Data Fabric helps stitch together the organization's business data landscape, agile data intelligence is the corresponding metadata fabric. In this presentation, we'll talk about Agile Data Intelligence's three core capabilities for building a Metadata Fabric. They lend a Data Intelligence Platform the necessary agility to be operated effectively and efficiently.

A victim of its success, Siemens' Pulse Analytics, an in-house developed platform offered to internal and external users to support decision making, was struggling to keep pace with the demands of its users for super fast dashboards as data needs increased over time.

Join Christoph Malassa, Managing Consultant / Head of Analytics and Intelligence Solutions, Siemens, to discover how he googled 'what's the fastest relational database analytics’ and ended up deploying SingleStore with the following benefits:
•    brought response rates from 2+ seconds down to milliseconds on queries spanning billions of rows
•    removed the need for time consuming processes to get fresh data into production.
•    Auto-scaling to accommodate daily users ranging from 500 to 100,000
•    And much more
Now Siemens Pulse Analytics helps even the largest organizations transform complexity into operational excellence!

Data Analysts and Data Scientists invest an immense amount of time into optimizing models and interpreting data, all in the quest to promote better business decision making and more efficient product development. We oftentimes however fail to take a step back and answer the overarching question: Why does the user show the observed behavior pattern? Why does a certain variable improve the accuracy of our prediction model? Despite all the advances we have made in analytics, even predictive analytics and ML models cannot truly answer what the user was thinking and why they act in a certain way. 

Adding the customer perspective into the insights equation opens up a whole new perspective on this problem. As a consequence, XING merged the User Research and Analytics departments to create a more holistic approach to insights generation. This presentation walks through the problem statements, the differences in the professional fields (analytics and research) and how the individual segments of both disciplines are complementary and lead to a more user centric decision making organization.

Target Audience: anyone open to thinking outside of the regular patterns of analytics and AI/DS 
Prerequisites: none 
Level: Basic 
 

Der Einsatz von Künstlicher Intelligenz zur Betrugserkennung bei Heilmittel- und Pflegeleistungen in der gesetzlichen Krankenversicherung. Von der Pseudonymisierung und Digitalisierung der Abrechnungsbögen bis zur Analyse, Auswertung und Darstellung der Anomalien - Ein Projektbericht! 

Zielpublikum: Management, Data Scientists, Data Engineers 
Voraussetzungen: Experience, Curiosity 
Schwierigkeitsgrad: Fortgeschritten

The Coronavirus lockdowns altered public transport occupation data. Ultimately, these changes in occupation data are perfect examples of sudden concept drifts that can be blockers in most machine learning deployments. We managed to overcome the obstacles by developing methods and engineering features that allowed us to adjust forecasts based on unforeseen changes in the occupation data. In this talk, we give insights into our journey from idea development to the ways how we overcame the challenges and share our learnings. 

Target Audience: Data Engineers, Software Architects, AI Architects, Data Scientists 
Prerequisites: Experience in Time Series. Basic understanding of machine learning 
Level: Expert 

Extended Abstract: 
Machine learning projects often view and predict a snapshot interval of reality. We machine learning engineers often forget that the real world is not static at all.  

After all, we got hit by the Coronavirus reality rendering countless machine learning models useless.  

The changes in public transport occupation out of Corona lockdowns is a perfect example of these so-called concept drifts. 

The instability of the machine learning models when concept drifts are appearing result in immense problems for the reliability and predictability of the AI.  

Therefore, we dig into this problem and show how we solved this challenge for the case of concept dirfts in public transport occupations. 

For many years, technology gurus, transformation evangelists and many more have pictured a world that will dramatically change with incredible pace. Consequently, it was predicted that impacts on society, economy, environment, and political landscapes will leave no stone unturned. As a matter of fact, the current times feel as if these predictions have eventually become reality. The VUCA world is not only part of our daily life, but also even a nucleus in itself that demands resilience from individuals as well as societies and organizations. 
While climate change and pandemics seem to be part of the “new normal”, global conflicts get closer to the western world resulting in even more severe instabilities of supply chains, natural resource availabilities and much more - clearly stretching the long-held promise of a flourishing globalization. 
To avoid the “Uber yourself before you get Kodaked” pitfall, companies no matter what size are finding themselves coping with an environment that is certainly fiercer these days but at the same time allowing for new opportunities that need to be discovered and unlocked. But what’s the right strategy to capitalize on these opportunities if strategies itself are not even worth the paper written on? How to keep the pace with rapidly shortening technology lifecycles or tech innovations that don’t seem to deliver against their promise? Is it even worth to define comprehensive roadmaps on digital strategies and transformations? 
In his keynote, Thomas Kleine reflects on the value of defining digital roadmaps from a company perspective. He will incorporate not only his personal experiences but also refer to his employer’s journey over the last two years specifically propelled to the frontline of fighting the COVID-19 disease. What are the key learnings and what about the half-value time of these learnings if tomorrow comes with a completely different set of challenges?