For the work we do in the long-term-vision-labs and strategy team for the Technology & Innovation board area at SAP is one of the playgrounds for the title question. These topics are good, because they generate exiting and abundant questions, that we believe should be asked by everyone.

How does the future work, the mechanics of change? What is the future of human work and our interactions with machines and each other? Is industrialization of space and 3d printed organs really a “thing” and how do you relate to these? What are the methods to innovate desirable futures? How do you structure such a vast and complex fabric of possibilities in your domain, team, business, boardroom? How long is the history of the future and how does that affect our fear and counter reactions to it in socio-political movement? Most importantly, who designs the future, that you your community will live in? 

We talk about change, but what are the mechanics and the dynamics behind it? How fast is it? What it means to be an innovator, is transforming faster than before, from classic product and service definition of the superficial to computational system design of everything, including social and political systems, deeply rooted in a space of challenges and promises between cutting edge tech and humanism. In an exponential and converging digital-fueled future, we design a relationship, a behaviour, that the product will follow.

What comes before strategy? We love to act on strategies, derive tactics, execute and operate, it is our psychological bias, we love to do what we are good at. But how do we derive a strategy? From what point of view, what narratives, what builds up a “Future fabric” that these narratives a woven from? And who does it in your enterprise? We will have, high level look at how we build a desirable future with these questions in mind, and also what we in our labs consider to be a desirable future of work, at least for the coming decade.

This exponential change is our innovation brief and the stakes are high, It is just too important to be left only to... any single team. Technology is the human evolution, the prior named HuMachine creates a playground for “Human centered“-adventure, this opens new worlds for our imagination in a time when “now” has never been so temporary. Bringing these thoughts together we need to answer the question “What is human, and what is work in a superhuman future?”

Main chapters

1. How Science-Fiction is becoming science-fact, and... business fact, now. The narratives and examples of current futures.
2. What values, attitudes, most of all methods and frameworks can we use to derive our
3. What we, SAP derived as our direction for the future of work and the tech to support it from, the above 2 insights.

Over the last few decades, ETL and specially Datawarehouse testing has been gaining quite a bit of traction. The reason for that traction? The modern enterprise's dependency on data. This dependency calls for the right testing strategy to ensure the quality and correctness of the provided data. Further on the drive to lower 'time to data' and overall costs is putting high pressure on Test specialists to increase efficiency in this area. In this presentation I want to show you our journey to daily regression in our enterprise data warehouse.

Target Audience: Testers, Test Managers, Project Leaders, Decision Makers, Data Engineers, Managers
Prerequisites: none
Level: Basic

Extended Abstract:

Over the last few decades, ETL and specially data warehouse testing has been gaining quite a bit of traction. Especially given the emergence of Agile and DevOps as top trends in the software development industry. The reason for that traction? The modern enterprise's dependency on data.

This dependency calls for the right testing strategy and execution to ensure the quality and correctness of the provided data. 

Further on the drive to lower 'time to data' and overall costs is putting high pressure on Test specialists to increase efficiency in this area.

In this presentation I want to show you our journey to daily regression in our data warehouse. 

We improved our processes and changed our testing approach. We started with a quarterly regression test which took a whole testing team of 10 people 2 months to finish. 

Now we do the same on a daily basis within our agile team.

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 architecture.

Target Audience: Data architects, data warehouse designers, IT architects, analysts, enterprise architects, solutions architects
Prerequisites: Some understanding of data architectures
Level: Advanced

Extended Abstract:

It is not big data that is the biggest change in the IT industry, it is data usage that has changed most drastically the last ten years. 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. This may mean that more data needs to be collected resulting in big data systems, but the goal remains to do more with data. Unfortunately, current data delivery systems don't have the right characteristics for supporting the requirements of big data systems, such as supporting data science, handling massive data streaming workloads, managing heavy data ingestion loads, and so on. Additionally, many organizations that are developing big data systems, are developing isolated data delivery systems, such as an isolated data lake, an isolated data streaming system, an isolated data services system, and so on. This avalanche of data delivery systems is not beneficial to the organization. The wheel is invented over and over again. It's time to design real big-data-ready data architectures. This session discusses such an architecture, including the supporting technologies, pros and cons.

Using distributed databases across various microservices will be explained based on a research project example. The presentation elaborates of how to achieve data consistency across microservices, how to communicate using message brokers, how to scale the microservices and achieve high application availability. Container virtualization and orchestration are technology basis for the solution. The project example shows of how to build an Artificial Intelligence (AI) solution – as a service!

Target Audience: Data Engineer, Data Scientist, Project leader, Architects
Prerequisites: Common understanding of database technology and architecture
Level: Advanced

Extended Abstract:

Developing microservices have various advantages compared to traditional monolith approach. Loose coupled microservices enables smaller teams to develop independently and using CI/CD at a daily basis. Microservices can be scaled independently using different database technologies optimised for particular use cases. The microservices are fault isolated so particular failures will not result in an overall outage.

But how to ensure data consistency for all distributed databases across the microservices? How to react at particular failures? And how to interact and communicate between services?

A research project for intelligent energy analysis will be presented. The solution realizes an Artificial Intelligence (AI) solution analyzing streaming data near real time to ensure energy savings in a production environment. The presentation will explain the steps necessary to establish a microservice environment for Artificial Intelligence and Machine Learning. Central logging guarantees operations monitoring across all microservices. Dashboards presents the results to technical staff monitoring the Machine Learning libraries as well as to the process owner of the domain, e.g. the operations manager or insurance agent. The solution is based on Docker as container virtualisation technology and Kubernetes for container orchestration. In the research project, the solution is realized on premise, but can be easily deployed in the cloud.

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 numerous projects is discussed.

Target Audience: Data architects, data warehouse designers, IT architects, analysts, solutions architects
Prerequisites: Some understanding of database technology
Level: Advanced

Extended Abstract:
Data virtualization is being adopted by more and more organization for different use cases, such as 360 degrees customer views, logical data warehouse, and self-service BI. The effect is that knowledge about this agile data integration technology is available on how to use it properly. In this practical session the lessons learned are discussed. Tips and tricks, do's and don'ts, and guidelines are discussed. In addition, answers are given to questions, such as: How does data virtualization work and what are the latest developments? What are the reasons to choose data virtualization? How to make the right choices in architecture and technology? How does data virtualization in your application landscape? Where and how do you start with the implementation? And what are the biggest pitfalls in implementation? In short, the expertise gathered from numerous projects is discussed.

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, Enterprise Architect
Prerequisites: Understanding of data integration software and development using a CI/CD DevOps approach
Level: Advanced

Extended Abstract:
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.

• The increasingly complex data landscape
• Challenges in governing and integrating data in a distributed multi-cloud environment
• What is data fabric, why it is important and what technologies are on the market?
• What is a data catalog and what products are on the market?
• Why organizations need these technologies?
• How do data catalogs and data fabric work together?
• A closer look at data fabric capabilities
• Defining common data entities in a catalog business glossary
• Using a catalog for automatic data discovery
• A DataOps CI/CD Approach to building reusable trusted data assets in using data fabric

Building a global analytics function across a diverse application landscape incl. SAP and multiple data sources provides many challenges. See how ABB successfully managed this journey and now enjoys the benefits of operational analytics globally, a shift in mindsets and a more data driven way of working.

You will also discover the impact of key technologies used (Change-Data-Capture, Automation & AI) and see real examples of specific analytics deployed in the business.

Target Audience: Business analysts, decision makers, C-Level
Prerequisites: Basic Knowledge
Level: Basic

Extended Abstract:

How do you enable digital transformation and create value through analytics?

This session tells the story of building a global analytics function in an environment with a diverse set of applications including a complex SAP system landscape and many data sources. The speaker will talk about some of the challenges in the journey but also the success in deploying operational analytics globally, shifting mindsets and help transition to a more digital/data driven way of working.

The audience will also discover the impact of key technologies used (e.g.: Change-Data-Capture, Visualization, Automation and AI) and how these helped to create value and drive revenue increase for ABB, using real examples of specific analytics deployed in the business.

A successful data culture brings together people from across the business to collaborate, share and learn from each other about data, analytics and the business value data can hold. In this session I share my suggestions for bringing together your most talented data people so your organization can gain more value from their skills and the data assets you invested in.

Come with an open mind and leave with tips, tricks and ideas to get started straight away.

Target Audience: Data practitioners, analysts, CoE leaders, Management
Prerequisites: basic understanding of how businesses use data
Level: Basic

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 the topic formally known as metadata.

Target Audience:
- Enterprise-, Systems-, Solutions and Data Architects
- Systems-, Strategy and Business Intelligence Managers
- Data Warehouse and Data Lake Systems Designers and Developers
- Data and Database Administrators
- Tech-Savvy Business Analysts

Prerequisites: Basic knowledge of data management principles. Experience of designing data warehouses or lakes would be useful

Level: Basic

Extended Abstract:

Metadata has – quite correctly – been at the heart of data warehouse thinking for three decades. It's the sort of cross-functional and overarching topic that excites data architects and data management professionals. Why then can so few businesses claim success in implementing it? The real question is: implementing what?

This session answers that question by first refining our understanding of metadata, which has been made overly technical over the years and more recently been misappropriated by surveillance-driven organisations. With this new understanding, we can reposition metadata not as a standalone topic but as a vital and contiguous subset of the business information that is central to digital transformation. With this positioning, 'metadata implementation' will become a successful – but largely invisible – component of information delivery projects.