What’s driving DEMAND?
Digitization is everywhere. Data-driven work is the norm. What’s more, the use of artificial intelligence is a hot topic with the rise of generative AI. Yet there is still a gap between tech giants, start-ups and the professional practice of SMEs. There is an overwhelming amount of data available. How can we use data in a way that’s ethically responsible, efficient and safe? DEMAND aims to create the right tools to answer these unanswered yet crucial questions.
From shared knowledge to a shared toolbox
DEMAND is funded by Taskforce for Applied Research SIA. This Dutch governing body fosters collaboration between various institutes, professorships and business partners. The goal? To improve the quality and widen the impact of the applied research done by universities of applied sciences in the Netherlands. Likewise, the goal of DEMAND is to maximize shared knowledge and develop a toolbox of new applicable digital technologies and methodologies. Data chains form the basis of a solid data-driven organization, but how organizations also reap the benefits of this is a challenge. DEMAND aims to contribute to the optimization of data chains in private and public organizations with this toolbox, because having the data foundation in place is crucial for any further application of digital innovations.
The focus of DEMAND
DEMAND initially focuses on data chain cases in the manufacturing industry, energy sector and public organizations. Later, other application areas will be included. DEMAND provides solutions for integrated and validated data chains/data pipelines.
By data experts, for data users
DEMAND creates a bridge between the latest insights from the fields of AI Engineering, Data Engineering and Data Management and real-world data issues. Our research project involves 3 universities of applied sciences (HAN, Saxion and Fontys) and 16 partners. DEMAND works on a shared toolbox. How? By identifying problems and maximizing shared knowledge. Staying connected helps us to connect the dots. Likewise, we see it as a shared responsibility to deal with data in a way that’s ethically responsible, safe and efficient.
That’s why we’ve established learning communities that focus on:
- AI Engineering: The design, development and deployment of architecture and digital environments for optimally enabling, and supporting advanced data processing and algorithmics within AI and Data Science operational applications.
- Data Engineering: The design, development, and deployment of data provision as a technical/digital system, including sensorics/IoT, data architecture, data pipelines, and data storage, among others, serving to make available good quality, appropriate data (including Big Data) for applications in information and knowledge systems, BI, data analytics, and AI, among others.
- Data Management: Developing, implementing, and overseeing plans, policies, standards, programs, practices, methods, and tools that help deliver, control, protect, and enhance the value of data & information assets throughout their lifecycle.
