Disruptive conditions, due to external circumstances, require a fundamental digital change. In our #AENextNormal blog series Stef Devos talked about Accelerating Digital Business with Human Technology, or how people and society will seek a new equilibrium that will be created by a shift towards human technology. To create this shift, data science and artificial intelligence will play a crucial role in automating complex interactions.
May 25, 2018. This was the date every company and organization had to be GDPR compliant. What we see two years later is that many organizations still struggle to comply with the GDPR rules. Important concepts in the GDPR context are anonymisation and pseudonymisation. As such none of them is mandatory under GDPR. Securing and protecting data however is mandatory, and both anonymisation and pseudonymisation are effective methods to do so and therefore they are strongly recommended.
At AE, we deliver analytics projects on a daily basis and as a result we can experience from the front row that data-driven companies simply perform better than others. As data experts, we can all understand that “data is the new oil” and to put this philosophy into practice we have developed a unique data-driven innovation method to define your next best data product. Sounds interesting right?
Despite the enthusiasm for the digital transformation to a digital manufacturing future, two out of three companies piloting digital manufacturing solutions fail to move into large-scale rollout (Industry Week). Our experience learned us there are 3 dimensions to consider in your journey:
- Business Dimension
- Technological Dimension
- Organisational Dimension
The technological dimension of course is the one considered in most cases. And it is the enabler to a smart factory indeed. However, fail to balance technological actions with a clear focus on business goals, or to embed this new technology into your organisation and the people working in it, and you’re guaranteed not to meet your goals.
Due to the rapid acceleration in new coronavirus literature, it becomes difficult for the medical research community to keep up. There is a growing urgency for innovative approaches, like recent advances in Natural Language Processing, to understand and analyze the abundance of medical/scientific articles.