Data

On January 11th, Shanghai’s vibrant startup community gathered for a night of learning and networking at the Startup Grind event. Entrepreneurs from all walks of life congregated to learn, share, and engage with one another. The event was marked by an exchange of ideas, insightful conversations, and an atmosphere that was palpable with entrepreneurial spirit.

As the adoption of Generative AI (GenAI) technology rises in large companies, IT managers are facing a unique set of challenges and concerns. Although GenAI holds immense potential to streamline operations and drive innovation, it also introduces new complexities in terms of management, security, and control.

Artificial Intelligence (AI) has become a buzzword in many industries, and it is fast becoming a reality for many knowledge workers. Organizations must understand how to best integrate AI to empower their employees. The knowledge worker is a vital asset in any company, and AI can help empower these workers by providing them with the information they need to make better decisions, faster, and focus their energy on higher value creation tasks.

Good data is the basis of good decisions in companies. Good data is also necessary to enjoy the benefits of business intelligence and machine learning. It is then surprising how much corporates spend on shiny technology versus producing good data. High-quality data can be an asset to companies that know how to use them.

Companies collect, store, process, and analyze data to make decisions. But the data is lying, the decision may not be the best, despite great care and effort. Why would data lie to you? There are many reasons why the data is lying. That could be the result of one of several mistakes at any stage of the process. That could be conscious or unconscious bias from one of the actors of the processing chain. There could be an agenda behind the data manipulation, or it could be an unfortunate coincidence. As companies are more and more data-driven, there is a tension between automating as much as possible the data management and processing, and the need for a bit of caution. There is a strong incentive to trust the data, but there should be a healthy suspicion about the data. Data must be treated as questionable until proven trustworthy. The issues with implementing this strategy are:

The current crisis has changed the way we do business forever but in hindsight, it has also given us the time to reflect on our habits and lean more towards efficient business practices, towards a data-driven corporate culture.