BIM is not only offering design in the construction industry, it can also be the way we share our data, the digital information management. In this article we explore what is a common data environment and how BIM can be one for the projects, bringing together digitalization and standardization.
Construction & Building Industry•Tequma AG••7 minutes
Choosing the right ERP for you and your business goes beyond your immediate market reality. The right resource planning must match the expectations of many stakeholders and simultaneously answer all technical challenges and business model requirements.
The phrase "data is the new oil" is floating around every organization and across industries. The value of data has become the focus of many C-Level executives and Senior Managers and since then companies started collecting as much data as they can. Nevertheless, it's not only about collecting and structuring data, but also about visualization and interpreting data to support fact-based decision-making.
Network (knowledge) graphs represent a collection of interlinked entities organized into contexts via linking and semantic metadata. They build a framework for data integration and analysis. Having the ability to do this can provide more context around metrics recorded from a network system. By leveraging these graphs, you can enhance your understanding of the data.
Any business wants to spend its marketing dollars wisely. Creating campaigns that are general in nature likely won’t deliver high revenue. Instead, you want to target as precisely as possible based on your customers’ buying activities. We can all agree that consumer behavior patterns are shifting and changing. Many things impact these patterns, both internally and externally. The last year-plus shifted the consumer base tremendously. For example, 75 percent of consumers tried new brands and different ways of shopping since the beginning of the pandemic. Consumer behavior is a “live” data set, and deriving insights from it needs to happen quickly, so you can pivot to align with current motivations.
The advancements of AI in various areas of our life make it one of the most appreciated technologies today. Impressive successes have been achieved, and the expectations are constantly rising. A tendency to transfer technology from the consumer space to industrial applications is also observed in this area. However, according to several analysts papers, industrial AI projects never get beyond a PoC in approximately 75% of the cases. Why is that the case?
Enthusiastically building on AI’s success in the consumer space often leave some important properties of industrial applications overlooked. In this article we list a few industrial challenges that have to be addressed to make an AI project successful.