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The largest value lies where business intersects

Very often the largest value increase for data lies at the intersection between subject areas, where data of different origins are combined and new insights are gained. Many advancing technologies (Virtual Reality, Artificial Intelligence, Big Data Analytics, Machine Learning, etc.) are even designed with the main purpose of exactly harvesting this immense value hidden within the growing pools of datasets.

However, for a number of reasons it is very difficult to combine datasets from different subject areas and thereby unleashing the largest value of all, namely the information hidden at the intersection between subject areas. The Environmental Intelligence solution seek to address exactly this challenge.

The EI-Group is addressing three of the largest problems that prevents access to- and sharing of technical data across subject areas

1. Project-centric/Silo work flows

In organizations structured around projects there is a tendency for data to still end up in a project folder after project termination without any chance for reuse by other projects at other times.

There is no culture or incitement to support sharing of valuable data after it has been used for its primary purpose. Therefore the silo culture is prevailing, where each person, team or project, individually gather and manage their required data, even though their neighboring colleagues are doing the exact same thing.

2. Software encapsulates data

Many software products are designed to manage both input and output of data. The internal structure of the data is therefore solely focused on supporting its own suite of output tools. In reality, data collected by one tool is very often of great interest to others that unfortunately are left to access the data through the same tool suite as originally collected it.

3. Knowledge about data is detached from data

Specialists with deep insight in the data itself often have no understanding of where to get the data in an optimal way. On the other hand IT specialists with knowledge about how to manage data in general have no domain knowledge about the data they are managing.

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