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SmartRegio
Management Consultant for Smart Energy in rural regions. Provides statistics from social media platforms as well as individual data of little regions in terms of mobility, energy and so on.
Type of content:
Assets
Type of asset:
Use case
Platform
Big data potential
Yes
Policy domains:
Environment & Energy
Phase in the policy cycle:
Policy Design and Analysis
Open license availability
No
Website
Tags:
E-Governance
Information management
Serves:
Addresses:
| SWOT Analysis for SmartRegio |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Recognition of trends from different data sources that have low cost: include search queries, transactions, case numbers contributions in (social) media and open platforms such as OSM and Wikipedia, but also information from city, community or infrastructure providers from energy, mobility and much more. • Is based on a three-tier architecture model: a) data integration and data retention b) evaluation and analysis of data and c) visualization of data. • Integration: All three levels are decoupled by open interfaces, and each level is given its own API. In this way, third-party vendors at all levels can deliver their own contributions and create marketplaces for data, analysis services and special visualization tools • Standardization and labelling of data as well as a form of data management that enables their high-performance processing |
Weaknesses• For small-scale areas, the data base is often more expensive. SmartRegio is developing a solution that combines mass data from many different sources as a basis for decision-making (heterogenous mass data). • The use of heterogeneous mass data poses high technical and legal challenges. • Heterogeneous mass data are distributed across many different data silos, not linked, and the potential data providers have no experience on this role • Heterogeneous mass data exist in many different formats and structures, many of which need extensive pre-processing to unlock their content. • Heterogeneous mass data spatial-temporal parameters differ, which makes their comparison difficult. E.g. While statistics are collected for administrative areas, infrastructures require their own spatial classification for technical reasons, and in the case of media or discussions in social networks, it is often difficult to determine the spatial relationship. • Data ownership issues • Data privacy issues: Many of the data sources contain personal information, and their anonymisation is particularly difficult due to the spatial reference and the combination of many sources. |
External |
Opportunities• Small and medium enterprises are at a disadvantage. First, their financial resources are limited and, secondly, they are much more rooted in their region. For small-scale areas, the data base is often more expensive and worse. SmartRegio is therefore focusing on these players and is developing a solution that combines mass data from many different sources as a basis for decision-making. • Smart home, home automation, variable tariffs, decentralized energy generation, storage or mobile charging. |
Threats• Small and medium enterprises are at a disadvantage. First, their financial resources are limited and, secondly, they are much more rooted in their region. For small-scale areas, the data base is often more expensive and worse. • The use of heterogeneous mass data poses high technical and legal challenges. • Heterogeneous mass data are distributed across many different data silos, not linked, and the potential data providers have no experience • Heterogeneous mass data exist in many different formats and structures, many of which need extensive pre-processing to unlock their content. • Heterogeneous mass data spatial-temporal parameters differ, which makes their comparison difficult. While statistics are, for example, collected for administrative areas, infrastructures require their own spatial classification for technical reasons, and in the case of media or discussions in social networks, it is often difficult to determine the spatial relationship. • Data privacy issues: Many of the data sources contain personal information, and their anonymisation is particularly difficult due to the spatial reference and the combination of many sources. |
