Prioritization of Cross-Sectorial Requirements
An actionable roadmap should have clear selection criteria regarding the priority of all actions. In contrast to a technology roadmap for the context of a single company, a European technology roadmap needs to cover developments across different sectors. The process of defining the roadmap included an analysis of the big data market and feedback received from stakeholders. Through this analysis a sense of what characteristics indicate higher or lower potential of big data technical requirements was reached.
As the basis for the ranking, a table-based approach was used that evaluated each candidate according to a number of applicable parameters. In each case, the parameters were collected with the goal of being sector independent. Quantitative parameters were used where possible and available.
In consultation with stakeholders, the following parameters were used to rank the various technical requirements. The ranking parameters included:
- Number of affected sectors
- Size of affected sector(s) in terms of % of GDP
- Estimated growth rate of the sector(s)
- Possible prognosticated estimated growth rate by the sector due to big data technologies
- Estimated export potential of the sector(s)
- Estimated cross sectorial benefits
- Short term low-hanging fruit
Using these insights, a prioritization composed of multiple parameters was created, which give a relative sense of which technological requirements might be poised for greater gains and which would face the lowest barriers. The ranking of cross-sectorial technical requirements is presented and illustrated in the table and figure below, where colour indicates the level of estimated importance, and size of the bubble the estimated affected sectors of the industries. It is important to note that these indices do not offer a full picture, but they do offer a reasonable sense of both potential availability and capture across sectors. There are certain limitations to this approach. Not all relevant numbers and inputs were available as the speed of technology development and adoption relies on several factors. The ranking relies on forecasts and estimates from third parties and the project team. As a consequence, it is not always possible to determine precise numbers for timelines and specific impacts.
Prioritization of technical cross-sectorial requirements
| Prioritization | Technological Requirements | Score |
| Level 1: Urgent | ||
| Data Security and Privacy | 78 | |
| Data Management Engineering – Data Integration | 69.25 | |
| Deep Data Analytics – Real-time Insights | 61.5 | |
| Data Management Engineering – Data Sharing | 48.5 | |
| Level 2: Very important | ||
| Data Quality | 40.5 | |
| Data Management Engineering – Real-time Data Transmission | 37 | |
| Deep Data Analytics – Modelling Simulation | 37 | |
| Deep Data Analytics – Natural Language Analytics | 37 | |
| Deep Data Analytics – Pattern Discovery | 34.25 | |
| Deep Data Analytics | 31.75 | |
| Data Management Engineering | 31.5 | |
| Level 3: Important | ||
| Data Management Engineering – Data Enrichment | 29.5 | |
| Data Visualization and User Experience | 29.5 | |
| Deep Data Analytics – Prescriptive Analytics | 29.5 | |
| Deep Data Analytics – Usage Analytics | 26.75 | |
| Data Quality – Data Improvement | 24 | |
| Deep Data Analytics – Predictive Analytics | 20.75 |
Excerpt from: Becker, T. et al. (2016) ‘Cross-Sectorial Requirements Analysis for Big Data Research’, in Cavanillas, J. M., Curry, E., and Wahlster, W. (eds) New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe. Springer International Publishing. doi: 10.1007/978-3-319-21569-3_15.