In recent years the growth in the amount of available data has increased enormously. More and more companies, government organizations and citizens are becoming data dependent. Simultaneously with this growth in the availability of the amount of data, there is increasing interest in opening up this data. Open data is data that is freely available to everyone, can be reused by everyone and can be redistributed by everyone again. Up to now, it is mainly government organizations that make the data publicly available or use open data sources themselves. However, various studies have indicated that a great potential for open data is still unused. If a larger part of the current available data were to be opened up, much (economic) value could be achieved. However, these studies do not determine for commercial companies. These companies are currently lagging behind in opening up their data because they do not see the potential of this. Opening up data is seen as unnecessary insight for the competition and a loss of the market position and the annual turnover. To show companies the value of open data, this research aims to design a method that companies can apply. By applying this method it becomes clear to companies which data in the current structure of their business system has the potential to be opened up and subsequently what value this opened-up data yields for the company.

This methodology, named "Open Data Implementation Methodology (ODIM)" in this study, was designed using the design science research approach. The various phases in this approach made it clear which factors are important of open data at commercial companies and, among other things, offered a framework for setting up the method. By going through all the phases in the design science approach, the methodology could be designed, tested and evaluated.

In the first phase, the identification of the problem and the motivation, an extended literature review showed, among other things, that most research into open data focuses on government organizations. From these investigated projects the most striking challenges have been identified to be included in the design of the method. These challenges within open data projects are in the technical, social, legal and economic areas. Analyzing the literature also confirmed that conducting a case study is a suitable method to test open data projects. All these elements were then included in the design of the ODIM. Because analyzing the value of open data projects at companies has to do with the design and organization of new systems at companies, it was investigated what a suitable design method was for the ODIM. Due to the iterative nature and previously proven functions, the design engineering approach has been chosen according to Dym and Little. This method was used to subsequently adjust it so that it is suitable for open data implementation projects at companies. The method, the artefact in the design science aproach of this study, follows different phases that companies can go through. By mapping the current system in detail, determining requirements of open data in the new system and analyzing for example the costs, revenues and use of the data by current users, a concept system design can be drawn up with a central role for open data. This design shows which datasets are suitable for opening up and what changes this entails within the business processes. By testing this new system with experts currently working with the system, the value of the open data in the new system can be determined and changes can be added. The results of the method for a company are ultimately insights into current systems and the value of open data in a newly designed system.

This open data implementation methodology was demonstrated in this study at the commercial company PostNL Data Solutions in the Netherlands. The new open data system and the ODIM have been validated through an expert panel workshop. The results were that open data was seen as a very valuable change compared to current business processes. The value of open data was primarily recognized in the economic, cultural and organizational area. According to the experts who participated in the workshop, opening the data ensures that innovation is stimulated and new and better products and services can be built. The transparency of the company and the accuracy of the data will also increase in this new system. Risks are that it can cost a lot of money and time to set up current IT systems for open data and that privacy can pose risks to users. To be able to apply this method to other companies as well, the benchmarks for value creation must be made more specific to companies. Value creation is very context dependent, which makes it difficult to apply the ODIM broadly. By means of the ODIM, it is possible to make clear how current data systems are structured at companies and determine which data sets are suitable for being opened up.

The conclusion of this research is that open data can create value at commercial companies by reorganizing current systems. The open data implementation method helps companies to gain insight into this and to determine the value of open data for their company. Further research is needed into the broad applicability of the method by examining multiple cases and into benchmarks for value creation. In addition, an implementation plan must also be developed in order to successfully build the developed system with open data.