# A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
## 5 Discussion  
### Contributions to Theory  
Big data allows CNOs to increase their competitiveness by improving their strategy, management and business processes. Big data has recently emerged in CNOs and forces them to deal with different organizational, technological and human challenges. Ten big data frameworks have been proposed to deal with these issues in a satisfactory way. Based on the review of these frameworks, a metaframework has been developed. A metaframework is an abstraction of a framework, highlighting the properties of the framework itself. The initial metaframework identified six dimensions and several sub-dimensions addressing different important aspects that a proper big data framework for CNOs should fulfill. Then, during the development of the CNO-BD framework, it was noticed that although the integration of big data in the companies is recognized as a key success factor of a big data project [50], none of the existing frameworks consider the management implications in big data projects. Moreover, neither do they explain when and how to improve the CNO's strategy or business process using big data. As a result, big opportunities for business performance improvements can be lost. To deal with these issues, a new dimension (_Programmable Modules_) and some sub-dimensions are necessary to add to the initial metaframework. Therefore, the metaframework is the first contribution of this study. Now, it is possible to analyze the scope, features, strengths and weakness of existing and future frameworks for big data ecosystems development in CNOs, mapping them with the metaframework. The second contribution of this study is the analysis of the ten existing frameworks for big data ecosystems development in CNOs using the metaframework (Table 1). The main conclusion is that although existing frameworks consider several dimensions, none of them include all the dimensions and sub-dimensions that are needed to properly support practitioners. Therefore, by using only one of these frameworks some important questions are always missing. Academics interested in improving theses frameworks can see the in Table 1 the areas of improvement for every framework. The analysis of existing frameworks proved that they have some weakness. For this reason, there has been a need to develop a new framework structured in seven dimensions: the CNO-BD framework. This is the third contribution of this study. In its development, it has been taking into account one of major threats to the potential benefits of big data in the CNOs: A framework for big data ecosystems implementation in CNOs should not consider only technological aspects. This could imply:  
* obtaining unfavorable results such as gaps related to interoperability analysis of the organizational structure, business processes or services [51];
* inadequate guides for the use of the different big data techniques [52];
* a lack of ability to filter out irrelevant information to reduce unnecessary costs [53];
* suboptimal handling of data connections [52];
* a lack of contextual information [54];
* a lack of adaptation to approaches according to requirements, that is, a lack of intelligent processing of big data [54]. Considering this background, the proposed CNO-BD framework offers two main novelties and contributions: _First_, it covers all the dimensions and sub-dimensions that a proper framework for big data ecosystem development in CNOs should have, offering solutions for both essential technological issues such as the necessity of addressing interoperability [12] or the processing of heterogeneous data [23], and the linkage with CNO strategy and business process (see Figure 1). Therefore, it allows obtaining meaningful and organized knowledge from the CNO's participants big data to support CNO decision making. _Second_, unlike the existing frameworks that are developed for a specific CNO manifestation, the CNO-BD framework can be used by the different kinds of CNO's identified in the taxonomy developed by [10].