Summary

selection, result visualization, and result interpretation/action in the current Big Data research. The emphasis is on research associated with data pre-processing and analysis, driven by infrastructural-related topics.

Besides the description of dimensions and relevant topics, a comprehensive overview has been given by presenting a critical perspective on Big Data, targeting political, legal, ethical, and scientific issues.

In the following Chapter 3 the concept of maturity models and the contextual brackets of Design Science Research have been presented as a starting point for the subsequent maturity model development.

By comparing different maturity construction models, key elements of the model con-struction have been identified, namely Scope, Design model, Develop instrument and Implement and Exploit. The analysis of different maturity models in the data analysis context from fields such as Business Intelligence revealed that no existing model is solely focusing on Big Data. Furthermore, for those models that partly address Big Data rel-evant topics, a lack of theoretical foundation in terms of i) no underlying theoretical construction model and ii) a lack of evaluation and validation approaches could be iden-tified.

Based on the described characteristics of maturity models and the related construction models (Chapter3), Chapter4contains the development of the construction model used in this thesis.

In doing so, the construction approach by Becker et al. [2009] has been selected as a basis and enriched by several aspects from the construction model by De Bruin et al.

[2005]. As both models do not consider sufficiently i) the specialty of the object in focus (novel, yet undefined topic with a need for clarification) and ii) the emphasis of the model evaluation (ensuring that the model represents the practical understanding of maturity in the practical context) - described in Chapter 1 and 4, the developed construction model that is applied in this research, emphasizes the description of the maturity object in focus, Big Data, and the evaluation of the developed artefact, the maturity model. A two-step evaluation approach has been developed, evaluating both the construction model itself (evaluation against the identified research gap) as well as the resulting maturity model (evaluation against the real world).

The governing aspect for the construction approach has been the correspondence with the principles of design science research by Hevner et al. [2004] and the principles of

correct modelling [Becker et al.,1995], resulting in a sound theoretical foundation of the resulting construction model.

Chapter 5 contains the deployment of the developed construction model. Out of the four dimensions, identified during the literature review in Chapter 2, the dimensions Datahas been identified and complemented with the dimension Organizationas a basis for the model development. As a bottom-up approach has been used, in a first step, a questionnaire, containing measurements that target different capabilities of Big Data relevant topics, has been developed, discussed with industry experts, and pre-tested.

One finding of the developed questionnaire has been, that the identified measurements partly represent an enhancement of existing maturity models, e.g. from the field of BI. Both new topics e.g. the process for the identification and evaluation of new data sources, as well as new items for existing topics, e.g. the result processing have been identified as relevant in the field of Big Data.

The resulting questionnaire has been answered by 71 companies from different industries and sizes. In the next step, for each item the related item difficulty has been calculated, using a statistical approach from the field of the IRT. This difficulty has been used as a vehicle to determine the maturity of each item, assuming that the higher the calculated difficulty, the more mature the associated capability is expected to be. Based on the difficulty, the items have been subsequently assigned to six maturity levels using a ward clustering.

The resultinginitial model has been evaluated in a two-step approach. In a first step, it has been discussed regarding the correct assignment of the measurements to levels with the members of the focus group and the measurements have been reassigned accordingly.

As the second part of the evaluation, the resulting fitted model has been deployed at eleven companies and the resulting model-based maturity assessment has been compared with the maturity evaluation of an industry expert. Based on interviews with the fo-cus group members, reasons for potential differences between the model based maturity evaluation and the industry expert evaluation have been identified, incorporated and translated into the final model.

Following the description of the final model, the maintaining process has been presented.

With regard to the different needs, the execution of the maintaining has been described, both in an industry as well as research context.

After describing the course of research, the fulfilment of the overarching goal, the

• Development of a maturity model for the field of Big Data

and the subordinated goals

• Using a quantitative bottom-up approach for the model population and the

• Development of a model evaluation process,

defined in Chapter1, are discussed regarding their fulfilment.

1. Development of a maturity model

An indicator for the success of this research project is the fulfilment of the research goals. By developing the model as described in Chapter 5, the overarching goal of this thesis as stated in Chapter 1, has been accomplished. Contrary to the criticism of most of the existing maturity models, the presented model has been developed on a sound theoretical foundation, based on a design science oriented construction model, applying a quantitative bottom-up population approach. By deploying the model at eleven companies it could be shown that the constructed model can be used to address the problems described in Chapter 1; the companies’ uncertainty which capabilities should be developed, in order to improve the handling of Big Data. The evaluation of companies’ capabilities can be taken as a starting point for an improvement of abilities in the field of Big Data.

2. Using a quantitative bottom-up approach for the model population

The next goal, the testing in how far a quantitative approach can be applied in a field, which contains both novel and established aspects, could also be met. It could be shown that one challenge, when applying a quantitative bottom-up approach for the model construction for a novel topic like Big Data, is the identification of measurements. Due to the absence of existing maturity models for Big Data and the low number of relevant publications, the input of a focus group is needed in order to gain a sufficient practical relevance.

3. Development of model evaluation process

The development and application of a suited maturity model evaluation that is supposed to i) contribute to the theoretical foundation and ii) prove the practical relevance and applicability of the model as the third research goal could be accomplished as well.

Concerning the first part of the model evaluation, it could be shown that a quantitative approach can be applied for the assignment of items on different maturity levels in the developed model construction context. At the same time, the discussion of the initial model with the focus group members - as the first step of the evaluation against the real world - yet revealed the need for a further fitting.

Every item had to be reassigned by the industry experts by an average of 0.77 levels.

This low reassignment shows that although all items had to be reassigned, the applica-tion of the quantitative approach leads to suitable results. It could be shown that the extent of reassignment is not linked with the novelty of the topic in focus. Both items related to the established topic known from the field of BI, and novel topics underwent similar reassignments. Similarly, within the group of topics with a low/high extend of reassignment, both novel as well as established topics could been found.

The model could be successfully deployed in an industry context as the second part of the evaluation. In this context - the identification of companies, covering the whole range from maturity level one to maturity level six in order to achieve a full-range model evaluation - has proofed itself as demanding for a novel topic like Big Data.

Nonetheless, a high agreement between the model based evaluation and the expert’s evaluation of companies maturity could be found. This can be interpreted as an adequate representation of the practical understanding of maturity by the developed model.

The positive model evaluation gains in weight when looking at the different scopes of the two maturity evaluations. The model is focusing on the dimensions Organisation and Data, whereas the expert is evaluating the companies’ capabilities along all Big Data dimensions. Despite these differences, the comparison shows a high agreement between the model and the experts’ based assessment, indicating a broad coverage and relevance of the selected dimensions.