We are in the midst of a new economic age, a complex competitive landscape defined largely by globalization and digitalization. That means that the utilization and production of knowledge and innovativeness have become critical to organizational survival. (Uhl-Bien – Marion - McKelvey, 2007). That development has had its impacts also to urban develop-ment. Urban development, performance and competitiveness are seen depending on the availability and quality of knowledge. (Caragliu - Del Bo – Nijkamp 2011.) Smart city has been in discussions and became fashionable especially after year 2010, but is still somewhat fuzzy or not clearly defined. Smart city has been used even to refer cities which do not have clear strategies or processes supporting that. (Dameri - Cocchia, 2013.) The smart city concept originated from that of the ‘information city’, but has now much broader and deeper scope. As in the case of City of Helsinki the term ‘ubiquitous’ was widely in the use in the early phase of the smart city concept. That term ubiquitous was derived from ‘ubiquitous computing’. And thus on that very early stage of smart city –concept development the dominant thinking was about defining services via integration of IT. (Lee - Phaal - Lee, 2013.) What has changed is the exponential rise in the volume of raw data available to us. Big data phenomena within urban context is growing exponentially among public sector organ-isations. There is no precise and exact definition of big data. There is no numerical or quantified definition for big data and it has be seen more as massive and typically very complex sets of information. The concept has referred to massive, voluminous amount of data and secondly to the analysis of that data. Despite of the variations of the definitions they all have at least one of the following assertions common: the massive size of the da-taset, the structural complexity of the data and the technologies are needed and used to analyze the datasets. The quantitative and computational techniques’ side of big data has over 40 years history, but what is now changing and creating the real revolution is how we are using big data. (Mayer-Schönberger & Cukier 2013, 6-9; Ward & Barker 2013; Barnes 2013.) As Michael Batty noted what is needed is a new theory, since data without theory is not sufficient (Batty, 2013). The theory need asks forth understanding of how to use analytics to improve e.g. public services. And thus it will also have a significant impact on how we collect, use and interpret data and how professionals and experts work. In a highly con-nected information society, learning and the process of acquiring new skills and knowledge are of fundamental importance and define whether we are active participants or passive observers in the digitalisation process. Importantly, it is not just about learning new skills but unlearning old ones. The challenge for us is to develop new adaptive com-munities and working methods and to ensure that people embrace change while maintain-ing and gradually transitioning away from old practices. (Laitinen et al., 2017.)