OPEN GOVERNMENT DATA AND VALUE CREATION
THE CASE OF NORTAL A/S
ROSKILDE UNIVERSITY Business Studies, Internship Report Spring 2017
SUPERVISON Kirsten Mogensen
AUTHOR Ninni Gustavsen / 49883 Illustration: toolinux.com Logo: Nortal.com ABSTRACT
This research project takes the starting point in the Estonian ICT market, with a particular focus on Open Government Data and value creation in private businesses. With Nortal as a case, the problem is explored through a thematic analysis of an interview with Oleg Schvaikovsky, Member of Board at Nortal. The findings are that value creation from OGD from Nortal’s point of view happens due to the use of particularly transparency and efficiency mechanisms with a focus on the entire ecosystem and not just implementing the IT. In conclusion, Nortal focuses a lot on value creation from Open Government Data by considering the surroundings and the ecosystem in which the technology will be implemented. Nortal makes use of both transparency mechanisms and efficiency mechanisms to create a seamless society. The findings of this project have contributed knowledge of value creation from Open Government Data in private businesses to a small academic field in development.
Keywords: Open Government Data, Nortal, private business, Estonia, value creation, mechanisms
1 TABLE OF CONTENTS
1.0 INTRODUCTION 4 1.2 PROBLEM STATEMENT 5 2.0 CONTEXTUALIZATION 6 2.1 DATA AND OPEN DATA 6 2.2 OGD AND EUROPE 7 2.3 OGD AND ESTONIA 8 2.4 Nortal AS 10 3.0 METHODOLOGY 11 3.1 ANALYTICAL DESIGN 11 3.2 LEVEL ONE: GATHERING EMPIRICAL DATA 12 3.3 LEVEL TWO: ANALYSIS OF THE EMPIRICAL DATA 13 3.3.1 CRITICAL REALISM 14 3.4 LIMITATIONS 14 4.0 OPEN GOVERNMENT AND VALUE CREATION THEORY 15 4.1 OPEN (GOVERNMENT) DATA 15 4.1.2 CHALLENGES FOR OPEN GOVERNMENT DATA 16 4.2 VALUE CREATION 16 4.2.1 TRADITIONAL VALUE CREATION THEORIES 16 4.2.2 VALUE AND OPEN GOVERNMENT DATA 17 4.3 MECHANISMS 18 4.3.1 THE OPEN GOVERNMENT DATA VALUE GENERATION FRAMEWORK 19 4.3.2 TRANSPARENCY MECHANISM 20 4.3.3 PARTICIPATION MECHANISM 21 4.3.4 EFFICIENCY MECHANISM 21 4.3.5 INNOVATION MECHANISM 21 4.3.6 LIMITATIONS 22 4.4 PART CONCLUSION 22 5.0 ANALYSIS 23 5.1 MECHANISMS 23 5.1.1 TRANSPARENCY MECHANISMS 24 5.1.2 PARTICIPATION MECHANISMS 25 5.1.3 EFFICIENCY MECHANISMS 26 5.1.4 INNIVATION MECHANISMS 27 5.2 VALUE ENABLING FACTORS 28 5.2.1 CHANGE MANAGEMENT 29 5.3 PART CONCLUSION 30 6.0 DISCUSSION 31 6.1 INTERNAL CHALLENGES 31 6.2 BALTIC DIGITAL SINGLE MARKET 34 6.3 PART CONCLUSION 34 7.0 CONCLUSION 36 8.0 BIBLIOGRAPHY 38
2 Appendixes Appendix 1: E-mail Appendix 2: Interview Guide Appendix 3: Interview, Nortal
Characters: 78.097 incl. Spaces / 32,5 standard pages
3 1.0 INTRODUCTION
The use of information and communication technologies (ICTs) on technological devices is such a common thing today that it is almost taken for granted, and therefore we rarely think about the production of data. Governments and government institutions produce and commission huge quantities of data, which can be useful in many different ways such as catalysts for commercial and civic innovation as well as in public administration (Wernberg 2016:3; Ministry of Economics and Communications 2013:5; Open Knowledge Foundation n.d.). This data produced or commissioned by the government is what is referred to as Open Government Data (OGD). OGD can be viewed as a shared resource, owned by the public but provided by the government and its institutions (Jetzek 2013:48). OGD can be used to help the public understand the work of the government and to hold governments accountable. Additionally, it can help increase government transparency, lead to an increase in public participation, and perhaps even innovative solutions can be created from the use of OGD (Ubaldi 2013:4). Increased focus on the general use of social networks and increased availability of OGD have led to a change from a clear separation between the market and the state into a market with a rise in data-driven innovations, connecting the two previous dichotomous units. This data not only brings markets together, but also connects the world and is thereby forcing us to rethink the way we use OGD and the market (Jetzek 2013:47). These developments have resulted in the emergence of innovations such as Skype, Taxify, AirBnB, Google Maps, eBoks and many more. But how are businesses able to use OGD? How is OGD used in practice? How does OGD create value for companies? And which mechanisms create an incentive to use OGD?
The starting point of this project is an interest in understanding how OGD is used in private organizations for innovation purposes. During my internship at the Royal Danish Embassy in Tallinn I was introduced to the Estonian ICT market and found an interest in the combination of OGD and private businesses. Open Data is becoming more and more important as a resource for businesses, however it is difficult to understand the value of this data. The use of OGD in businesses is relatively new and for this reason the amount of literature focusing on OGD and value creation is sparse (Granickas 2013). This project focuses on OGD in Estonia and how Estonian companies use OGD, and will end with a discussion of the possibilities for OGD from a macro-regional approach in the Baltic Sea
4 region. In this project, I have chosen Nortal as a case study in order to analyse how OGD creates value. Nortal is the largest Estonian IT Company and has a great deal of experience in working with open data for many different jobs worldwide (Nortal 2017). Nortal is a consultancy company delivering platforms for both private and government institutions allowing the institutions to make full use of the potential of OGD. Nortal is therefore familiar with private and public sectors work with OGD. Estonia is often described as a leading e-country, but there are still bumps on the road (e-Estonia). The technical interoperability between private and public sector is lacking, which hinders (among other things) economic productivity. The Digital Agenda 2020 for Estonia focuses on “(...) creating an environment that facilitates the use of ICT and the development of smart solutions in Estonia in general” (Ministry of Economic Affairs and Communications 2013:2). It is a strategy that through various measures and actions hopes to “(...) increase the economic competitiveness, the well-being of people and the efficiency of public administration” (Ibid). There is a great focus on ICT and the abilities to optimise current business processes as well as to develop new innovative products and services (Ibid:5).
There are many ways of working with OGD and several ways of approaching a concept as complex as open data. Through the use of a case study, I will explore Nortal’s use of OGD and analyse which mechanisms generate value for the company and its clients. This will be answered through the application of empirical data collected in the form of a semi- structured interview with Oleg Shvaikovsky, Member of Board, combined with secondary sources. This approach will allow for a discussion of the pros and cons of working with OGD now and in the future as well as discuss the opportunities and challenges in relation to a macro-regional approach to OGD.
Based on the choices and considerations presented above, the following problem statement is to be answered:
1.2 PROBLEM STATEMENT How can we understand the use of Open Government Data from Nortal’s point of view and how is value created for private businesses?
5 2.0 CONTEXTUALIZATION
This chapter will provide the reader with an introduction to Open Government Data (OGD) in general as well as a country specific description of the Estonian approach to OGD. The case study for this project, Nortal, is presented with respect to the historical context as well as the current situation and other relevant data. These two topics are introduced and described in order to contextualize the problem this project is dealing with.
2.1 DATA AND OPEN DATA There are several recurring terms when talking about data: Big Data, Open Data and Open Government Data. Big Data is extremely large and often complicated datasets. The gathering of such large datasets has become possible due to the increasing use of devices collecting and sharing data, plus an increase in storage and processing capacities. Big Data can take the form of many different data types e.g. texts, numeric data sets, images and videos (Bertot et al. 2014:6). Many governments are using big data to identify and analyse problems but also to make data available. Data.gov is one of many examples of open government initiatives building on Big Data. Open Data is data that is freely available, accessible, can be republished and exists beyond the limit of technical restrictions such as copyright, patents or similar barriers that might hinder the openness of data (Bertot et al. 2014; Open Knowledge Foundation, 2012; Jetzek et al 2013, 102). Therefore OGD relates to government data that is open to the public as well as private agencies. According to OECD, OGD is a philosophy promoting “(...) transparency, accountability and value creation by making government data public to all” (OECD 2017). Government data can be anything from traffic, weather and geography to statistics and data on businesses and public sector budgeting (M. Janssen et al. 2012). This opens up an interesting subset of open data allowing for data-driven innovation. Open data can lead to businesses creating new innovative products and services for individuals as well as communities or governments (Ministry of Economic and Communications 2013).
The difference and similarities between the three different types of data are illustrated in below
6
Figure 1 Source: Joen Gurin (2014)
Since the middle of the twenty first century, there has been a focus on opening up government data due to the idea of transparency and public accountability. Today, a change in the justification can be found and now OGD initiatives are also driven by innovation, flexibility, and efficiency are part of the rationalization (Robinson and Yu 2012:193). The developments in Big Data, Open Data and OGD indicates that the amount of data is only increasing and that more and more governments are using big data to identify and analyse problems, but also to make data publicly available through OGD initiatives.
2.2 OGD AND EUROPE In the European Union, the initiatives regarding OGD stems back to the Directive on re- use of public sector information (PSI) (Directive 2003/98/EC) that was put into effect 1 December 2003. The directive focused on the economic aspect of reusing information and encourages Member States to publish as much PSI as possible. The ePSIplatform was created as a result of the directive (www.epsiplatform.eu) promoting PSI and Open Data re-use (Cox and Alemanno 2003). In 2013 the directive was revised (Directive 2013/37/EL) and by 2015 the member states were expected to have implemented the revised rules into national law. The revisions included limiting the fees that can be charged by the public authorities at the marginal cost as a rule, making the machine-readable format the norm (Schulz and Shatter 2013). To promote OGD, the European Union has set
7 up the European Union Open Data Portal (www.data.europa.eu) providing information concerning the institutions and other bodies of the EU as well as the European Data Portal both aiming at improving accessibility and increase the value of Open Data (European Data Portal). The portal builds on data from all over Europe. Making data available is affected by another European directive - the Data Protection Directive (95/46/EC). This directive explains the basics of data protection Member States have to transpose into national legislation. The directive focuses on the protection of individuals when it comes to processing personal data and the free movement of data. If a database contains personal data or data related to an individual, it is at the risk of the publisher to be convicted on the basis of an offence against data privacy (Haensch and Serna 1995). In 2016 a new regulation, the GDPR, repealed the Directive 95/46/EC, and shall apply from 25 May 2018 (European Commission B n.d). The new comprehensive regulation focuses on giving the citizen back the control of their personal data. At the same time, it wishes to simplify the regulatory environment for businesses (Schulz and Hennis- Plasschaert 2016).
2.3 OGD AND ESTONIA The history of Estonia is a turbulent one with many different rulers. In 1991 Estonia became a free country again liberating them from the Soviet Union. A completely new government was elected and there were a great focus on digital technologies. Estonia was the first in the world to adopt complicated e-solutions such as personalised ID cards, internet voting, and digital signature, and today they are famous for their e-residency where foreigners with a few simple steps can sign up online and become e-residents enabling them to start businesses in Estonia (Liin et al. 2012:45). By the early 2000’s, IT had become an integral part of Estonia’s culture, economy and international reputation.
In Estonia, the X-Road is considered the backbone of digital developments. It consists of a database containing data about Estonian citizens. X-Road was initially invented as a data- exchanging layer allowing different public institutions and their IT-systems to safely communicate with each other. Today private institutions and enterprises are also able to connect, improving and easing the data exchange between all members (Republic of Estonia, Information System Authority A 2017). If private companies would want to use data, they make a request via X-Road where it will be send to the target institution encrypted and formatted. Every interaction is logged and only authorized persons can
8 access the data (Republic of Estonia, Information System Authority B 2017). Citizens can see exactly who has been accessing their data and why, which helps to create an environment of trust. In an interview, Taavi Kotka, Estonian government’s chief of information officer states that “98 per cent of government interactions in the country are online” (Reynolds 2016). The X-Road means that administrative processes are very fast, company registration is very easy and financial data can be exchanged electronically at the request of the citizen (Hofheinz and Osimo 2017:23).
Estonia brands itself as being very active in the field of Open Data. Most OGD is to be found on Statistics Estonia (www.stat.ee) or the Open Data Portal of Estonia (www.opendata.riik.ee) containing public sector data available for use and reuse. Such a portal is often a very important step in gaining access to public sector information (Open Data Portal of Estonia). Furthermore, in November 2013 the Government approved the Digital Agenda 2020 for Estonia. Both the Open Data Portal and Estonia's long vision focus on promoting ICT developments shows that ICT and OGD is important for Estonia both in order to be a transparent and accountable nation as well as in order to allow for innovative ideas to spur. Estonia ranks number 9 in the Digital Economy and Society Index 2017 (DESI). The key challenge mentioned is the digitalization of private companies, indicating that it is more difficult to turn huge amounts of data like OGD into value (European Commission C).
In summary, Estonia has put a lot of emphasis on e-government and becoming a digital forerunner, both in order to service all its citizens spread out in rural areas as well as increase the ease of doing business in Estonia. All this has lead to the reputation of Estonia as a digital state promoting e-governance however the usage and implementation of OGD in private organizations still have room for improvement. This raises many questions in relation to Open Data and OGD. Is it too difficult to work with? Does companies not find OGD of any value? How could the government and the commercial sector better work together?
9 2.4 Nortal AS Nortal is a privately owned international ICT and business consultancy company founded in 2000 under the name Webmedia. In 2011, Webmedia purchased the Finnish Software Developer CCC, making them the largest software development leader in the Baltics. At the same time, they rebranded themselves as Nortal, which stands for Nordic Talent. Nortal provides both public and private sector clients with high-end tailor-made data-driven business transformations.
Nortal has planned and implemented more than 40 % of Estonia’s e-services and one of their aims is to promote a digital seamless society. They do this by promoting three pillars; enterprise, e-health, and e-government. They combine a strategic approach with data- driven technology in order to optimize and simplify. Some of the projects they have planned and executed in Estonia is the Employment Information System (EMPIS) focusing on automation of critical business processes. They used the X-Road by implementing all national databases and registers in EMPIS allowing for automatic pre-filled forms, reminders and notifications is automatically created and so forth. Closed government data and OGD is used to crosscheck all data, and the pre-filled forms build on the same data. In the Estonian system, you only have to enter your data once and it will be integrated into all the different government platforms, making it easy and reliable for both the government services and the citizen (Nortal C).
Nortal helps both public and private sectors with information managing systems. Within the private sector, they work with clients such as banks, telecommunications companies and insurance companies. As an example, the Finnish company Neste (One of Nortal’s main customers in the private sector) were introduced to a new logistics system improving the accuracy and speed of information exchange processes (Saarman 2017). Nortal just expanded to the United States, providing services for one of the largest telecommunication companies, T-Mobile (Shavikov 2017:39-41). Here, they are creating an e-commerce platform based on data analysis aiming to find particular products the user would be likely to buy (Pau 2017).
In summary, Nortal helps both private and government organizations to become more digitalized and teaches them how to use and/or improve their usage of OGD. Nortal therefore has a unique insight into what drives organizations to use open government data
10 and how it creates value for them. Nortal is promoting the usage and spread of OGD in a safe way where both the citizen benefits as well as other institutions and organizations.
3.0 METHODOLOGY
This chapter will present the methodological considerations and give insight to the methodological choices that have been made throughout the process in order to answer the problem statement. The methodology related to the empirical data is divided into two levels: The first level presents the gathering of empirical data and the second level focuses on the analytical approach taken to examine the empirical data. The analysis is carried out with the assumptions of critical realism in mind, helping to uncover the mechanisms that can explain what enables value generation from OGD. The limitations related to the project are described in the final section.
3.1 ANALYTICAL DESIGN At my internship at the Royal Danish Embassy in Tallinn, I have been working closely with public and private Danish organisations that all seem to have an interest in how Estonia has become a forerunner within IT. As stated, much of Estonia's success in becoming an IT-nation stems from the willingness of the government to open up their data and platforms and invite others to use it. My internship has allowed me to gain insights, which would otherwise have been very difficult to obtain.
The analysis builds on a selection of value generation theories and mechanism theory, both selected through a review of the academic literature. A semi-structured interview has been conducted with Oleg Shvaikovsky from Nortal AS. The analysis is structured around the open government data value generation framework presented by Thorhildur Jetzek et al. (2014). Through an analysis of the collected empirical data combined with academic texts as well as information published by the Estonian government and others, the model functions as a tool for understanding value creation when using OGD.
11 3.2 LEVEL ONE: GATHERING EMPIRICAL DATA The analysis is primarily done on the basis of an interview conducted with Oleg Shvaikovsky from Nortal AS. The interview allows for a deeper insight and understanding of Nortal’s processes behind value creation from OGD. The interview is structured around Steinar Kvale and Svend Brinkmann’s theories about qualitative interviews. Here, qualitative interviews are seen as “an active process where both the interviewer and the interviewee produce knowledge through their relation” (Kvale and Brinkmann 2009:34).
Prior to approaching Nortal, I did a reflective search of Estonian companies that possibly could be working with Open Government Data one way or another. Finding companies that had the time and willingness to participate turned out to be more challenging than expected. I found Nortal particularly interesting since they have experience in helping both governmental and private organizations, allowing for an interesting understanding of the value of OGD from many different perspectives.
The interview took place at the Royal Danish Embassy in Tallinn and was conducted in English, since this is a common language for the researcher and Mr. Shvaikovsky. It is necessary to take into consideration that this might have limited the natural flow of the conversation, since it can have caused both the interviewee and the interviewer to focus on finding the right words and hence not answering the question in a straightforward manner. Before the interview, Mr. Shvaikovsky was briefly informed about the content and the purpose hereof (Appendix 1). He was informed that there were no right or wrong answers and steps were taken to create a trusting and open environment, although I am aware that he acts in his own and Nortal’s interest in general, and therefore might omit information. Mr. Shvaikovsky has been working in Nortal for the past five years. He is a partner and a member of the board. Before joining Nortal, he has been working in the ICT sector for about 25 years. His expertise lies within e-identity, telecom, and Data science (World e-ID and Cybersecurity).
A semi-structured interview allows for a flexible structure, and for the interviewee’s understanding and point of view helping to create a natural flow and through that gain deeper insight as well as with more detailed answers than a structured interview (Bryman
12 2012:470). This structure still ensures that a number of pre-selected topics will be touched upon.
An interview guide was conducted as a tool to use throughout the interview (Appendix 2). The interview guide was not controlling the conversation and the conversation at times deviated from the guide, but it still gave a direction for the conversation and topics, even though questions were asked in different wordings and orders than intended (Bryman 2012:471). There was an emphasis on not asking closed or leading questions. As emphasized by Kvale, the interviewer have to be a good listener and be sensitive to wordings and show interest in order to ask relevant questions as well as try to understand where the interviewer comes from (Kvale in Bryman 2012:473).
3.3 LEVEL TWO: ANALYSIS OF THE EMPIRICAL DATA The aim of the analysis is to draw out meanings and knowledge from the interview and thereby exploring the mechanisms behind and value created when using OGD. This will be structured and conceptualized through the understandings provided by the theories. The interview will thereby function as the main source of empirical data throughout the analysis.
The interview and the choice of theory were approximately settled at the same time, however the interview has led me to specify the theory chosen. The interview contains questions regarding OGD, value creation and crossing borders with OGD. When transcribing the interview, it became clear that a few themes that I had not considered were mentioned several times: seamless society, management and interdisciplinarity.
The identification of new themes in the empirical data affected the direction for the analysis. The themes found in the data generally fit into the framework, and therefore the framework acts as the foundation for the analysis, while additional themes identified are analysed and conceptualised in relation to the framework. In the analysis, I make use of selected quotes highlighting how the theories can be applied to the empirical data. The quotes shed light on how OGD is used, how value is created. The quotes are selected on qualitative and subjective foundation where I have selected quotes identified as relevant.
13 3.3.1 CRITICAL REALISM Critical realism acts as the scientific approach in the project. In essence, it acknowledges the role of subjective knowledge as well as the existence of independent structures influencing this knowledge (Bhaskar 1997). The analysis is based on the open government data value creation framework extended with value creation theory and themes identified in the empirical data. Each archetype of OGD value generation mechanism is analysed on the framework of OGD and value creation and is supported with quotes selected on a qualitative basis. These steps will lead to an answer of the problem statement and hence conceptualize the mechanisms creating an incentive for the use of OGD as well as the value it creates.
3.4 LIMITATIONS The methodological choices chosen in this project all have implications for the selection of theory, the analytical framework, the discussion and following reflections and hence the outcome of the project, for which reason it is important to be aware of the decisions made and how they affect the findings.
To start off, one should always keep the aspect of subjectivity in mind. The empirical data primarily consists of the analytical findings building on statements and knowledge selected by the researcher alone, therefore my implicit assumptions might have biased the outcome. As Critical Realism supports, individuals are affected by their own subjective evaluation and in this way my evaluation of the relevant and irrelevant statements might have unintentionally affected the framing, context and communication.
Secondly I could have gone forward with a large-N analysis focusing on quantitative data in order to show the mechanisms and value related to OGD, however as I wished to obtain an in-depth knowledge, I went for the case study and small-N analysis. Small-N studies fit into a cause-off-effects template and thereby explaining the outcome of a single or few cases (Seawright and Geering 2008). Furthermore the choice of case study as the method is based on the wish to explore and identify causal mechanisms and several Critical Realism researchers have identified case study as the best way to do this (Ackroyd 2010; Easton 2010; Miles and Huberman 1994; Mingers 2004b; Wynn and Williams 2012).
Finally, when conducting the research of OGD and value creation in private businesses, I have come across a lack of academic literature in the sense that it is still a relatively new
14 field of research. I have however found theories that I found fitting and adjusted them to my angle and it has given me the opportunity to contribute a small piece of knowledge to the field. Nortal has however an immense influence on my findings, for which reason this research will not be able to generalize on its conclusions. Additionally, the single-case study nature of the project further complicates the generalization capability of the findings. Thus, further research is necessary to establish universality in the findings.
In summary, critical realism acts as the scientific approach and the analysis is build on the Open Government Data Value Generation Framework and extended with additional themes identified in the empirical data. Each theme is analysed within the framework and is supported by quotes selected on a qualitative basis. These steps are taken in order to answer the problem statement, and hence conceptualize how OGD can create value for Nortal.
4.0 OPEN GOVERNMENT AND VALUE CREATION THEORY
In this section, the most important approaches and trends within the academic literature relevant for this project will be identified and categorized. The first step is a focus on OGD theories presented through a historical overview of the approaches and conceptualizations of open data. Next off the theories behind value creation in relation to OGD will be explained in order to develop a greater understanding of the mechanisms helping to generate such a value. This leads up to an exploration of the concept of mechanisms and the underlying theories.
4.1 OPEN (GOVERNMENT) DATA Open Data Theories in general are not rooted in a single discipline or school of thought, which also allows for several understandings of Open Government Data (OGD). Throughout the 21st century a more common understanding of OGD has developed and by the end of 2007 an open government working group drafted a set of principles defining OGD. The data must be complete, primary, timely, accessible, machine-processable, non- discriminatory, non-proprietary, and license free (Public.resource.org 2007). Seven additional principles have since then been added stating that the data must be online and free, permanent, trusted, assumed to be open, documented, safe to open, and designed with public input (Opengovdata.org; Sunlight Foundation 2010). Today, these principles
15 are widely accepted and many governments have picked up on them as a guideline for their OGD initiatives (Ubaldi 2013:8).
4.1.2 CHALLENGES FOR OPEN GOVERNMENT DATA Governments are motivated to opening up data due to various reasons, many of them already mentioned, such as increased transparency, citizen participation, co-innovation between public and private sector and economic benefits (Yanf and Kankanhalli 2013; Okamoto 2016; Ubaldi 2013; Jetzek et al. 2014). However there are also limitations of OGD. The limitations can be divided into two categories: legal and technical barriers. The first concerns questions regarding privacy, security and other legal implications as well as, barriers in relation to releasing data, affecting what becomes available (Jansen et al. 2012; Scassa 2014; Okamoto 2016). The latter refers to technical barriers - OGD can be difficult to use without prior experiences with downloading and managing data (Janssen et al. 2012). Data must be standardized in order for it to make sense when used (Jetzek et al. 2014). Jetzek et al. (2014) have further identified factors challenging OGD; “Closed or inaccessible datasets, (...) lack of validity, completeness and exhaustiveness of datasets, (...) too fragmented and disparate open data community” (2014:103).
4.2 VALUE CREATION Value creation is often perceived as profit and often value creation is related to a subjective experiences. This section will mainly focus on value creation theories in relation to OGD. This is done in order to provide theoretical knowledge of the context related to the problem area found in this project. I start out with a review of the two main theories related to value creation; Porter's framework for value creation and Resource Based theory. Afterwards the connection between OGD and value creation will be investigated in order to uncover the mechanisms turning data into value.
4.2.1 TRADITIONAL VALUE CREATION THEORIES Porter’s framework for analysing value creation at firm-level focuses on a total of nine activities affecting a firm’s value chain. The primary activities are: Inbound logistics, Operations – transforming inputs into a final product, outbound logistics, Marketing and sales, Service – and aftercare. Porter is interested in finding the strategically important activities and hence understands their impact on cost and value (Porter 1985:39). Critiques find Porter’s framework only well-suited for traditional manufacturing companies, however other industries where it is more difficult to define what is produced and what is
16 shipped (like insurance companies and banks) a well as technology developing industries does not fit the framework (Stabell and Fjeldstad 1998:414). Resource based theory (RBT) likewise finds Porter’s framework diffuse. RBT focuses on how different resources can be at advantages for organizations, if competitors are not able to imitate them, making the resources valuable, rare, and unique (Amit & Schoemaker, 1993; Black & Boal, 1994; Mahoney & Pandian, 1992, Bowman & Ambrosini, 2000). In general, there has been a great focus on protecting the resources of value from rivalling firms (Bowman & Ambrosini 2000:2). Resources are in general assumed to valuable when they are put in relation to a specific task; e.g. when the resource is used to exploit opportunities (Barney 1991:105), resources are valuable when they are a means to making happy customers (Bogner and Thomas 1994; Verdin and Williamson 1994), or when resources can be used to implement strategies improving efficiency (Barney 1991:106).
4.2.2 VALUE AND OPEN GOVERNMENT DATA There is a distinction between public and private value generation; private value is created when goods and services end up producing a profit. When talking about public value, value is related to governmentally produced benefits. The value of governmentally- produced benefits can furthermore be divided into two; value is derived from the direct usefulness of the benefits, secondly, value is derived from the fairness and equitability of their production and distribution, and meets citizens’ requirements for “properly ordered and productive public institutions” (Harrison et al. 2011:90; Moore 1995:53). In order to clarify which sort of value one is speaking of, Bowman and Ambrosini (2000:2) suggests differentiating between use value and exchange value. Use value is the value a product gives customers through their use of it – use value is related to the customers needs and is therefore a subjective assessment, however use value applies to all purchases – not just the ones done by the end consumer. Use value is added when the resource is combined with existing resources in an organization. Exchange value is referring to the price and value is then generated when an actor manages to capture part of the portion of the value (Ibid, Jetzek 2013:49). Moreover, there is an economic value referring to the worth of a good set by the market reflecting Porter and Millar’s view. On the other hand, social value refers to the value added to individuals or societies as a whole (Porter and Millar 1985; Jetzek 2013:49). OGD is as stated a resource that can be reused hence the product is not solely available for exchange once – the data has the ability to generate shared value (Porter and Kramer, 2011). Bowman and Abrosini highlights the fact that
17 value is not added when new resources are added to the organization. The resources need to be worked on, transformed, and activated before they start to cast off any value (2002:5).
Many researchers have pointed out various factors hindering value generation from data. According to Jetzek, Avital and Bjørn-Andersen, the most common barriers are: “a) closed or inaccessible datasets, b) lack of comprehensive data policies, c) lack of validity, completeness and exhaustiveness of datasets, d) insufficient metadata, e) lack of consistency in cross-border access regimes, f) lack of motivation within public sector, g) lack of technical and semantic interoperability, h) lack of technical ability within public and private sectors, i) the digital divide and j) too fragmented and disparate open data community” (2012:103). Following Jetzek, Avital and Bjørn-Andersen recommendations in relation to the complex causal relationship between OGD and value, the investigations of the causal causes should seek to analyse how different key mechanisms come into play and from that analyse which factors can lead to a successful outcome.
In summary, when talking about value creation, classical theories like Porter’s framework focuses on value understood in money terms in this research however value does not equal money but more an incentive. There are many understandings of value and value generation i.e. private and public value generation, use value and exchange value, economic value and social value. Which forms of value generation takes place from Nortal’s perspective when working with clients will be investigated in section 5.0.
4.3 MECHANISMS Mechanisms are a term well known within sense-making theories. The theory of mechanisms goes hand in hand with critical realism (CR), in the sense that CR suggests that events observed in the real world can be used to uncover underlying mechanisms arising from an interplay of micro-level structures (decisions and actions), which all has the potential to influence the existing situation - leading mechanisms to help explain how value can be generated from OGD. For this paper, I am looking for mechanisms that come into play when OGD is used to generate value. This could be legal frameworks, how data is disseminated, human resources, etc.
Mechanisms refer to either causal powers or tendencies (Fleetwood 2004; Smith 2006; Wynn and Williams 2012). Causal powers are what lead individuals to a certain action
18 over others. There are many aspects within these powers, and not all aspects might come to play at the same time, however they are always present one way or another (Fleetwood 2005:46). Causal powers is used when talking about possibilities, tendencies and is used to describe the typical characteristics of things (Bhaskar 1997:230). This does not mean that because a certain pattern is a tendency then it is expected; it just means that it is a possible course of action. Individuals and groups are all components of structures and in these structures events occur. Based on their thoughts and beliefs, individuals and groups carry causal powers explaining [LP28] of how actions are linked to consequences (Bhaskar 1998). Based on Bhaskar's study (1998), Wynn and Williams explain that, “(...) an actor’s beliefs or reasons that motivate intentional behaviors correspond to a tendency to act in certain ways.” and for that reason “CR views an actor’s reasons as the generative mechanisms which are the cause of a given action” (2012:791). Each action might trigger subsequent mechanisms within other places of the structure and therefore the result might not be as expected or intended.
4.3.1 THE OPEN GOVERNMENT DATA VALUE GENERATION FRAMEWORK Building on Harrison et al. (2012) and Jetzek et al. (2013) and their view of mechanisms, the analysis will be structured around four general mechanisms helping to highlight how the use of OGD can lead to value generation. As previously stated, there are primarily two ideologies behind most OGD initiatives; the efficiency and innovation or the reuse of data perspective and on the hand the open government perspective focusing on transparency and active citizen participation. The first ideology is highly connected with a focus on the economic value of open government data (Jansen 2011), while the latter directs its attention towards how OGD can help generate social value (Harrison et al. 2012; 85).
Jetzek et al. has developed a matrix presenting the main mechanisms to how OGD can be used to create value.
19
Figure 2 Source: Jetzek et al. 2014
The horizontal line shows to what extent the external stakeholders are able to generate value from the data. The horizontal line spans between the public sector on the right and the private sector to the left. The private sector seeks to generate what Jetzek et al. calls first-hand value. The private sector is represented in the column to the right, while the column on the left represents the public sector as the more active stakeholder in value generation. The vertical line shows to which extent the OGD initiative is focused on creating social value (Jetzek et al. 2014:105). The four types of mechanisms are: Transparency mechanism, participation mechanism, efficiency mechanism, and innovation mechanism. The mechanisms are each different and all requires different tools to implement however they are not mutually exclusive.
4.3.2 TRANSPARENCY MECHANISM The emphasis is on reducing information asymmetry, ensuring equal access to data and thereby limit corruption (understood as government misuse of public power for private interest and benefits). Improving information asymmetry will lead to a more equitable allocation of resources and thereby the possibilities of more social and economic value. Opening up data is not in itself a guarantee for a transparent government as there are still ways to hide information the government has no interest in sharing. Furthermore if the
20 users of OGD have no knowledge of how to use it nor the ability to process the information, then the incentive behind opening up data disappears. Hence one cannot conclude that opening up government data automatically leads to a transparent government, however it is a step in the right direction (Harrison et al. 2012; 87; Jetzek 2013:52; Jetzek 2014:106).
4.3.3 PARTICIPATION MECHANISM Value is created when openness and sharing joins forces and leads to contributions of ideas and active citizen participation is highly encouraged. For citizens to actively be involved in democracy, they first of all need access to information. Followed by this, the contributions of the citizens can lead to new ideas/new data (Jetzek et al. 2013:51). The thought behind participation is once again to equalize information asymmetry in order to lower the barriers to participate for the ones who otherwise are willing but unable to do so (Axelsson et al. 2010). Public participation and including a diversity of citizens’ voices is a way to improve social equity. Moreover the participation mechanisms have potential to lead to decision makers making better-informed decisions towards desirable policies (Harrison et al 2012:87).
4.3.4 EFFICIENCY MECHANISM Here there is an emphasis on the economic perspective for both private and public sector and the re-use of data. One of the tools is trying to reduce transaction costs as a way to minimize waste that could possible lead to both direct and indirect cost-savings. When talking about OGD, the transaction costs for instance is the time spent on locating data, archiving the same data in different places, and administrative bureaucracy. As mentioned, the strategy behind this mechanism is related to the vision of increasing efficiency. When focusing on governments the wish to lower transaction costs can possibly also be motivated by the wish to generate shared value, i.e. by creating better and more effective public services (Harrison et al. 2012:90; Jetzek et al 2013:51; Jetzek et al. 2014:107).
4.3.5 INNOVATION MECHANISM Innovation mechanisms will lead to the generation of new ideas, products, processes, and methods (OECD 2005). Following Schumpeter's economic theory, innovation is the source of value creation. Innovation leads to new production methods, products and services and in the end this can lead to a transformation of the industries and markets and thereby increase value (Schumpeter 1934). Technological developments helps businesses and
21 institutions collecting, managing and processing data and subsequently innovations follow, leading to value creation (McKinsey & Company 2011). Social and business innovation can overlap in the sense that business innovations can lead to social value creation as seen within e-health (McKinsey & Company 2011; Jetzek et al. 2014;107).
4.3.6 LIMITATIONS When talking about mechanisms, it is important to keep in mind that mechanisms are not only related to human actors. Social structures, physical objects, and technological artifacts such as software applications are all sources of emergent powers that along with actors’ beliefs exert causal influence and may be appropriate to examine (Wynn & Williams 2012:792). Furthermore it is important to bear in mind, that there are many possible sets of mechanisms producing an outcome due to the fact that we are unable to see every aspect of the underlying structure within an open system in which mechanisms are situated (Ibid.). For these reasons, the mechanisms referred to in this analysis might not make up all the mechanisms existing.
It can be concluded that transparency mechanisms, participation mechanisms, efficiency mechanisms, and innovation mechanisms all help describe how value is generated from OGD. Transparency mechanisms want to improve visibility and transparency of an organization, participation mechanisms hope to engage external stakeholders, efficiency mechanisms focus on improving and optimizing processes, while innovation mechanisms lead to idea generation and perhaps new products and services.
4.4 PART CONCLUSION To sum it all up, the academic talk about OGD is still developing, and Estonia proves as an example of how actions have been taken towards opening up data as seen with the online portals and the X-Road. The usage and implementation of OGD in private enterprises still have room for improvements. When talking about value creation from OGD, there are many understandings to take into consideration - is it i.e. economic value or social value produced? In order to help answer this question, the four archetypical mechanisms presented by Jetzek et al. are introduced. The following section will present the analysis using the theory mentioned.
22 5.0 ANALYSIS
This section aims at exploring the connection between open government data (OGD) and value generation. The theories will help to understand the general mechanisms behind the value creating taking place from Nortal’s perspective. The value creating theories will be applied throughout the analysis to the empirical data. Through a selection of relevant quotes from the interview, supported by data from secondary sources, the analysis will shed light on the kind of mechanisms that have become evident, when creating value of OGD in private businesses. It is no secret that part of what drives most businesses is the aim of generating an economic value, however what is behind the incentive to use Open Government Data and Open Data, how does the data become of value? The analysis will be divided into two parts, the first will be based on the framework of OGD value generation and the second will identify the factors having enabled value generation from OGD from Nortal’s perspective.
5.1 MECHANISMS The main focus of this project is to explain how value is generated from OGD, and if we therefore go back to figure 2, in the case of Nortal, it can be argued that stakeholders outside of the public sector mainly generate value, while the data however comes from the public as well as private sector. The main value generation therefore happens when data- using organizations (be they governmental or private institutions) need to learn how to manage the data or needs to optimize their utilization. For Nortal value is also created when they can help organizations that are not using data processes in an optimal manner, Nortal can thereby sharpen the client's product, which in the end makes things easier for the end-consumer. With Nortal working with both governments and private organizations the horizontal line representing the external stakeholder’s role in value generation spans from low to high. When working with governments - depending on the assignment and visions behind it - the public sector is the active stakeholder in value generation. Placing the external stakeholder’s role in value creation at a low. On the other hand, when working with private sector, the external stakeholder’s role in value generation is high, placing it to the far right of the matrix. When looking at the vertical line, Nortal once again places them in the middle. Based on the interview and articles collected, it is understood that Nortal by and large makes use of the same procedure no matter if they are working with public or private sectors - they focus not just on the technical part but on the surrounding facts as well (Interview 146-147). In both cases, Nortal creates what
23 Shvaikovsky calls a universal language and they are dealing with transaction costs in both private and government organizations (Interview 285; 146-147). Based on this observation, the following analysis will use examples of Nortal’s work with both sectors when describing the procedures used. As described above, there is however a difference in incentives behind OGD for private and government institutions.
Nortal is a privately owned company they do not represent a purely social organization and their wish to create a seamless society, in their vision making life easier for everyone involved shows a focus on generating social value.
5.1.1 TRANSPARENCY MECHANISMS Nortal and the products and services they offer show a commitment, through open data and OGD, to enhance transparency and accountability by improving services. Nortal is a keen promoter of good governance through the use of e-government. They want to reduce transaction costs, reducing waste and increasing transparency and overall creating a more efficient and seamless society. With Nortal’s great focus on good governance and creating a seamless society, transparency is a means towards the goal.[LP35]
From Nortal’s perspective, it can be argued that transparency mechanisms are what creates value for government institutions. Their work with the Estonian government has helped improve the transparency and with the connection to the X-Road. Nortal however when working with private businesses also sees the many opportunities in working with data - be that OGD or open data, and therefore supports the usage of open data. Nortal sees a wish for both government and public organizations to create a universal language where all the departments can speak the same language and make use of each other's data, without interfering in the daily life of the other which is what the X-Road is doing to a large extend in Estonia (Interview 289-297). As stated, these mechanisms are trying to improve visibility and transparency. As Mr. Shvaikovsky stated, the X-Road is free of charge, and he thinks that is a smart move of the government. It creates the incentive for others to sign up, it improves transparency and since it is already paid for with taxpayer’s money them charging money for it would accord Shvaikovsky be wrong (308). It is an environment for both private and government organizations to work in, allowing for easy information sharing between organizations. Shvaikovsky gives the example of Telia, a telecom company that uses the X-Road:
24
“We are exchanging [data] through the X-Road where we are submitting our information, taxation board to social security eh, to statistical boards, financial kind of, ministry of finance and so on and so on. So, so you are communicating in that environment.” (Interview:348-351)
This goes to show that both private and governmental organizations can work together and in the view of Nortal this helps create a seamless society and hence create value for everyone involved including the private persons whose transaction costs are also lowered (Interview:142-144). Joining the X-Road creates transparency, and ensures a responsible reuse of data since every move is being tracked and leaves a mark, which can be seen by the citizen.
5.1.2 PARTICIPATION MECHANISMS Nortal products and services do not require much from participation mechanisms. When talking about taxation systems, participation in the government is not on volunteer basis. As Shvaikovsky points out, the end-user needs an incentive to engage with a product. When talking about a mobile ID implemented in an Eastern-African country by another company the technical part was done successfully, however not many ID cards were being issued. Nortal took over the project and by engaging the surroundings and not just focusing on the technical implementation succeeded with creating an incentive for the end- user to want the product.
Participation Mechanisms is about organizations and citizens joining forces, and Nortal have as far as this research goes not focused much on this mechanism. Going back to transparency, and cheering for transparency through good governance, governments opening up and sharing their data, it can be argued that through those steps Nortal indirectly encourages to citizen participation in the government. Through good governance through e-governance more data will be available, citizens will have an opportunity to be involved in democratic participation. Opening up governments and government data is the first step towards transparency, which can lead to democratic participation, and from there eventually governments and citizens might be able to join forces. I would however still argue, that Nortal is not directly focusing on participation mechanisms. However I argue
25 that Nortal does not focus directly on public participation in government, however it might be a “side effect” to their products.
5.1.3 EFFICIENCY MECHANISMS Efficiency Mechanisms are used to improve resource utilization in order to minimize waste. Shvaikovsky mentions several times that Nortal wishes to create a seamless society by lowering transaction costs not only for companies but for private persons as well (142-144; 146-147; 184). This is where the efficiency mechanisms come into play as value generating mechanisms.
Nortal teaches organizations how to make fully use of the data - no matter if it is Big Data, Open Data or OGD. Through the optimizing of the resources and data management, they help the organizations to help themselves and thereby help the organizations help the end-user by freeing up more resources that can be used more efficiently. Nortal states that the healthcare sector can benefit from the technical revolution in terms of higher efficiency (Nortal B) when talking about the systems they have implemented for the Estonian Unemployment Insurance Fund, EMPIS and TETRIS, all the benefits listed relates to efficiency mechanisms. They are all showing how the new systems have eased the workload of the Fund and made it more efficient. When talking about EMPIS it is mentioned that the automated data retrieval have shortened the registration time to an average 10 minutes and it has brought forth an additional 6.4 million EUR value-added tax (Nortal C). In relation to TETRIS the time saved is once again: “TETRIS creates value by leveraging optimized business processes to enable fast, consistent, transparent, and timely work ability assessments” (Nortal A). The quote highlights the focus on optimizing business processes and the goals achieved by doing this. This clearly shows the focus on efficiency mechanisms minimizing waste and increasing productivity. Shvaikovsky states that:
“(...) with the seamless society story, that’s about how to reduce the transactional costs (...) So we are dealing with, we are reducing this transactional cost, constantly, all the time. Both in private and in governmental (...) ” (136-147)
26 Once again highlighting the importance of efficiency mechanisms and the results hereof - not just from an economic perspective but also creating social value in the sense that they are creating a seamless society for everyone.
Much data is stored in different places and many companies have it lying around (Hofheinz and Osimo 2017:5) and as mentioned in 4.1.2 technical makes it difficult to use OGD. If one does not have a system setup for identifying the relevant data needed, then the amount of data can seem exhaustive. Nortal has the capability to help develop platforms that can connect registers and databases and thereby help organizations retrieve the data available and of use.
5.1.4 INNIVATION MECHANISMS As with participation mechanisms it is difficult to find specific examples of Nortal using innovation mechanisms, however whenever they have to develop a solution to a client, they have to innovate products that can have economy-wide effects. Nortal themselves work with innovation mechanisms for internal reasons. They have to stay on top when it comes to understanding their client’s wishes in order to be able to collect, manage and use different types of data and improve their client’s organizations. When working with any sort of organization, Nortal tries to solve complex social problems and simplify them for the consumer, cutting down transaction costs and in the end improving the management of data. An example of Nortal’s work leading to an innovative and transformational change is their work in Oman where they created a portal suiting the ease of doing business and creating a business. The services planned and implemented by Nortal have transformed the markets in Oman. Nortals work created value for the government, easing their workload and they helped create value for the private business by easing their communication with the government. Estonia is very well known for their e-solutions and when working with the X-Road or e-residence and e-identity and connecting databases – both private and governmental to the X-Road, many opportunities to innovate is created. Nortal helps improve government transparency, which in turn has led to private companies signing up for the X-Road and thereby gaining access to information that can lead to new innovations.
In summary, Nortal pushes for transparency through their work, they want to create a seamless society and in order to do this, they believe in a transparent data-sharing
27 infrastructure. They observe that both private and government institutions can gain from the use of transparency mechanisms. Participation mechanisms are not much of a focus to Nortal, however they encourage good governance and in order for citizens to participate they must have access to data and it can therefore vaguely be argued that Nortal indirectly encourages the use of participation mechanisms - the same goes for private companies, if they wishes for citizens to bring ideas to the table, then information and data is needed. Efficiency mechanisms are used to create an overview of the data and reducing waste – both in terms of time and paperwork and to ensure the laws is followed leading to legitimate decisions without bureaucracy. This goes for both private and governmental organizations (Nortal C). Finally Nortal has to stay innovative in order to be successful, and it can be argued that when freeing up time previously spent on paperwork and leading through complicated legislative texts, the time can now be spent on improving products and services and perhaps even lead to new innovative ideas to spur. It is however difficult to tell from the empirical data gathered here. It can be argued that the archetypical mechanism in terms of value generation is the efficiency mechanisms in the lower left corner; however I will argue that the precise mechanisms used is more towards the center.
5.2 VALUE ENABLING FACTORS When working with the open government data value generation framework, the use of certain mechanisms influencing value creation for Nortal, governments and private institutions was covered. However in the process of working through the empirical data I identified several themes not covered, which influences the value creating process. These themes are widely related to the role and importance of management. Thus, this section will provide an analysis of additional themes identified in the empirical data; meaning extraction, change management, and leadership.
External sources of knowledge are plenty fold today, however access to data and knowledge does not necessarily equal idea generation. In order to integrate external resources in an organization, competences and capacities are needed. Several times throughout the interview Shvaikovsky highlighted that the data itself is not the important player when creating value from OGD. It is the goal behind the use of value and how to reach that goal that is of importance. As summed up by Cockburn and Smith (2013), the net effects of the digital development are that new demands will be placed on leaders and teams in any industry. As mentioned by Shvaikovsky, interdisciplinary abilities are a
28 necessity and Smith and Cockburn agree with this (Interview:479; Cockburn and Smith 2013). Capacity to extract meaning from the data is another factor that leads to generating value. Hofheinz and Osimo states that real insight comes from looking at the broader picture and aggregating the data (2017:18), however if you only have information and large amounts of data flowing in but no knowledge of how to structure and manage it, then much of the data is useless.
5.2.1 CHANGE MANAGEMENT For companies to make use of the data available from various sources and be able to turn the raw material data is into value, a shift in the organizational culture might be necessary. Smith and Cockburn states that the new data stream and the digital technologies will lead to new demands on leaders and teams, and “business as usual” is no longer an option. Changing the culture of an organization requires leaders to re-equip themselves and create a team-based culture (Smith 2014:282).
The culture of an organization makes up the underlying set of values, beliefs, understandings and norms - this again goes for both private and governmental organizations. Culture and values lay the foundation for how an organization is driven and therefore influences what is possible to implement, both from a top-down as well as a bottom-up perspective (Daft 2014:26). The culture and values sets the framework for the organization and its employees as well as influences ethical behavior, commitment to employees, efficiency, and much more (Yu & Hang 2010:442).
As stated, Nortal focuses on “the overlapping space between strategy, change management, and technology” (Nortal: About Us). Shvaikovsky states that “in order for data to give value, you need to combine it with other resources, and with humans doing some thinking” (Interview:486). This highlights the fact that data and technology cannot stand alone, then it loses it value - as Bowman and Abrosini (2002) states, resources needs work, transformation, and needs to be activated before they start to cast of any value (2002:5). Hofheinz and Osimo (2017) points out the fact that it is not the data alone but the aggregation of data points that helps create social insight and lead to changes (2017:18) which is exactly the same point as Shvaikovsky makes. Shvaikovsky estimates that the technological part of a project is about 20% and the remaining 80% is related to change management. Change management comes into place when transformations
29 happen. Change management is about implementing new ideas, procedures, processes or behaviours to organizations. There are different ways of going about change management – i.e. Lewin’s Force Field Analysis, also known as the three-step model or Kotter’s eight-step model. I will not explore these models more here, since the importance here is not on how change management is conducted, but simply that it is conducted. All this relates back to what Bowman and Abrosini emphasise when stating that value is only added when resources (data) are worked on and activated (2002:5).
Shvaikovsky states that 20-30 years back in time, the management books highlighted people as the major asset of every company, and that today you would add data to that equation, making it “people plus data” (Interview:420-425). Managing both assets takes skills and Nortal is good at juggling both and have helped governments and private organizations do exactly this. Nortal has the understanding of empowering the entire organization and encourage interdisciplinary. It is about creating a collaborative work environment where everybody feels responsible for reaching the goals set. Shvaikovsky states that interdisciplinary is very important and it is when fields are overlapping and people with different background and knowledge support each other towards a common goal that you really can turn data into value. When only focusing on the technical aspect, the chances of failing are very big (Interview:479-487). Extracting meaning from the data is once again related to management decisions (Interview:433-439). In conclusion, in order for OGD to lead to value creation, the mechanisms presented is a necessary tool (in varying degrees however) but value can only be created if all surrounding systems and without any use value related to the customers needs, it will not succeed. Only focusing on implementing the technical part will, as proven by Nortal with the case in Eastern-Africa (Interview:511-532), more often than not lead to success, however handling all the surroundings and managing the change will lead to a change in organizational procedures and perhaps even cultural changes will happen.
5.3 PART CONCLUSION Rarely, a company will fit into one single mechanism when trying to explain a certain outcome (Jetzek 2014:114). Nortal fits very well into the left side of figure 2 focusing on generating social value and the public sector is the active stakeholder in value generation. This is due to Nortal’s collaborations with government organizations and also primarily due to the focus of this report on OGD. Nortal has a great focus on transparency and on
30 improving the visibility of governments and organizations. Furthermore they highlight the importance of letting the private persons control their data and be able to back trace the use. This leads to a more responsible use of OGD and creates trust between private persons and organizations. Participation mechanisms are not what Nortal uses the most. They have a great focus on involving the entire organization they are working for however when it comes to public participation through voluntary contributions and ideas, it is not their main focus. When talking about efficiency mechanisms, Nortal focuses on lowering transaction costs in order to create a seamless society. Optimizing resources does this and efficiency mechanisms are very important tools for Nortal’s work. Innovation mechanisms is used by Nortal to stay on top of themselves, however related to the previous set of mechanisms, lowering transaction costs and freeing up time previously spent on e.g. paperwork can now be spend on innovation products and services. Additionally, as highlighted by Shvaikovsky, competences and skills are necessary in order to transform the raw data into something useful, which connects change management to value creation.
6.0 DISCUSSION
In this section I will discuss the findings of the analysis and draw in the perspective of future challenges for Open Government Data (OGD). The chapter begins with a discussion of Estonia's position - is it just good branding or what is the reason for private companies struggling with digitalisation? This will lead to a discussion of the future challenges and whether a Baltic digital single market would work or if the only way forward is a European digital single market.
6.1 INTERNAL CHALLENGES As stated in section 2.3 the Digital Economy and Society Index 2017 (DESI) points towards the fact that Estonian private companies are struggling with digitalization and Estonia scores below the EU average in digitising businesses due to low scores in i.e. electronic information sharing. As mentioned throughout this report, many consider Estonia a forerunner within IT and they brand themselves as e-Estonia however is it just good branding?
Estonia is by far most famous for their public IT-solutions and the X-Road. When it comes to digital public services, Estonia comes in first on the DESI. Estonia is the only country in
31 the world that has developed such a thing as the X-Road where only one ID card is necessary and both public and private institutions are connected. The share of IT specialists in Estonia is above the EU average - this might as well be due to the branding aspect (European Commission D 2017). Furthermore, Estonia has the perfect size for trying out new IT solutions, and this is encouraged from political side as well which once again helps attract specialists. The Estonian argument would be that it is not due to branding; it is all true and then highlight all the many start-ups instead of focusing on the lack of digitalisation of companies. Moreover, there have been improvements in the area, although they are stalling. Europe’s Digital Progress Report 2017 (EDPR) suggests that even though there are many IT specialists in Estonian, ICT start-ups might struggle finding experts that can help them. The EDPR highlights the fact that Estonia at the moment has no specific strategy for the digitalisation of its economy. The digitalisation strategy is broad and wishes to facilitate an environment stimulating entrepreneurship and innovations (EDPR 2017, Estonia). For that reason, it is suggested that Estonia could start implementing targeted initiatives. Nortal helps develop services that can ease the use of large quantities of data, but buying such services might be too expensive for a small enterprise and it could therefore be argued that the government could initiate programs that could help small companies on how to make use of the data and how to implement it in their businesses.
Related to the lack of resources, as shown throughout the analysis above, Nortal highlights change management as a crucial step in successfully implementing a new IT strategy. If this is not done right, the new initiatives might seem unusable and will not fit to the old culture of the organization and hence not be optimally utilized. As Shvaikovsky states, management today is about people plus data (Interview:424). It is still necessary to manage the people, otherwise the ‘plus’ disappears and both data and people need to cooperate in order to create a success.
To sum up, Estonian companies are lacking behind in digitalisation and the reasons might be that 1) e-Estonia is a lot of branding and not all companies follow that description or 2) as the analysis shows and as pointed out by Shvaikovsky, implementing the technology is not enough alone. One needs to incorporate the entire organisational ecosystem and perhaps change organisational culture; otherwise the new technological implementations will not fully work as intended. 3) The companies on the DESI includes all sorts of private
32 companies and investing in new technologies for, say, a sawmill can be very expensive and the economic aspect is of course worth noticing. 4) Managing both people and data together is crucial for technological implementations to be a success.
Governments, companies and private persons are often reluctant to share their data even though they see the potential. They are afraid that the data will be misused or violated. A recent European Commission-funded study found that 87% out of 100-surveyed companies did not share data (Hofheinz and Osimo 2017:5). It can be argued that privacy is an enabler of trust and trust is an enabler of free movement of data - as shown with the X-Road where both public and private institutions sign up and private persons have the control over their data. On one hand, the question dates back to who owns the data - is it the individual who owns the product, is it the company who produced the product or is it everyone who is interested?
The questions above relates to the question of pragmatic open data vs. ideological open data. Should we follow the ideological way where everything is open source and anybody can make use of - and reuse the data as they wish. On the other hand, is a pragmatic view where data is available in a timely way ensuring that everyone is allowed the same access more fitting? The EU have with the General Data Protection Regulation (GDPR) (being enforced in may 2018) taken the stance to give back control of the data to individuals - much like what is possible in Estonia. All data, which can be traced back to a person, is subject to strict rules governing both access, use, reuse and cross-border transfer (Ibid.). Estonia already have a high level of trust when it comes to not misusing data and the X-Road is free of use and everyone can use the X-Road - making the X-Road a part of the ideological open data movement, however the technology behind it is not open source. The private person is in full control over who has access to the data, which makes the data from the X-Road pragmatic open data. This goes to show that both pragmatic and ideological open data is possible. When reasonable, data can and should be open, however in order to create trust and ensure no violations of personal rights, there is a need for some sort of pragmatism.
33 6.2 BALTIC DIGITAL SINGLE MARKET In the Digital Agenda 2020 for Estonia, the Ministry of Economics and Communications highlights the importance of countries to join forces and not develop basic services on their own (2013:2) + OECD report of 2011 (se digital agenda 2020 p. 6 + p. 10). According to the Baltic Development Forum (BDF) (2016), the Baltic Sea Region is a potential forerunner within digitalisation and could be an interesting place for testing new initiatives, particularly cross-border city collaboration (2017:4). On the open data aspect in the Baltic countries the current state of affairs is uneven. Each country has its advantages and disadvantages and they could therefore benefit much by learning from each other. With a larger and harmonized market, the potential for successful innovations increases. First of all the larger pot of data can potentially lead to more data-driven innovations, secondly in larger markets there is a natural larger demand for specific applications and new inventions (BDF 2016:11). Hofheinz and Osimo argues for a common European Digital Single Market, highlighting that companies can only thrive in the digital environment by working together and further using some of the same arguments as mentioned above (2017:5). They further state that the countries and regions who build up such data markets will have a great advantage when overcoming e.g. social challenges. They argue that creating a Digital Single Market in the EU would also ease the life of EU citizens who then can access their data in other EU member states (Ibid.:24).
It is clear, that there is an issue of trust that needs to be solved before data can travel freely within the EU. There is a consensus that sharing and re-using data can lead to more innovation and that OGD promotes entrepreneurship. However in order to cooperate across borders and create larger markets and therefore greater possibilities for companies, the issue with personal data needs to be solved first. The GDPR will be a move in the right direction, and it will be interesting to see if more regions will start to work together, since it will enhance not only the regions cooperation but it can ease cooperation all across the European Union.
6.3 PART CONCLUSION In summary Estonian companies are lacking behind in digitalisation and the reasons might be due to the e-Estonia and the brand it has become while companies are experiencing something different. It could also be because one needs to not only consider the technological part but also perhaps change organisational culture. Managing both people
34 and data together is crucial for technological implementations to be a success. For companies to digitalize it could be useful to create a Baltic Digital Single Market allowing companies to learn from one another across the entire region. One important step towards this, is establishing trust among citizens and enterprises that the data they produce will not be violated.
35 7.0 CONCLUSION
Value creation in relation to use of Open Government Data (OGD) is understood as a process consisting of several steps involving both human and technical resources. These steps are in a great deal related to change management and managing the many surrounding factors relating to OGD and technical implications. Thus OGD can be said to have an influence on Nortal’s way of doing business as goes for their customers be they governmental or private organisations in the sense that managing culture and implementation of new initiatives influence the outcome and use of data.
This project took a starting point from my internship at the Royal Danish Embassy in Tallinn where I often met both private and public organizations who were interested in how Estonia has become a nation of IT. I set out to investigate how OGD is used in private businesses. Nortal was selected as a case, as they are the largest IT company in Estonia and have helped both private and government institutions with implementing systems that use OGD and therefore know how to turn OGD and Open Data into value. Thus the problem statement of the project was:
How can we understand the use of Open Government Data from Nortal’s point of view and how is value created for private businesses?
The problem was investigated through a collection and analysis of empirical data including an interview with Oleg Shvaikovsky, Member of Board. The empirical data was analysed on the basis of the Open Government Data Value Generation Framework presented by Jetzek et al. (2014), which was selected through a review of the relevant academic literature on OGD and value creation. In this process it was noted that it is a fairly new field of research an the literature on value creation in private organisations when talking about OGD is quite limited and the link between OGD and especially private businesses is largely unexplored, and in this sense this project sought to contribute to the academic field.
The analysis showed that Nortal and their great focus on creating a seamless society makes use of transparency mechanisms and they highly supports transparent data- sharing emphasising that both private and government institutions can gain from this.
36 Participation mechanisms is not of a great focus for Nortal, however their work indirectly encourages democratic participation and indirectly allows for active citizen participation. Efficiency mechanisms are on the other hand very important for Nortal’s work. They have a focus on cutting transaction costs, optimizing processes and reducing waste. Finally, when focusing on innovation mechanisms Nortal have to stay on top themselves in order to continue their successes. Additionally it can be argued, that their solutions free up time in organizations that now can be used differently and perhaps lead to innovative ideas to spur – such results would however need to be investigated further. Taken all this into consideration, Nortal is to be found somewhere in between the center and the efficiency mechanisms due to their focus on generating social value while the public sector is the stakeholder when talking about value generation.
As the DESI index proves, Estonia is doing fairly well on many parameters however private businesses are lacking behind in digitalization, however their reputation as e- Estonia is highly due to branding and as highlighted by Shvaikovsky, the important part is not to implement the technology but the entire organization needs to be in on the changes taking place. Managing both data and people is not easy and this might very well be a reason for the Estonian private businesses that have not successfully implemented digitalization strategies. Both private organisations and persons are reluctant to share their data highlighting that there is a need to establish trust. This can be done through privacy and giving private persons the control of their data - the GDPR is a step towards this exactly.
Focusing on the Baltic Digital Single Market, not all Baltic countries are on the same level of implementing open data and open government data however focusing on a relatively small common market allows both states and private organisations to learn from one another. This could later on prepare the Baltic Digital Single Market for a common European Digital Single Market. Working across borders is once again only possible of the issue with personal data is solved and it will be interesting to see how the GDPR will affect this issue.
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