Paul Brous

Marijn F W H A Janssen

Riikka Vilminko-Heikkinen

A Brous

Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles

Keywords: data, governance, e-government, data governance, data quality, data management

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Many public organizations routinely store large volumes of data. The storage and analysis of this data should benefit society, as it can enable organizations to improve their decisions. Members of the public often assume that the authorities are well equipped to handle data, but, as Thompson et al. [START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF] illustrate, this is not always the case. Thompson et al. explain that these issues often do not arise from existing business rules or the technology itself, but from a lack of sound data governance. The objective of this article is to derive principles for data governance for developing effective data governance strategies and approaches.

Many academic sources follow the information governance definition of Weill and Ross [START_REF] Weill | IT governance: How top performers manage IT decision rights for superior results[END_REF] and define data governance as specifying the framework for decision rights and accountabilities to encourage desirable behavior in the use of data [START_REF] Khatri | Designing data governance[END_REF][START_REF] Otto | Data governance[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. Practitioners such as the Data Management Association (DAMA) tend to disagree with this generalization believing that data governance is more than only the specification of a framework, but can also be practiced. According to Otto [START_REF] Otto | A morphology of the organisation of data governance[END_REF], important formal goals of data governance for public organizations are: 1. to enable better decision making, 2. to ensure compliance, 3. To increase business efficiency and effectiveness, 4. to support business integration [START_REF] Otto | A morphology of the organisation of data governance[END_REF].

Data governance provides both direct and indirect benefits [START_REF] Ladley | Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program[END_REF]. Direct benefits of data governance for business processes can be linked to efficiency improvements [START_REF] Griffin | Action Record -Four Critical Principles of Data Governance Success[END_REF][START_REF] Hripcsak | Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting[END_REF][START_REF] Ladley | Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program[END_REF][START_REF] Smallwood | CHAPTER 2: Information Governance, IT Governance, Data Governance: What's the Difference?[END_REF], an increase in revenue and market share [START_REF] Begg | Data Governance in Practice: The SME Quandary Reflections on the Reality of Data Governance in the Small to Medium Enterprise (SME) Sector[END_REF], [START_REF] Van Bommel | Giving meaning to enterprise architectures: architecture principles with ORM and ORC[END_REF], [START_REF] Coleman | The Role of Data Governance to Relieve Information Sharing Impairments in the Federal Government[END_REF], reduced risk [START_REF] Otto | A morphology of the organisation of data governance[END_REF][START_REF] Otto | Data governance[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF] and a reduction in costs incurred [START_REF] Malik | Governing Big Data: Principles and practices[END_REF][START_REF] Otto | On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America[END_REF][START_REF] Otto | Organizing data governance: Findings from the telecommunications industry and consequences for large service providers[END_REF][START_REF] Panian | Some practical experiences in data governance[END_REF]. Reductions in risk can be found in reducing privacy violations [START_REF] Tallon | Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost[END_REF][START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF][START_REF] Trope | A coherent strategy for data security through data governance[END_REF], increasing data security [START_REF] Khatri | Designing data governance[END_REF][START_REF] Panian | Some practical experiences in data governance[END_REF][START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF], and reducing the risk of civil and regulatory liability [START_REF] Otto | On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America[END_REF][START_REF] Otto | On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America[END_REF][START_REF] Tallon | Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost[END_REF]. Indirect benefits of data governance can be found in improving the perception of how information initiatives perform [START_REF] Griffin | Action Record -Four Critical Principles of Data Governance Success[END_REF][START_REF] Ladley | Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program[END_REF][START_REF] Tupper | 1 -Understanding Architectural Principles[END_REF], improving the acceptance of spending on information management projects [START_REF] Panian | Some practical experiences in data governance[END_REF][START_REF] Tallon | Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost[END_REF][START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF], and improving trust in information products [START_REF] Otto | Organizing data governance: Findings from the telecommunications industry and consequences for large service providers[END_REF][START_REF] Otto | Data governance[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF].

Although scant attention has been paid to this topic by the scientific community, there have been several calls within the scientific community for more systematic research into data governance and its impact on the information capabilities of organizations [START_REF] Otto | A morphology of the organisation of data governance[END_REF][START_REF] Otto | Data governance[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. Little evidence has been produced so far indicating what actually has to be organized by data governance and what data governance processes may entail [START_REF] Otto | A morphology of the organisation of data governance[END_REF].

Most research into data governance till now has focused on structuring or organizing data governance. Evidence is scant as to which data governance processes should be implemented, what data governance should be coordinating or how data governance could be coordinated [START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. By means of a systematic review of literature, the principles of data governance we present here attempt to fill this gap. This article is in line with Wende's [START_REF] Wende | A Contingency Approach to Data Governance[END_REF] call for further analysis of the guidelines and policy aspect of data governance.

Research Methods

According to Webster & Watson [START_REF] Webster | Analyzing the Past to Prepare for the Future: Writing a Literature Review[END_REF], a methodological review of past literature is important for any academic research, and they criticize the Information Systems (IS) field for having very few theories and outlets for quality literature review. A lack of proper literature reviews can and has hindered theoretical and conceptual progress in IS research [START_REF] Levy | A systems approach to conduct an effective literature review in support of information systems research[END_REF][START_REF] Webster | Analyzing the Past to Prepare for the Future: Writing a Literature Review[END_REF]. This article follows the method proposed by Webster & Watson and Levy & Ellis and attempts to methodologically analyze and synthesize literature and as such provide a firm foundation to data governance and advance the knowledge base of data governance by providing number of principles for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance strategy and approach. There is only limited research on data governance [START_REF] Otto | A morphology of the organisation of data governance[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF] and an elaborate analysis of the interaction of roles and responsibilities, and the principles of data governance is missing. For our research, we therefore also incorporate data governance sources from practitioners (e.g., [START_REF] Dyché | A Data Governance Manifesto: Designing and Deploying Sustainable Data Governance[END_REF][START_REF] Griffin | Action Record -Four Critical Principles of Data Governance Success[END_REF][START_REF] Ladley | Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program[END_REF][START_REF] Smallwood | CHAPTER 2: Information Governance, IT Governance, Data Governance: What's the Difference?[END_REF][START_REF] Soares | The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices[END_REF][START_REF] Sweden | Data Governance -Managing Information As An Enterprise Asset Part I -An Introduction[END_REF][START_REF] Tupper | 1 -Understanding Architectural Principles[END_REF]).

In November, 2015, the keywords: "data governance", and "principles", returned 17 hits within the databases Scopus, Web of Science, IEEE explore, and JSTOR. 8 hits were journal articles, 6 were conference papers, 2 were books and 1 hit was an article in the press. OF these articles, only 1 article, [START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF], was directly related to egovernance. The query [all abstract: "data governance" "principles"] searching between 2000 and 2015 returned 1710 hits in Google Scholar. We found a great deal of these articles covered data governance in general, but few articles included an explicit list of principles for data governance. We then filtered these results and performed a forward and backward search to select relevant articles based on the criteria that they included a theoretical discussion on what data governance is or does. Based on this forward and backward search, 35 journal articles, conference proceedings and books were selected and relevant principles from these sources were listed. Practical sources were only used when the authors provided factual evidence for their assertions. As the review is concept-centric, the sources were grouped according to concept proposed by Webster & Watson [START_REF] Webster | Analyzing the Past to Prepare for the Future: Writing a Literature Review[END_REF]. Webster & Watson recommend the compilation of a concept matrix as each article is read (Table 1). The next step recommended by Webster & Watson is to develop a logical approach to grouping and presenting the key concepts that have been uncovered (Table 2) and synthesize the literature by discussing each identified concept.

Following the recommendations of Bharosa & Janssen for principle generation, the long list of concepts seen in Table 1 was reduced to a short list as seen below in Table 2. The articles were categorized based on the types of variables examined, a scheme that helps to define the topic area. Principles constrain the design which ultimately seeks to attain the required business goals. By focusing on the formal goals of data governance which contribute to e-governance (enable better decision making, ensure compliance, increase business efficiency and effectiveness, and to support business integration), which we identified as independent variables, we were able to identify the dependent variables (long list of concepts, Table 1) contributing to these goals and grouped them according to intervening variables (short list of principles, Table 2), which appear in more complex causal relationships. Intervening variables come between the independent and dependent variables and shows the link or mechanism between them. Four concepts related to the goals of data governance were identified in the literature (Table 2). At this stage in our research no unit of analysis is included in the matrix, as the unit of analysis currently used is the organization. Future research can focus on identifying which principles are applicable to the varying units of analysis (organizational, group, or individual).

Foundation and Boundaries

Principles are particularly useful when it comes to solving ill-structured or "complex" problems, which cannot be formulated in explicit and quantitative terms, and which cannot be solved by known and feasible computational techniques [START_REF] Simon | The sciences of the artificial[END_REF]. Principles are a set of statements that describe the basic doctrines of data governance [START_REF] Dyché | A Data Governance Manifesto: Designing and Deploying Sustainable Data Governance[END_REF]. This paper follows the definition of Bharosa and Janssen who define principles as "normative, reusable and directive guidelines, formulated towards taking action by the information system architects" pp 472. In their Architecture Framework (TOGAF), the Open Group [START_REF]The Open Group: TOGAF® 9.1[END_REF] lists five criteria that distinguish a good set of architecture principles: understandable, robust, complete, consistent and stable. Van Bommel et al. [START_REF] Van Bommel | Giving meaning to enterprise architectures: architecture principles with ORM and ORC[END_REF] believe that the underlying tenets should be quickly understood by individuals throughout the organization and according to Khatri & Brown [START_REF] Khatri | Designing data governance[END_REF], principles should be supported by a rationale and a set of implications. A robust principle should enable good quality decisions to be made, and enforceable policies and standards to be created.

There is much confusion about what 'data' really is. Data is a set of characters, which have no meaning unless seen in the context of usage. The context and the usage provide a meaning to the data that constitute information [START_REF] Al-Khouri | Data ownership: who owns "my data[END_REF]. Most scientific sources use the terms "information" and "data" interchangeably. This generalization has led academic sources to follow the information governance definition of Weill and Ross [START_REF] Weill | IT governance: How top performers manage IT decision rights for superior results[END_REF] and define data governance as specifying the framework for decision rights and accountabilities to encourage desirable behavior in the use of data [START_REF] Khatri | Designing data governance[END_REF][START_REF] Otto | Data governance[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. Practitioners tend to disagree with this generalization as whilst the scope of data governance may include information as well as data, the two are different. The term, "data" is often distinguished from "information" by referring to data as simple facts and to information as data put in a context or data that has been processed [START_REF] Huang | Quality Information and Knowledge[END_REF], [START_REF] Price | A Semiotic Information Quality Framework[END_REF]. Also, many practitioners prefer to define data governance as a business function. For example, Forrester research defines data governance as being "a strategic business program that determines and prioritizes the financial benefit data brings to organizations as well as mitigates the business risk of poor data practices and quality" [START_REF] Goetz | Blog[END_REF] pp 1. DMBOK [START_REF] International | The DAMA Guide to the Data Management Body of Knowledge[END_REF], defines data governance as, "The exercise of authority, control, and shared decision making (planning, monitoring and enforcement) over the management of data assets" pp. 37. As such, in the eye of the practitioner, data governance is more than only the specification of a framework, but can also be practiced. Data governance ensures that data and information are managed appropriately. Theoretically, data governance describes the processes, and defines responsibilities. Data managers then work within this framework.

Principles of Data Governance

Four principles were identified from the basis of the literature review. These principles are presented individually in detail in the following sections.

Organization

Most researchers agree that data governance has an organizational dimension ( [START_REF] Khatri | Designing data governance[END_REF][START_REF] Otto | On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF]). For example, Wende & Otto [START_REF] Wende | A Contingency Approach to Data Governance[END_REF] believe that data governance specifies the framework for decision rights and accountabilities to encourage desirable behavior in the use of data. The first organizational dimension of Otto (2013) relates to an organization's goals. Formal goals measure an organization's performance and relate to maintaining or raising the value of a company's data assets [START_REF] Otto | On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America[END_REF]. Functional goals refer to the tasks an organization has to fulfil and are represented by the decision rights defined such as the definition of data quality metrics, the specification of metadata, or the design of a data architecture and a data lifecycle [START_REF] Weber | One Size Does Not Fit All-A Contingency Approach to Data Governance[END_REF]. Otto's second organizational dimension is the organizational form, such as the structure in which responsibilities are specified and assigned, and the process organization. Issues are addressed within corporate structures [START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. The data governance model is comprised of roles, decision areas, main activities, and responsibilities [START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. However, the organization of data governance should not be seen as a "one size fits all" approach [START_REF] Wende | A Contingency Approach to Data Governance[END_REF]. Decision-making bodies need to be identified for each organization, and data governance must be institutionalized through a formal organizational structure that fits with a specific organization [START_REF] Malik | Governing Big Data: Principles and practices[END_REF]. Decision rights indicate who arbitrates and who makes those decisions [START_REF] Dyché | A Data Governance Manifesto: Designing and Deploying Sustainable Data Governance[END_REF]. According to Dawes [START_REF] Dawes | Stewardship and Usefulness: Policy Principles for Information-based Transparency[END_REF], "stewardship" focuses on assuring accuracy, validity, security, management, and preservation of information holdings. Otto's [START_REF] Otto | On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America[END_REF] third organizational dimension consists of a transformation process on the one hand and organizational change measures on the other. Malik [START_REF] Malik | Governing Big Data: Principles and practices[END_REF] indicates the need to establish clear communications and patterns that would aid in handling policies for quick resolution of issues [START_REF] Malik | Governing Big Data: Principles and practices[END_REF], and Thompson et al. [START_REF] Thompson | Government data does not mean data governance: Lessons learned from a public sector application audit[END_REF] show that coordination of decision making in data governance structures may be seen as a hierarchical arrangement in which superiors delegate and communicate their wishes to their subordinates, who in turn delegate their control.

Alignment

Data governance should ensure that data meets the needs of the business [START_REF] Panian | Some practical experiences in data governance[END_REF]. A data governance program must be able to demonstrate business value, or it may not get the executive sponsorship and funding it needs to move forward [START_REF] Smallwood | CHAPTER 2: Information Governance, IT Governance, Data Governance: What's the Difference?[END_REF]. Describing the business uses of data establishes the extent to which specific policies are appropriate for data management. According to Panian [START_REF] Panian | Some practical experiences in data governance[END_REF], if used correctly, data can be a reus-able asset as data is a virtual representation of an organization's activities and transactions and its outcomes and results. Data governance should ensure that data is "useful" [START_REF] Dawes | Stewardship and Usefulness: Policy Principles for Information-based Transparency[END_REF]. According to Dawes, information should be helpful to its intended users, or should support the usefulness of other disseminated information. While government organizations may want to achieve the goals of data governance in theory, they often have difficulty justifying the effort unless it has a practical, concrete impact on the business [START_REF] Panian | Some practical experiences in data governance[END_REF]. Data governance also provides the framework for addressing complex issues such as improving data quality or developing a single view of the customer at an enterprise level [START_REF] Panian | Some practical experiences in data governance[END_REF]. Wende and Otto [START_REF] Wende | A Contingency Approach to Data Governance[END_REF] believe that a data quality strategy is therefore required to ensure that data management activities are in line with the overall business strategy. The strategy should include the strategic objectives which are pursued by data quality management and how it is aligned with the company's strategic business goals and overall functional scope. Data quality is considered by many researchers to be an important metric for the performance of data governance ( [START_REF] Khatri | Designing data governance[END_REF][START_REF] Otto | Organizing data governance: Findings from the telecommunications industry and consequences for large service providers[END_REF][START_REF] Wende | A Contingency Approach to Data Governance[END_REF]).

Compliance

Data governance includes a clearly defined authority to create and enforce data policies and procedures [START_REF] Wilbanks | Data governance for SoS[END_REF]. Panian [START_REF] Panian | Some practical experiences in data governance[END_REF] states that establishing and enforcing policies and processes around the management data is the foundation of an effective data governance practice. Delineating the business uses of data, data principles establish the extent to which data is an enterprise wide asset, and thus what specific policies are appropriate [START_REF] Khatri | Designing data governance[END_REF]. According to Malik [START_REF] Malik | Governing Big Data: Principles and practices[END_REF], determination of policies for governance is typically done in a collaborative manner with IT and business teams coming together to agree on a framework of policies which are applicable across the whole organization [START_REF] Malik | Governing Big Data: Principles and practices[END_REF]. Tallon [START_REF] Tallon | Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost[END_REF] regards data governance practices as having a social and, in some cases, legal responsibility to safeguard personal data through processes such as "privacy by design", whilst Trope & Power [START_REF] Power | The 2006 survey of legal developments in data management, privacy, and information security: The continuing evolution of data governance[END_REF] suggest that risks and threats to data and privacy require diligent attention from organizations to prevent "bad things happening to good companies and good personnel" ( [START_REF] Power | The 2006 survey of legal developments in data management, privacy, and information security: The continuing evolution of data governance[END_REF] pp 471). Mechanisms need to be established to ensure organizations are held accountable for these obligations through a combination of incentives and penalties [START_REF] Al-Khouri | Data ownership: who owns "my data[END_REF] as, according to Felici et al. [START_REF] Felici | Bringing Accountability to the Cloud: Addressing Emerging Threats and Legal Perspectives[END_REF], governance is the process by which accountability is implemented. In such a manner, accountability can unlock further potential by addressing relevant problems of data stewardship and data protection in emerging in data ecosystems.

Common Understanding

According to Smith [START_REF] Smith | Data Governance and Enterprise Data Modeling -Don't Do One Without the Other! -Enterprise Information Management Institute[END_REF], governing data appropriately is only possible if it is properly understood what the data to be managed means, and why it is important to the organi-zation. Data understanding is essential to any application development, data warehousing or services-oriented-architecture effort. Misunderstood data or incomplete data requirements can affect the successful outcome of any IT project [START_REF] Smith | Data Governance and Enterprise Data Modeling -Don't Do One Without the Other! -Enterprise Information Management Institute[END_REF]. Smith believes that the best way to avoid problems created by misunderstanding the data, is to create an enterprise data model (EDM) and that creating and developing an EDM should be one of the basic activities of data governance. Attention to business areas and enterprise entities should be the responsibility of the appropriate data stewards who will have the entity-level knowledge necessary for development of the entities under their stewardship [START_REF] Smith | Data Governance and Enterprise Data Modeling -Don't Do One Without the Other! -Enterprise Information Management Institute[END_REF]. To ensure that the data is interpretable, metadata should be standardized to provide the ability to effectively use and track information [START_REF] Khatri | Designing data governance[END_REF]. This is because the way an organization conducts business, and its data, changes as the environment for a business changes. As such, Khatri & Brown [START_REF] Khatri | Designing data governance[END_REF] believe that there is a need to manage changes in metadata as well. Data governance principles should therefore reflect and preserve the value to society from the sharing and analysis of anonymized datasets as a collective resource [START_REF] Al-Khouri | Data ownership: who owns "my data[END_REF].

Discussion

Data governance is a topic that is attracting growing attention, both within the practitioners' community and among Information Systems researchers, due to growth of the amount of data. But data governance is a complex undertaking, and data governance projects in government organizations have often failed in the past. There is not one, single, "one size fits all" approach to the organization of data governance. Decisionmaking bodies need to be identified for each individual organization, and data governance should have a formal organizational structure that fits with a specific organization [START_REF] Malik | Governing Big Data: Principles and practices[END_REF]. An organization outlines its individual data governance configuration by defining roles, decision areas and responsibilities, with a unique configuration, and specialized people need to be hired, trained, nurtured, and integrated into the organization. Researchers have proposed initial frameworks for data governance ( [START_REF] Khatri | Designing data governance[END_REF][START_REF] Otto | Organizing data governance: Findings from the telecommunications industry and consequences for large service providers[END_REF]) and have analyzed influencing factors ( [START_REF] Weber | One Size Does Not Fit All-A Contingency Approach to Data Governance[END_REF]) as well as the morphology of data governance [START_REF] Otto | A morphology of the organisation of data governance[END_REF]. A number of data governance principles have emerged out of this research. These principles are depicted in figure 1 below. From the Long list of principles, four principles of data governance for public organizations were distilled. These principles are: 1. Organization, 2. Alignment, 3. Compliance Monitoring and Enforcement, 4. Common Understanding. Data Governance should ensure that data is aligned with the needs of the business. This includes aligning the quality of the data with the quality required by the business. Data quality is often related to "fitness for use" and data governance demands binding guidelines and rules for data quality management [START_REF] Otto | Organizing data governance: Findings from the telecommunications industry and consequences for large service providers[END_REF].

Fig. 1. Long list of key concepts and principles of data governance

Governing data also includes ensuring compliance to the strategic, tactical and operational policies which the data management organization needs to follow. While use of data has significant potential, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective data management approaches [START_REF] Hripcsak | Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting[END_REF]. Public organizations need to be able to create and share information in a way that is specifically customized for that organization to ensure a common understanding of the data.

Conclusions

Data governance is a complex undertaking and many data governance initiatives in public organizations have failed in the past. Principles of data governance include organization of data management, ensuring alignment with business needs, ensuring compliance, and ensuring a common understanding of data. However, the organization of data governance should not be seen as a "one size fits all" approach and data governance must be institutionalized through a formal organizational structure that fits with a specific organization. Data governance should also ensure that data is aligned with the needs of the business. This includes ensuring that data meets the necessary quality requirements. Ensuring alignment can take the form of defining, monitoring and enforcing data policies (internal and external) throughout the organization. Establishing and enforcing policies regarding the management of data is important for an effective data governance practice. But governing data appropriately is only possible if it is properly understood what the data to be managed means, and why it is important to the organization.

Table 1 .

 1 Long list of Data Governance Key Concepts

	Data Governance Key Concepts
	Accountability ([1, 2,	Meeting business	Compliance ([1, 2,	Shared data com-
	6, 11, 19, 41, 49]);	needs ([2, 8]);	35, 41]);	mons ([1, 7, 26, 27,
	Decision rights ([25,	Aligning business	Policy enforcement	39]);
	35, 41, 49]);	and IT ([29]);	([30, 39, 42]);	Use of standards
	Balanced roles ([1,	Developing data	Due diligence ([6,	([27, 41]);
	15, 35]);	strategy ([18, 22, 27,	15, 35]);	Metadata manage-
	Stewardship ([8, 15,	30, 39, 49, 50]);	Privacy ([1, 7, 11,	ment ([18, 27]);
	18, 33, 41, 49]);	Defining data quali-	15, 18, 19]);	Standardized data
	Ownership ([13, 41,	ty ([2, 15, 19, 27,	Openness ([11, 19]);	models ([27, 41]);
	43]);	35, 43, 49]) ;	Security ([1, 6, 7, 11,	Standardized opera-
	Separation of duties	Reducing error of	12, 15, 18, 19, 24,	tions ([27, 41]);
	([22]);	use ([7, 26, 49]);	30, 39, 42]);	Facilitates communi-
	Separation of concern	Effective policies	Measuring data	cation ([22, 27, 39])
	([22]);	and procedures ([13,	quality ([15, 18, 19,
	Improved coordina-	15, 35, 48])	27, 35, 43, 48])
	tion of decision mak-
	ing ([27, 35, 39])

Table 2 .

 2 Concept matrix showing the concepts in relation to the authors

	Concepts
	Organization	Alignment	Compliance	Common Understand-
				ing


Luigi Reggi
email: luigi.reggi@gmail.com

Sharon Dawes
email: sdawes@ctg.albany.edu

Open Government Data Ecosystems: Linking Transparency for Innovation with Transparency for Participation and Accountability

Keywords: open government data, ecosystems, transparency, innovation, participation, accountability

The rhetoric of open government data (OGD) promises that data transparency will lead to multiple public benefits: economic and social innovation, civic participation, public-private collaboration, and public accountability. In reality much less has been accomplished in practice than advocates have hoped. OGD research to address this gap tends to fall into two streams -one that focuses on data publication and re-use for purposes of innovation, and one that views publication as a stimulus for civic participation and government accountability -with little attention to whether or how these two views interact. In this paper we use an ecosystem perspective to explore this question. Through an exploratory case study we show how two related cycles of influences can flow from open data publication. The first addresses transparency for innovation goals, the second addresses larger issues of data use for public engagement and greater government accountability. Together they help explain the potential and also the barriers to reaching both kinds of goals.

Introduction

The open government philosophy has stimulated a global transparency movement with goals of innovation, participation, and accountability. National and subnational governments in every part of the world are adopting open data programs with the expectation that free and open publication of government data will lead naturally to an array of economic, social, and political benefits. Yet, Yu and Robinson [START_REF] Yu | The New Ambiguity of 'Open Government[END_REF] suggest that the vagueness of the label "Open Government" does not help distinguish between openness of government data in terms of technical access and reusability for service innovation and the use of open data for civic participation and accountability purposes. Data publication and re-use by private actors can and does support innovative applications that reflect the interests and skills of technical experts. But publication by itself does not necessarily lead to greater collaboration between government and ordinary citizens nor to greater accountability by government for policies and programs. For example, Shkabatur [START_REF] Shkabatur | Transparency with (out) accountability: Open government in the United States[END_REF] contends that the US Open Government directive fosters "transparency without accountability". by allowing public agencies excessive discretion over which datasets are of "high value" and thus chosen to be published. Consequently, much of the data disclosed in discretionary OGD portals such as Data.gov can be irrelevant for purposes of accountability. Lourenço [START_REF] Lourenço | An analysis of open government portals: A perspective of transparency for accountability[END_REF] draws similar conclusions from a systematic analysis of seven national open data portals. Even in places where all data is required to be published, there is no guarantee that civic collaboration or greater accountability will result [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF].

While the rhetoric of open government data promises that data transparency will lead simultaneously to innovation, collaboration, and accountability, most research falls into one of two streams -one that focuses on data publication and re-use for purposes of innovation, and one that looks at data publication as a stimulus for civic participation and government accountability. Few attempts have been made to understand empirically whether and how these streams interact. In this paper we begin with a recent ecosystem model that draws on the first stream of work [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF] and extend it through the use of an exploratory national case study to also encompass the second. In particular our research aim is to demonstrate conceptually and empirically the crucial connections that allow these two streams of effects to be understood as a complex and integrated ecosystem with attendant barriers and enablers.

Literature review

Since the release of the Open Government directive in the United States in 2009 and the long process of revision of the European Directive on Public Sector Information concluded in 2013, public rhetoric has promised a trio of potential benefits: data-driven product and service innovation, greater public participation in policy making, and more government accountability. Researchers have generally followed two divergent paths -one group focusing on the innovation theme, the other on participation and accountability.

In the first group, authors have focused their attention on the economic benefits of re-use of OGD to foster innovation [START_REF] Ferro | Eight business model archetypes for PSI Re-Use[END_REF]. At the core of these works is the idea that the continuous release of easily accessible, machine-processable and possibly real-time government data can act "as a platform" for the creation of new applications and services [START_REF] O'reilly | Government as a Platform[END_REF], including "civic innovation" initiatives by NGOs and civic technology communities. To this end, Sieber and Johnson [START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF] identify two proactive strategies that governments can adopt to increase co-production of new services. In the code exchange model, governments actively support the use of OGD through app contests based on explicit public needs. In the participatory open data model, governments create feedback loops about data quality and structure, with the aim of initiating an "on-going co-creation of raw data between both governments and governed". A very similar view is presented in the open data for engagement framework [START_REF] Davies | Supporting open data use through active engagement. Using Open Data: policy modeling, citizen empowerment[END_REF], where users participate in the improvement of governmental datasets by offering feedback and creating new data resources. For example, platforms for open data publication have been developed that integrate the collection of user feedback on the data released through Web 2.0 functionalities [START_REF] Alexopoulos | A Platform for Closing the Open Data Feedback Loop based on Web2. 0 functionality[END_REF]. Related work has focused on data quality and management practices, and OGD program design and operation [START_REF] Zuiderwijk | Open data policies, their implementation and impact: A framework for comparison[END_REF][START_REF] Dawes | Designing electronic government information access programs: a holistic approach[END_REF].

Other scholars have highlighted the potential benefits of analyzing and visualizing government information to better understand public problems and make better decisions [START_REF] Fung | Full disclosure: The perils and promise of transparency[END_REF]. This literature also considers the contribution of OGD to more general Open Government objectives of increased availability of government information, improved civic participation and collaboration, and greater accountability of governmental activities. Peixoto [START_REF] Peixoto | The Uncertain Relationship between Open Data and Accountability: A Response to Yu and Robinson's' The New Ambiguity of Open Government[END_REF] considers civic participation as a key contributor to "unlocking the potential for open data to produce better government decisions and policies". Janssen, et al. [START_REF] Janssen | Benefits, adoption barriers and myths of open data and open government[END_REF] list political and social benefits including not only increased transparency, but also accountability, citizen empowerment, trust in government, and improvement of the policy making process. Published data can also be a powerful tool against corruption [START_REF] Bertot | Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies[END_REF] in transparency initiatives that emphasize disclosure of public budgets, agency performance, and contracts.

However, whether focused on innovation or on participation and accountability, current OGD practices suffer from substantial legal, political, social, institutional, economic, operational and technical challenges [START_REF] Zuiderwijk | Special Issue on Innovation through Open Data: Guest Editors' Introduction[END_REF], leading to what have been called the "myths" of open data [START_REF] Peixoto | The Uncertain Relationship between Open Data and Accountability: A Response to Yu and Robinson's' The New Ambiguity of Open Government[END_REF]. These include the belief that opening data leads automatically to more open and inclusive government. Political challenges include the lack of institutional motivation and political will to publish relevant datasets. Additional challenges emerge when considering the actual use of OGD. On the supply side, OGD programs are often designed not for citizens but for technical experts and intermediaries [START_REF] Janssen | Infomediary business models for connecting open data providers and users[END_REF]. On the demand side, the lack of incentives, interpretive tools, and contextual and technical knowledge among users can prevent meaningful data use [START_REF] Barry | Barriers to open data release: A view from the top[END_REF]. Finally, lack of institutional processes for dialogue prevents integration of public feedback into existing strategies and programs [START_REF] Janssen | Benefits, adoption barriers and myths of open data and open government[END_REF].

Recently researchers have begun to use an ecosystem metaphor to model the complex dynamics among these different actors and concerns [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF][START_REF] Pollock | Building the (Open) Data Ecosystem[END_REF]. In particular, Dawes, et al. [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF] draw from evidence in two empirical cases in different settings -New York City and St. Petersburg -to explore OGD programs as ecosystems of interconnected organizations and individuals working within a shared social context. Briefly, they identified a cycle of influences regarding the ways in which ecosystem factors shape publication, use, and feedback about the data itself. According to the model, OGD providers can influence data use by designing OGD strategies and publication practices that encourage use. In turn, the users, such as transparency advocates and civic technologists take advantage of the data by using it directly or by developing new applications that can reach a broader audience of beneficiaries, therefore acting as OGD intermediaries [START_REF] Janssen | Infomediary business models for connecting open data providers and users[END_REF]. The resulting economic and societal benefits can influence further advocacy and interaction with providers to improve the quality of OGD data, strategies and practices.

In the next section we describe the case of OpenCoesione in which data publication is augmented by both government and intermediaries to become information usable by civic groups and individuals attempting to hold the government accountable for development projects in Italy. We then use the case data to suggest an extension of the Dawes, et al. [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF] model to better integrate participation and accountability elements and discuss how the transparency inherent in OGD data publication programs can stimulate both innovation and participation and accountability.

In this section we present preliminary results of a case study of an OGD initiative in Italy from 2014 to the present that aims to improve citizen engagement and accountability, and the related ecosystem of data intermediaries and users. The data comprise participant observations and a review of three complementary open government applications (an OGD portal and a Massive Online Open Course developed by the Italian government and a civic technology application from civil society). In order to collect data on the perspectives of governmental and nongovernmental actors with different roles in the ecosystem, key stakeholders with knowledge and direct experience in these programs were identified through purposive sampling and interviewed between January and March 2016. They include practitioners with different roles in the Italian government (one project manager and three analysts), two members of the steering committee of the civil society initiative Monithon, one representative of a local community in Southern Italy, and two researchers at two different Italian research institutions. The semi-structured interviews were focused on their perspectives on the enablers and barriers to an effective and sustainable OGD ecosystem. Three published program reviews [START_REF] Zola | How to do (good) things with data. Civil society data-driven engagement for societal progress and innovation[END_REF][START_REF] Buttiglione | Il monitoraggio civico delle politiche di coesione e lo sviluppo di comunità civiche[END_REF][START_REF] Ciociola | A Scuola di OpenCoesione: From open data to civic engagement[END_REF] served as additional sources of information. The integrated conceptual model developed from the case was sent to the respondents for comment and validation.

Context

European Structural and Investment Funds (ESIFs) represent the main investment policy tool of the European Union (EU), with a total budget of €454 billion or 43% of the total EU budget.. The funds co-finance a wide range of national and local development policies, from the support of new businesses to the development of infrastructures in areas such as broadband, renewable energies, and water supplies, with a strong focus on reducing disparities among European regions and countries.

The economic literature has shown mixed results in assessing the real impact of European investment funding on economic growth [START_REF] Rodriguez-Pose | Between development and social policies: the impact of European Structural Funds in Objective 1 regions[END_REF], and these programs face challenges in terms of efficiency, effectiveness and the complexity of evaluating the performance and impact of millions of different projects funded across Europe.

Furthermore, concerns have been raised about the limited opportunities for bottomup inclusiveness and participation in the policy process, from programming to implementation to evaluation of results. A much more inclusive participatory process has been suggested as a way to improve efficiency, effectiveness and accountability of the policy, with stakeholders, civil society and final beneficiaries to be substantively involved [START_REF] Rodríguez-Pose | Do Institutions Matter for Regional Development?[END_REF][START_REF] Barca | An Agenda for A Reformed Cohesion Policy: A Place-Based Approach to Meeting European Union Challenges and Expectations[END_REF].

Starting in 2014, new regulations were adopted to increase transparency and participation of relevant stakeholders. First, a set of mandatory information must be disclosed in the form of OGD through development of program-specific national OGD portals, increasing the number of mandatory fields from 3 to 11, thus forcing administrations to release more detailed information on each funded project and its recipients [START_REF] Reggi | Information strategies for open government in Europe: EU regions opening up the data on structural funds[END_REF]. Second, the national portals must provide "information to all operational programs in that Member State, including information about the timing of implementation . . . and any related public consultation processes". Third, a code of conduct on partnership encourages broader engagement of local stakeholders.

ESIF in Italy: OpenCoesione, Monithon and A Scuola di OpenCoesione

Italy is the second largest recipient of ESIF among the EU countries, with an allocation of €42.7 billion for 2014-2020. The total budget for these policies is even higher -about €123 billion -thanks to national and regional co-financing and additional funds for regional development. These resources represent the main source of investments in Southern Italy, where about 80% of the funding is to be spent.

OpenCoesione.

All these financial resources are tracked on the national OGD portal OpenCoesione.gov.it, which acts as the national transparency portal under the new regulations. OpenCoesione was created in 2012 by the Ministry of Economic Development to publish information about every project carried out in the 2007-13 period. The portal makes use of a large set of administrative data from the national monitoring system managed by the Ministry of the Economy. The system is a federated information network that interoperates with dozens of local applications collecting information from the recipients of the funding, with a complex multi-level governance organization.

In March 2016, OpenCoesione was publishing data on almost 950.000 projects with a total investment of €51.2 billion. The projects range from the construction of large infrastructures worth billions of Euros to individual grants to students. For each project, users can access a webpage with information about the amount and sources of funding, approximate location, actors involved, and implementation timeframes. They can download raw data, use the Application Program Interfaces (APIs) to analyze the data or develop an application, or browse through interactive diagrams.

OpenCoesione also launched different initiatives to stimulate the use and re-use of the datasets, including publication of articles with news, analysis and infographics; maps and interactive visualizations; webinars; a data journalism school; and workshops and seminars at numerous research institutions. Interactive tools are available on the portal to receive comments and suggestions directly from the recipients and final beneficiaries. However, having insufficient resources for directly managing citizen engagement activities, OpenCoesione representatives also participated in hackathons organized by both national and local civic technology communities to stimulate new initiatives and applications and to collect feedback both on the data released and on the results of the projects included in OpenCoesione.

Monithon.

During one of these hackathons in 2013, the OpenCoesione team organized a Monithon, that is a "civic monitoring marathon" of local EU-funded projects. Based on the project-level OGD on OpenCoesione, a group of journalists, analysts, developers and individual citizens collected further information about five projects on the renovation of school buildings in Bologna by conducting interviews with people in charge of implementation and gathering evidence such as videos and photographs documenting progress and results. They soon realized that further investigations were needed since the OGD on the portal provided no clues about crucial questions such as: What are the policy motives and decisions that led to finance the project? Who is responsible within each governance actor? Who are the contractors and subcontractors and how have their expenditures been tracked? Most importantly, basic narratives about the projects' objectives and activities, performance data, and output indicators were all missing.

This first experiment then evolved into a nationwide, civil society initiative named Monithon.it, that in two years of activity drew dozens of local communities -some formed on purpose and others based on existing associations -and more than 3,000 people into civic monitoring activities. Both national and local communities are involved, such as Action Aid Italy or the main Italian anti-mafia association Libera. However, Monithon.it is not a formally funded organization; it relies mainly on volunteer effort. Although the costs of developing and maintaining the technical platform are partly covered by grants received thanks to partnerships with NGOs, Monithon.it faces a persistent problem of economic sustainability [START_REF] Zola | How to do (good) things with data. Civil society data-driven engagement for societal progress and innovation[END_REF]. Effective engagement and coordination of local communities depends heavily on the work of the three volunteers who comprise the central staff.

Civic monitoring is organized as a group activity in which interdisciplinary competences are employed to carry out qualitative investigations to assess project performance. These include the project history, the underlying policy motives, and the network of governance actors and implementers responsible for programming or implementation. The purpose of this activity is not only to enrich the information in the publicly available datasets and collect feedback on data quality, but also to collect feedback on the ground about project results and suggestions for improvement from the perspective of the final beneficiaries. All the new information acquired is collected through a standard methodology (questionnaire, interview guide, guidelines for data analysis and fieldwork organization), and then represented in a map on the Monithon.it platform [START_REF] Buttiglione | Il monitoraggio civico delle politiche di coesione e lo sviluppo di comunità civiche[END_REF].

In March 2014, after one year of activity, 55 "citizen monitoring reports" were published on Monithon.it, covering different policy areas such as transportation, cultural heritage, urban policy, education, and social inclusion [START_REF] Buttiglione | Il monitoraggio civico delle politiche di coesione e lo sviluppo di comunità civiche[END_REF]. By July 2015, 98 reports had been uploaded. About 40% contain basic information about the project, plus some evidence about the progress and the result, such as photographs. The remaining 60% can be characterized as in-depth investigations with detailed descriptions of project history and motives, displaying photos, videos and links to project or policy documentation.

The information collected is published on the Monithon.it platform as OGD and can be used by administrations and local governments, journalists, researchers and NGOs to influence the implementation of the ESIFs and the programming of future policy actions. In some cases, citizen monitoring reports received the attention of local newspapers, especially during special events such as Open Data Day, generating public debate about the use of public funding. In other cases, new collaborations were created between local communities and administrations. For example, the group Monithon Piemonte in Torino initiated a dialogue with the director of the Egyptian Museum to improve a renovation project funded by ESIFs. In Bari, the results of civic monitoring of social innovation projects were used by the Ministry of Research to program future actions in this field. In other instances, the lobbying activity of NGOs like Libera has helped channel feedback to the attention of policy makers. In these cases, citizen feedback influenced the way national and local administrators made decisions, in both the monitoring and in the policy creation phases of the policy cycle.

However, in many cases the feedback is shared only within the community and fails to be addressed to or by policy makers. While OpenCoesione collects citizen feedback both on the data itself and on policy performance it does not directly bring feedback to the attention of policy makers, but instead points the users in the right direction by giving information about the agencies responsible for specific projects and programs. But, since no legal mechanisms are in place that force administrations to consider feedback from individual citizens and informal groups, the Monithon communities need to persuade local decision makers to listen and collaborate [START_REF] Zola | How to do (good) things with data. Civil society data-driven engagement for societal progress and innovation[END_REF]. When it reaches local administrations, often it is not taken into account to make actual decisions. In addition, administrations have raised concerns about the representativeness of feedback collected.

A major challenge for sustainability, then, is creating enduring local groups with sufficient motivation and specific, interdisciplinary expertise to do this kind of work. While basic crowdsourcing activities such as collecting photos and videos documenting the progress of public works can be relatively easy to conduct, more sophisticated investigations require specific knowledge about ESIFs policy mechanisms, national and local administrative procedures, data analysis and visualization, fieldwork, data collection, and communication of findings. To this end, a partnership with Action Aid Italy was created to support local volunteers in developing new skills such as understanding financial data, via free workshops and laboratory sessions. In one case, a project financed by the European Commission will ensure three years of financial support to this kind of activity in Sibari (Calabria).

A Scuola di OpenCoesione.

Partly in response to these problems, in September 2013 OpenCoesione in partnership with the Representation Office of the European Commission in Italy launched A Scuola di OpenCoesione (or OpenCoesione School), an educational challenge for high school students and a Massive Online Open Course (MOOC), in order to stimulate data use, civic engagement and awareness. The Ministry of Education also partnered in this initiative with the goal of increasing data literacy and ICT use among students and teachers. A Scuola di OpenCoesione uses the Monithon tools and methodology to organize civic monitoring activities. The students learn not only how to analyze policy and administrative sources and conduct field investigations, but also how to use complex datasets regarding real-life civic issues to develop and present multimedia content. In the 2015-16 edition, 120 schools and 2,800 students from all Italian regions enrolled. Each school chose a project to analyze based on OGD from OpenCoesione.

As the students organized events to disseminate their results, they created further opportunities to raise civic awareness and to strengthen the dialogue with NGOs and local representatives of the European Commission (the "Europe Direct" network) from which they received support. All of the events are public and represent a sort of "accountability forum" in which the students interact with the local community and political leaders and administrators responsible for implementing the projects, asking questions and suggesting solutions. These events produced mixed results. In some cases, they stimulated an evidence-based public debate. In others, especially when the results of the citizen monitoring were mainly negative, local politicians did not respond to issues raised or simply did not get involved. When events were organized in municipalities where courts have appointed administrators to replace elected officials implicated in mafia crimes, public institutions did not attend at all [START_REF] Zola | How to do (good) things with data. Civil society data-driven engagement for societal progress and innovation[END_REF].

Discussion: Toward an integrated open government ecosystem

Drawing from the evidence in the case, we propose an extension of Dawes, et al. ecosystem model [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF] to show how a related second cycle of influences can flow from open data publication. While the first cycle addresses transparency mainly for purposes of innovation, the second addresses issues of collaboration and engagement around government policies and toward greater accountability for policy performance.

As shown in Figure 1, a government's OGD Policies and Strategies and Data Publication practices, such as choices about the format and granularity of the datasets, strongly shape the realm of possible Data Uses and Apps. In particular, OGD use in civic applications like Monithon.it not only stimulates civic awareness and social capital among local communities (Socio-economic benefits), but also enables the systematic Collection of citizen feedback on government performance, spending and policy results from the perspective of the actual beneficiaries. This feedback can be directly addressed to policy makers, or can be conveyed through the work of intermediaries such as the media, NGOs or other relevant stakeholders. In the first case, feedback can be conveyed through engagement tools and channels developed by the government. In the second case, Intermediaries can influence policy decisions by stimulating public debate or lobbying for specific goals. Intermediaries can also press for better data increasing the level of Advocacy and interaction with data providers, with consequent influence on OGD Policies and Strategies. The realization of more participatory forms of Policy Making can enable evidence-based decision making with the desired effect of improving accountability, efficiency and effectiveness of the policy. These perceived benefits, in turn, can potentially lead not only to better policy making practices and choices but also to improved OGD programs. Our case shows that all these influences are possible, although the last few related to evidence-based policy making and public accountability tend to be weak and infrequent. Thus we indicate them in Figure 1 with dotted arrows.

The case shows how a combination of government and civic actions can stimulate a cycle of collaboration and accountability (the darker box in Figure 1) that is linked to the more data-oriented innovation cycle (the lighter box) proposed by Dawes, et al. [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF]. According to our case, an OGD provider (OpenCoesione) published data with high granularity about significant European and national public investments, in ways that could prove useful for individual citizens and communities [START_REF] Dawes | Stewardship and usefulness: Policy principles for information-based transparency[END_REF] for diverse uses. Civil society actors such as Monithon.it leveraged this data to develop civic technology tools and methodologies to foster civic engagement for systematic collection of citizen feedback on project results. The government-sponsored A Scuola di OpenCoesione created civic awareness, social capital and new skills for participation [START_REF] Zuckerman | New media, new civics? Policy & Internet[END_REF] based in public schools, while NGOs such as Action Aid Italy and Libera fostered both skills and use of the data for important public issues. These practices helped overcome some of the typical limitations of OGD programs, which tend to offer engagement only about the data itself, and then mainly with a restricted group of technical experts and data intermediaries. Feedback collected from these communities on data content -that is on how public investment projects are progressing and what results they are achieving -shows the potential to influence not only existing OGD strategies and practices, but also the policy decisions about programming and implementation. The examples from the case show that new forms of direct engagement between communities and governments can be triggered by civic monitoring activities. In addition, indirect public influence for more evidence-based public debate can be enabled the intermediation of the media and NGOs through news reporting, advocacy, and lobbying. These interactions set the stage for greater policy accountability, project efficiency and effectiveness, better policy decisions, and improved OGD strategies -although making these final connections to accountability and policy making are seldom fully achieved.

The case also highlights important enablers and barriers to substantial realization of an integrated OGD ecosystem. Enablers include at least two key elements. First, when the data content and characteristics match the interests of the user community, engagement seems more feasible and more productive. In the case, citizens and intermediaries were interested in understanding and giving feedback about specific local projects. Aggregated information about government spending, for example, would hardly be useful for meaningful analyses that could be directly used by policy makers to improve implementation or future programming of these specific public investments. The fact that OpenCoesione is dedicated to publishing data about development projects also helped the staff support efforts to collect feedback about them. All-purpose OGD portals seem too diffuse to offer this kind of support.

Second, proactive government strategies for stimulating use and re-use of OGD appear to improve both data quality and public engagement. Interaction between publishers and users stimulates interest in the content and quality of the data. In particular, involvement of communities and NGOs appears to stimulate local ecosystems of governmental and non-governmental actors working with the data. This involvement can enable new forms of collaboration, as the cases of Monithon and A Scuola di OpenCoesione demonstrate. In addition, active involvement of NGOs, associations and other stakeholders in monitoring activities is crucial to stimulate participation, especially when the realization of their own objectives also depends on the effectiveness of the public policies being monitored.

We also identified three main barriers. First, citizen feedback is greatly hampered by the absence of specific data and supplementary contextual information. For this case, information about project objectives and activities, underlying policy motives, decisions, contractors, results and output indicators -the elements of process transparency [START_REF] Shkabatur | Transparency with (out) accountability: Open government in the United States[END_REF] -were missing. The available data did not allow users to fully understand the chain of responsibilities among these diverse actors and therefore was simply not legible for local communities [START_REF] Picci | Reputation-Based Governance and Making States "Legible" to Their Citizens[END_REF]. This problem makes citizen investigations more difficult and less likely because specific skills and expertise are required not only to understand the published data, but also to retrieve additional information to put it in context. Consequently, effective civic monitoring seems to require expert support to obtain meaningful results. Without this kind of expertise, policy accountability and broad citizen participation and collaboration all suffer.

Second, the health of the ecosystem appears to depend heavily on the tenuous sustainability of civic technology initiatives and organizations acting as OGD intermediaries. In the case, intermediaries were sometimes supported by government or by NGOs created for other purposes. However, there were few such entities and their long-term economic prospects were usually dim. These infomediaries play a critical role in representing citizen interests or helping citizens represent themselves, therefore sustainable business models for this function, including a role for government, seem necessary [START_REF] Janssen | Infomediary business models for connecting open data providers and users[END_REF].

Third, and perhaps most important, is the absence of real public accountability mechanisms between government and citizens. This absence is a powerful barrier to systematic integration of citizen feedback in the policy cycle. In the case, neither the ESIFs regulations nor the national legal framework provided these mechanisms. Specific internal government processes to encourage and process feedback from the bottom-up were weak, infrequent, and often completely missing. While the rest of the ecosystem may be robust, this gap at the end of the policy process may be the greatest barrier to achieving the collaboration and accountability benefits promised by OGD.

Conclusion

The objectives of OGD programs include not only fostering innovation but also encouraging greater government accountability and civic participation in policy making. In this paper we used an exploratory case study of OpenCoesione in Italy to try to understand whether and how all of these purposes can be served by open government data programs. We presented the results in a preliminary integrated open data ecosystem model that comprises two interrelated cycles of influence that flow from OGD publication. One cycle addresses the innovation potential of OGD, the other addresses how OGD might support democratic values of participation and accountability. Our case analysis showed actors inside and outside government interacting in a complex open data ecosystem to pursue these diverse goals. The case study emphasized the importance of intermediaries who represent a crucial link between data providers and the ultimate beneficiaries of OGD products. In the case of innovation, intermediaries seek to provide information-based services to interested consumers. In the case of participation and accountability, intermediaries provide expertise in analysis and a variety of other domains that puts data in context for ordinary citizens and helps them communicate their views to policy makers and administrators. We also found that the weakest link in the ecosystem is a lack of effective mechanisms that channel citizen feedback into the policy process.

This work is only a first effort to understand the interdependencies among the multiple goals of open data initiatives. In future research, we intend a) to expand the Italian case study to include perspectives from other data intermediaries and users such as local authorities and NGOs, the media, teachers and students involved in the civic monitoring activities and b) to apply the model in additional case studies (such as different EU countries in the same policy context) to improve its usefulness and generalizability.

Fig 1 .

 1 Fig 1. Integrated OGD ecosystem for innovation, collaboration and accountability