Resources

This web article focuses on the administrative side of a data stewardship program. It is in a listicle format and provides that there exist 5 considerations that need to be taken into account when determining what the reporting structure within a data steward framework.

The article concludes by providing that an organization should want the data steward to report to a person within their organizational structure that is:

• Influential within the organization – regardless of where they are in the organization’s hierarchy
• Has access to resources
• Understands the value of data and its management
• Dedicated and spends time to provide the necessary support.
  The 5 consideration just elaborate on how an organization can implement this during the designing phase

This whitepaper lists out the dichotomy of data stewardship taxonomy, i.e. Is it a business function or an I.T function? This paper breaks down the elements of successful data stewardship programs across sectors and prescribes the factors that companies and organizations should consider before launching a data stewardship program.

The USP of this white paper is that it is based on actual working examples with clients to formalize data governance programs and to institute data management best practices, we have formulated five primary models for data stewardship.

5 such models have been prescribed and each is unique, with its own benefits and risks. Each represents a deliberate approach to launching a data stewardship program that can meet the company from its decision to incept such a program.

This is a blog post that sets out the role and responsibilities of professional data stewards in an interview format. Since the interviewee is a data stewards in a university he has elucidated on the topic “what is a Data Steward (DS), and how does the role fit within a library?”

The blog gives some insight into the project, as well as his advice for other organizations looking to take on Data Steward Roles. The post also highlights the importance of the Data management plan before launching any stewardship program.

These public-private data sharing collaborations require data governance rules. Data governance addresses many barriers. Based on a literature review of data governance and three use cases for data sharing in the logistics sector, the paper presents a data sharing decision model from the perspective of a data provider. The decision model addresses technical as well as ownership, privacy, and economical barriers to sharing publicly and privately owned data and subsequently proposes interventions to address these barriers. The decision model is useful for identifying and addressing data sharing barriers as it is applicable to amongst others privacy and commercial sensitive data.

Although not an academic work, this site is very elucidative on how to develop a data stewardship framework. It lays out what should be the development approach, framework elements, and also gives out a toolkit.

This Communication puts forward a European data strategy whose ambition is to enable the EU to become the most attractive, most secure & most dynamic data-agile economy in the world – empowering Europe with data to improve decisions and better the lives of all of its citizens. It enumerates a number of policy measures and investments needed to achieve this goal.

This strategy has 4 key pillars:

1. cross-sectoral governance framework for data access and use;
2. Investments in data and strengthening Europe’s capabilities and infrastructures for hosting, processing and using data, interoperability;
3. Empowering individuals, investing in skills and in SMEs;
4. Common European data spaces in strategic sectors and domains of public interest.

Unlike the current ‘one size fits all’ approach to data governance, there should be a plurality of Trusts, allowing data subjects to choose a Trust that reflects their aspirations, and to switch Trusts when needed. Data Trusts may arise out of publicly or privately funded initiatives. By potentially facilitating access to ‘pre-authorised’, aggregated data (consent would be negotiated on a collective basis, according to the terms of each Trust), our data Trust proposal may remove key obstacles to the realisation of the potential underlying large datasets.

Stakeholders in the monogram publishing industry face challenges in moving beyond reporting that focuses on print sales toward capturing and articulating the value of investments in open access (OA) monographs in the context of users who engage with their books across multiple sites and formats. However, data about how OA books are being used may include sensitive commercial information as well as information about users that must be handled carefully in order to safeguard privacy.

his paper develops a case for the need for action and a description of the landscape, and we propose a ‘community data trust’ as a way forward for the monograph community laying the groundwork for a community approach to handling data about OA books. In this endeavor, it stresses how for a Successful collaboration for thoughtful engagement with issues of trust, the development of shared technical standards, and the development of requirements for the validation of data and information will be required.

“This is a Digital Action Roadmap for Canad for making Canada a Competitive, data-driven, digital economy. Through this engagement process Canadians came together to share their vision for a competitive, inclusive, digital, and data-driven Canada in three areas:

1. Skills and Talent: Preparing for the Workplace of the Future
2. Unleashing Innovation: Supporting Growth of Competitive Canadian Companies
3. Privacy and Trust: Making Canada a Leader in the Digital Age

This document recognizes that not all data is created equal and there is no one-size-fits-all model for data, and that different approaches to data management should be considered that all respect Canada’s core values including a right to privacy and stresses the need for:

1. Clear and Responsive Marketplace Frameworks;
2. Putting Data to Use for Canadians; &
3. Security

There is a need for a more holistic,
iterative and outcome-based understanding of public-private data collaboration. The findings point to five areas for leaders to focus upon to strengthen trust. Leaders need to:

1. Ensure that all relevant stakeholders are committed to shared outcomes.
2. Operationalize the principles of responsible data governance;
3. Deliver insights that are achievable, accurate, fair and explainable
4. Support both senior leader decision-makers and front-line users with the skills and resources to use data; and
5. Establish sustainable economics to ensure long-term impact.

This post highlights the benefits of managing data concerns through the use of Data trusts. It defines Data Trusts and lists out the advantages and benefits of using such a model.

Data Trust is then complemented with the legal principle of Trust and trustee and then the post addresses data issues and how fast trust can be helpful in solving these from a clinical study perspective.

This report, represents efforts with business, government, civil society leaders, experts and practitioners to advance public-private data collaboration to address some of the world’s most pressing humanitarian and sustainable development challenges.

The report also provides a holistic governance framework designed to strengthen trust, balance competing interests & deliver impact. It offers insights to balance both the need to innovate in the use of data and the mandate to protect vulnerable populations against known and emerging harms.

Our framework uniquely highlights the fact that building an effective data commons requires consideration of an additional broader set of societal and institutional layers that ensure interpretation of such repositories for the social good — what we call the broad data commons.
Human knowledge feeds into the development and preservation of the other layers, and whose inclusion and education can correct, enhance, and supplement them. The addition of three layers establishes the broad data commons, which encompasses a full range of applications and audiences. 

“This paper starts by exploring relationship between data ethics and legal compliance, some existing data ethics frameworks and ethical considerations in data collection sharing and use. After this exploration – and considering the challenge of aggreing practical data ethics frameworks for sectors, society or even globally- this piece offers the approach of Data Ethics CAnvas which is an organization to manage data ethics considerations.”

This study portrays the current mood among German companies with regard to the extent to which data are already being shared between companies in Germany and the requirements, attitudes & conditions which the idea of Industrial Data Space has to face up to.

executives from 210 large enterprises, small and medium-sized companies & institutions throughout Germany were asked to participate in this survey and spontaneous reactions to the concept of Industrial Data Space were collected from within the companies.

The evaluation of these interviews points that the Data exchange between companies as an essential feature of digitization and data economy and then the piece points out essential features, challenges & a standardized approach for industrial data space.

This article examines both the concept of data stewardship as well as the considerable legal barriers to data access and use that can exist, even when stewardship is present, in the context of better delivery and research on healthcare.

The article tells that Experts have posited that health data stewardship necessitates entities that acquire, hold, and aggregate information, releasing it for use in research. Stewardship of health information data compels ‘‘trust and competency; adoption of technology; and new models for data exchange (and new skills for managing health information) that include the patient as part of the data supply chain.’’

Health data stewardship rests on critical assumptions: first, that it is possible to gain access to data; second, that data stewardship will deal with the identifiable patient and provider information; and third, that research protocols and technology exist to enable the safe and secure use of personal health data, such as research protocols that avoid the creation of large, static databases susceptible to leaks or tampering.

The article provides a transnational overview and a comprehensive analysis of the legal situation in Europe. It discusses why data ownership is widely perceived as a problem in Europe and how this perception can be overcome by a fundamental property law approach. Taking into account economic realities, the paper argues that European property law provides a sufficient framework for establishing a theoretical concept of data ownership. Thereafter, draft the dimensions of a data ownership concept have been drafted in this piece by proposing potential criteria for assigning ownership and analyzing both positive access and negative restriction rights.

This is not a single reference piece instead the link redirects the visitor to a repository of articles on various articles of interest on recent issues in Data governance including Data Stewardship and other Data-driven fields for solving the contemporary global challenges

Data collaboratives are an emerging form of public-private partnership, in which information held by companies (or other entities) is shared with the public sector, civil society groups, research institutes and international organizations. These entities — a new form of collaboration for the data age — have now been tried out across sectors, and around the world.

This web resource actually excerpts from the training session in the US Forest Service on the topic of Data Stewardship. This has been curated from the point of view of ‘would be’ data stewards but it contains some very interesting insights for the designing stage as well.

Particularly the data Inventory section provides an idea of how to go about managing data steward models for forestry data.

This is actually a 10 part article which is adapted from the book “Universal Meta Data Models” by David Marco & Michael Jennings, John Wiley & Sons.

This was written in 2005 but it tracks (over its 10 parts) the basic framework that is required to develop an efficient data stewardship model. All the 10 links of the article can be accessed from the web-portal in the above link.

In this series of articles, the author has presented the Data Stewardship Framework. Further, it is clearly mentioned and a disclaimer is provided in the post only that since no two data stewardship groups are exactly the same, therefore the article is meant only to be a guideline to how to form data steward groups. Further, the framework mentioned in the 10 article series is basically designed to provide corporations and government entities with the strategies and guidelines necessary to implement a highly successful data stewardship organization.

This article takes a little divergence from the ex-ante designing of the stewardship program. Instead, it focuses on ex-post and during the program performaոce analysis. This article focuses on briefly identifying different types of measures that an enterprise may consider when developing their data stewardship programs and measuring the success of their data management programs.

Although the article mentions 2 factors in reference to the ability to implement metrics for a data stewardship program –

1. The enterprise’s willingness to consider permanent (longer-term) value alongside the immediate measurable benefits of the programs and
2. The enterprise’s ability to attribute the improvements in information-based capabilities to the action of the stewardship program

The second factor (Attribution Analysis) is not the focus of the data stewardship performance measurements described in the above article.

This is actually a book with individual chapters available on the link above. The chapters lucidly deal with topics like “Implementing Data Stewardship”. “Measuring Data Stewardship” & “Practical Data Stewardship”. The DOI link of individual chapters are also hosted on the above link.

The resources from these books act as a step by step ‘Cook-book’ for addressing various facets of a Data Stewardship programme.

To realize the potential benefits of data for our societies and economies, we need trustworthy data stewardship.

In this piece, the author discusses the ODI’s ongoing work to test data trusts as an approach to stewarding data, and some of the recent developments in the field.”

This article considers various stakeholder claims to data ownership and the value generated by data, through a political economy lens. Following a data value framework established by the Open Data Institute (2019), It has been first considered how data generates value from the point of its creation, how data as a resource imbues various stewardship obligations onto data controllers, and finally how – given competing interests – decision-making authority is apportioned across stakeholders. This analysis is then applied to three emerging models of data ownership: Laissez Faire, Data Trusts and Data Commons. The structural qualities of each model are revealed by an in-depth critique, before a visualisation of the data flows between stakeholders is offered. Finally, comparisions have been made with these models across categoric issues that emerge from this analysis, considering how each model tackles issues such as incentives, competition, innovation and feasibility.

Governments, industry, trade unions and civil society should collaborate to pilot data trusts in order to improve upon the consent-based model of privacy, especially in sectors where an absence of competition has left consumers with no viable alternative.

All actors involved in AI – data consumers, data providers & data subjects – have trust issues which data trusts need to address. Furthermore, it is not only personal data that creates trust issues; the same may be true of any dataset whose release might involve an organization risking competitive advantage.

– Data trusts are intended to define a certain level of trustworthy behavior for data science. The appropriate form of trust is based not on rules, but on social license to operate.
– Data trust must generate meaningful ethical codes for its members. to constrain all who operate it.
– A data trust is not a data store.
– The appropriate set of beneficiaries will depend upon the set of agents whose trust is to be solicited by the data trust.

In this article, the author put forward the concept of data trusts as a way to assert some control over the digital utilities.

The post mentions how Using Data Trusts offers the chance of a greater say in how data is collected, accessed and used by others. This goes further than limiting data collecting and access to protect of privacy; it promotes the beneficial use of data, and ensures these benefits are widely felt across society

This paper provides guidance on establishing a data stewardship program for administrative data. It clarifies the different types of data stewards and managers and their roles and responsibilities, where they reside in an organization, how they work with colleagues to ensure that the data are maintained, and what special skills or training are needed to meet both university responsibilities and best practices.

This paper is written in US context and mentions this facts by pointing out that US Data steward models need to be in-sync with US Statutes:

1. FERPA: The Family Educational Rights and Privacy Act serves as the basis for protecting educational records.
2. HIPAA: The Health Insurance Portability and Accountability Act is key to working with clinical and health insurance data.

Based on a literature review of data governance and three use cases for data sharing in the logistics sector, this paper presents a data sharing decision model from the perspective of a data provider. The decision model addresses technical as well as ownership, privacy, and economical barriers to sharing publicly and privately owned data and subsequently proposes interventions to address these barriers. The paper makes a case that the decision model is useful for identifying and addressing data sharing barriers as it is applicable to amongst others privacy and commercial sensitive data.

This is a guide developed by The European Medicines Agency policy on the publication of clinical data for medicinal products. It deals with aspects like anonymization of Clinical Data and Patient Data. Further this guide tells abut the the iinformed consent procedures as per Eurpoean Union regulations.

This article focuses on the general concept of Fiduciary Relationship and defines it as one of the most fundamental legal relationships, and its importance for both public and private law is increasingly recognized.

It then goes on to show how many public and private fiduciary institutions are best understood as being administered on the basis of governance mandates. The piece is aimed at providing important new insights for core issues in corporate law, administrative law, and constitutional law, among other fields.

Global Fishing Watch visualizes, tracks, and shares data about global fishing activity in near-real-time and for free via their public map. To date, the platform tracks approximately 65,000 commercial fishing vessels globally.

Based on the typology of data collaborative practice areas, Global Fishing Watch is an example of the data pooling model of data collaboration, specifically a public data pool. This approach enables the data stewards and stakeholders involved in Global Fishing Watch to bring together multiple data streams from both public- and private-sector entities in a single location.

This piece then elucidates about the activities of Global Fishing Watch and the outcomes, impact & challenges in its methodology.

Data collaboratives offer a way around this limitations of private ownership and access restrictions, untapped potential. They represent an emerging public-private partnership model, in which participants from different areas , including the private sector, government, and civil society , can come together to exchange data and pool analytical expertise in order to create new public value.

The article by ODI talks of planning and budgetary estimations required for implementing an Open Data initiative and is based on a case study done in 2010 by the UK Government wherein it was decided that individual payments by each ministry would be published as open data.

Talks about 4 stages of such a data initiative – Preliminary stage, Development Stage, Roll out stage and Business as usual stage.

Disclaimer: It does not give express budgetary figures, rather it points towards ways in which such budgetary figures can be calculated. Since these are general principles, they can be applied on other forms of data steward tools also.

This Essay explores the trust’s potential as a data management tool. It argues that trusts can support delegated management of secondary uses for clinical data; long-term data governance that protects research subjects; complex value allocations among members of a data collaboration; and the isolation of data from a researcher’s insolvency risks. Although this Essay uses health data as an animating application, the functions presented here may be applicable to other fields.

This blog post is centered around the 2014 declaration by the company Ello where it had then announced that it promises to never sell user data in order to raise revenue.

This blog post contemplates how the model of Data Trust can be utilized to actually give realization to this corporate promise of Ello.

This piece talks about Account Aggregators i.e Data Sharing in Indian Context, particularly financial data. It talks about how although there is too much hype pf Data Aggregation, the final seamless implementation will prove even more difficult than it looks.

After explaining with the help of examples, the concept of Aggregation, this piece elaborates upon the existing framework of regulators that deals with similar structure and then enumerates the roadblocks that exist for seamless user experience with Data Sharing. This piece particularly stresses that there is a lack of clarity on privacy issues on both the consumer and regulatory front and that before launching a data-sharing model it is the need of the hour that these issues be clarified.

This is a vision document laying the roadmap for the Indian Digital Journey. It describes the Vision, the mission & the objectives of NDAP.

Then it lays out the challenges in this endeavor and then it goes on to lay its approach on how to solve those challenges. The key features of that approach are:

1. inspiration from the best platforms around the world,
2. building on the success of existing Indian data platforms,
3. a user-centric approach to providing access to data,
4. providing access to data from multiple sectors in one place,
5. becoming a reliable platform for up-to-date data,
6. relying on effective collaboration; &
7. transforming data-driven discourse, research, innovation, and decision-making in India

This document summarises round table discussion on Non-Personal Data, held in Delhi on November 28, 2019. The discussion saw participation from multiple stakeholders (lawyers, policy professionals, academics).

Definition of non-personal data and whether anonymisation is absolute emerged as key issues. Asserting intellectual property rights over data, especially NPD, and giving competitors access to NPD were also discussed extensively. Participants highlighted concerns about sharing NPD with government, and made recommendations that could help the committee of experts that is to come up with a governance framework for NPD.

This report by ODI strives to explain the legal issues and legal rationale behind the concept of data trusts. It then elaborates on four main problem areas that data providers face, and
which need to be accounted for if the data trust is to work. The 4 problem areas usually include:

1. Data protection and privacy law
2.Commercial confidentiality
3.Intellectual property rights; &
4.contractual obligations

Then the report also covers prospective rules for data users which as per the report needs to be legally binding, and must ensure that the rights and interests of the data trust, data providers & data subjects are respected. Then the report also covers enforcement and other governance aspects of these rules and data trusts in general.

Finally, although the report does not specifically recommend law reform to facilitate the use of data trusts, though it does suggest that attempting to reform trust law so that it could appropriately govern data trusts would be a long and difficult project if it is even achievable.

This document is an operational guide for Giving researchers the ability to collect and analyze data through online platforms that leads to innumerable positive impacts, including identifying and rectifying human rights abuses, enabling a greater understanding of the role of bots in influencing public opinion and supporting transparency into toxic and discriminatory online behavior.

It mainly talks about Four draft principles that have been created as a starting point are listed below, along with guidelines to enable implementation:

1. Multistakeholder Collaboration – PIRA ensures the appropriateness and effectiveness of the PIRA Principles by engaging members from academia, civil society, government, industry, intergovernmental organizations, and the public.
2. Members engage in a collaborative approach to the ongoing development, implementation, and oversight of the principles and operational guides.
3. Governance, Accountability, & Transparency – Members must adhere to a set of shared governance mechanisms that support accountability and transparency in data access and use for public interest research.
4. Responsibility – PIRA members must implement strategies to ensure responsible research practices, including implementation of robust evaluation processes to ensure that data access and use do not infringe upon the rights of data subjects; &
5. Data Privacy, Security, & Integrity – Members must put in place appropriate governance and technical strategies to support privacy, security, and integrity in data access and use for
   public interest research.

This blog post first talks about the theoretical importance of Data, Stewards, functions of data stewards &why it matters. It then brings to light what are the “Best Practices” of Data Stewardship.

Although the link that takes us to the portal containing the best practices in a detailed version is a member-only link (Data Governance Professionals Organization. “DGPO Best Practices Categories: Stewardship.” (Members Only Area). DGPO.com. 15 Mar 2020. – www.dgpo.org). It still is worth mentioning in this lit review.

Trusts are legal instruments that appoint a steward (trustee) to manage an asset for a purpose — such as conservation of land or maximizing value — on behalf of a beneficiary or beneficiaries who own the asset. Data trusts are legal trusts that manage data, or the rights to data. The largest technology companies in the world are increasingly face mounting instability as governments politicize ad hoc approaches to data rights and regulation. The shift is stressing the limits of organizational structures, which were not designed for data protection, let alone on a global scale. Five policy issues: Data rights valuation; jurisdictional harmonaisation; beneficiary definitions; license limitations; and Data diligence standards.

Data Stewardship embraces three main elements: · The direct responsibility to users regarding a data steward’s access and use of the data. · The secondary responsibility to users regarding access and use of the data by third parties. How does it protect data and its user’s rights once it relinquishes direct control and access of information to third parties? · The tertiary responsibility is to the data itself. How does the provider protect the data it holds? Does it disclose risk to users? Does it encrypt data?

“This whitepaper highlights key elements of NODEs and suggests design principles that can help realize their full potential to create transformative impact across sectors.

From the perspective of governance, this paper lists out 10 Principles for various things including Transparent Governance, Design of Delivery Platforms & Vibrant Community.

Of this, from a data governance point of view, Principle 8 is important as it specifically talks about Creating transparent data governance by Outlining data policies & standards on ownership,
contribution & consumption of data whilst ensuring that they are easily understood & readily available to all users and simultaneously Putting in place a set of mechanisms to enable enforcement of these and monitor adherence. “

This paper has dispelled some uncertainties and confusion about data trusts. In particular, it has been shown that the device of the trust, on traditional equitable principles of trust law, is a perfectly suitable vehicle for the management of data for both private and public purposes. Rights to data are suitable trust assets and the data protection law

In this piece, the concept of a data trust as one way to overcome the main challenges of data custody and to provide a way to make data available for the common good while safeguarding various privacies is presented.

Further, various constraints to this model and the components which have been relied on to describe how to navigate these constraints in light of a vision of a data trust, have also been discussed.

This article describes four foundational principles—Findability, Accessibility, Interoperability, and Reusability—that serve to guide data producers and publishers as they navigate around these obstacles, thereby helping to maximize the added-value gained by contemporary, formal scholarly digital publishing.

The ideas within the FAIR Guiding Principles reflect, combine, build upon and extend previous work by both the Concept Web Alliance partners, who focused on machine-actionability and harmonization of data structures and semantics, and by the scientific and scholarly organizations that developed the Joint Declaration of Data Citation Principles. The FAIR Principles are also complementary to the ‘Data Seal of Approval’ (DSA) in that they share the general aim to render data re-usable for users other than those who originally generated them.

The end result, when implemented, will be more rigorous management and stewardship of these valuable digital resources, to the benefit of the entire academic community.

The paper also expounds real-life working Examples of FAIRness- Dataverse, FAIRDOM, ISA, Open PHACTS, wwPDB, UniProt, etc.

This is an old paper (2004) and thus it serves as good learning from hindsight experience.

This paper attempts to show why the then-existing legal and extra-legal modes for the protection of privacy online were failing to protect consumers and promote consumer trust.

In the second part lessons from the crisis faced that time by intellectual property in cyberspace, particularly in reference to MP3s and peer-to-peer downloading are highlighted. Further, the paper also draws parallels from the solution devised by William Fisher of the Berkman Centre, Harvard, in the form of an alternative payment scheme for copyright holders.

Finally, the insights are drawn from Fisher’s work are combined with original proposals drawn from a comparison of the consumer-data collector relationship in cyberspace with the roles played by truster, trustee & beneficiary in the institution of common law trust. The resulting ‘modest proposal’ suggests that a ‘privacy tax’ be levied on the profits made by data collectors and data processors.

This piece talks about Data Collaboratives (DC) and what effect did they have on data sharing models. Speaking on success of Data collaboratives, the piece then talks about the vital importance of Data Stewards in DC’s success. The piece then veers off into defining what exactly is a Data Steward and what are the goals associated with this role. The post then talks about functions of Data Stewards (in an elaborate yet concise fashion) and then concludes by opening a dialogue about Data Sharing models, DC’s and role of Data Stewards in wider networking arena.

Data Stewards: “Individuals or teams that are empowered to proactively initiate, facilitate and coordinate data collaboratives when they may be useful or necessary.”

Functions of Data Stewards:

– Partnership and Community Engagement – Internal Coordination and Staff Engagement

– Data Audit, Ethics & Assessment of Value and Risk

– Dissemination and Communication of Findings

– Nurture Data Collaboratives to Sustainability Goals of Data Stewards:

– Collaborate with partners to unlock value of data

– Protect data under their control and influence

– Act to identify partners and opportunities

This report aims to initiate a dialogue for discussing on a framework to contribute to the live policy debate about data regulation and governance.

A key issue in this endeavor is to ensure that the benefits of the data revolution are captured as fully as possible but also that they bring wide social benefits, and do not become or remain a vector for the unequal distribution of wealth and power. Effective policy in this area will require understanding the distinctive characteristics of data.

Informed consent is often not required for studies that access anonymized and pooled data.
In theory, such data are no longer connected to a person. But in fact, risks remain. Many studies have shown that individuals can be identified within anonymized and aggregated data sets. Last week, researchers demonstrated how it is possible to re-identify people, even when anonymized and aggregated data sets are incomplete.
Vulnerable individuals and groups — including undocumented immigrants, political dissidents, or members of ethnic and religious communities — are at risk of being identified, and therefore targeted, through digital-data studies.

This report begins by stating how India is experiencing a personal data revolution and how much increment has been seen in this regrads within the country in recent years. Then it goes on to list out some associated inherent risk with such exponential data consumption rise without adequte redressal framework. In this endeavor the report first defines personal data and then also lists out the practices by corporates for acessing the user’s personal data.

Then pricaxy concerns (prvacy 1.0) are addressed in which the view of the satkeholders (Regulators & indivduals) is also presented. This is suceeded by a enumeratng orinciples of privacy and furthered by a call of action on part of stakeholders to take cognizance of the situation.

In a seperate vein the report also illuminates the view point on the other end of equation (i.e the Businesses) and tells us about the business models that these entities use and how does privacy fits into all this.

A tension exists between data generators and data collectors over who actually owns data is empowering movements such as #DeleteFacebook and raising questions of data’s influence in civil society.

In response to these observations, alternative models of data ownership have begun to emerge. This article has examined three: laissez-faire, data trusts, and data commons, through a data value framework proposed by the ODI. This article’s purpose is to elucidate the nuances at the heart of each model from a political economy perspective and reveal future avenues of research.