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MODULE 7

INTRODUCTION TO DATA COLLABORATIVES



In the Data Collaboratives Learning Module, we’re going to look at the sharing of and access to private sector data, and at how you can use such data to better define and understand a problem you’re trying to solve. Specifically, we’re going to explore the exchanges of data and talent between the public and private sectors through what’s known as a data collaborative. By the end of this module, we hope that you will: (1) understand what a data collaborative is and its value for solving public problems; (2) be able to describe the incentives for private companies to collaborate with other sectors in the public interest; and (3) know the process for establishing a data collaborative and the key considerations involved.

Download the worksheets for this module here.

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SELF ASSESSMENT
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PRACTITIONER INTERVIEW

Using Data Collaboratives for Problem Solving


In this interview, Stefaan Verhulst, Chief Research and Development Officer at the Governance Lab at New York University, describes data collaboratives, their impact on understanding and solving public problems, and the key considerations that go into creating a data collaborative. Using an example, he illustrates how data collaboratives were used to investigate the intersection of urban mobility and gender, combining a wide range of datasets, including call detail records and high-resolution satellite data, in order to understand if and how gender plays a role in the way people move around megacities.

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ABOUT THE INTERVIEWEE

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STEFAAN VERHULST
CHIEF RESEARCH AND DEVELOPMENT OFFICER, THE GOVERNANCE LAB

Stefaan G. Verhulst is Co-Founder and Chief Research and Development Officer of the Governance Laboratory @NYU (GovLab), where he is responsible for building a research foundation on how to transform governance using advances in science and technology. He is the Curator and Editor of the Living Library and The Digest. Verhulst’s latest scholarship centers on how technology can improve people’s lives and the creation of more effective and collaborative forms of governance. Specifically, he is interested in the perils and promise of collaborative technologies and how to harness the unprecedented volume of information to advance the public good. Before joining NYU full-time, Verhulst spent more than a decade as Chief of Research for the Markle Foundation, where he continues to serve as Senior Advisor. At Markle, an operational foundation based in New York, he was responsible for overseeing strategic research on all the priority areas of the Foundation, including: transforming health care using information and technology, re-engineering government to respond to new national security threats, improving people’s lives in developing countries by connecting them to information networks, developing multi-stakeholder networks to tackle global governance challenges, and changing education through information technology. Many of Markle’s reports have been translated into legislation and executive orders and have informed the creation of new organizations and businesses.

READINGS

Data Collaboratives: Exchanging Data to Improve People’s Lives

Stefaan Verhulst and David Sangokoya

The GovLab

2015

The essay refers to data collaboratives as a new form of collaboration involving participants in different sectors exchanging data to help solve public problems. These collaborations can improve people’s lives through data-driven decision-making; information exchange and coordination; and shared standards and frameworks for multi-actor, multi-sector participation.

Read the full article here.

Creating value through data collaboratives

Bram Klievink, Haiko van der Voort, Wijnand Veeneman

Information Polity Journal

2018

Driven by the technological capabilities that ICTs offer, data enable new ways to generate value both for society and for the parties that own or offer the data. This article looks at data collaboratives as a form of cross-sector partnership to exchange and integrate data and data use to generate public value. The concept thereby bridges data-driven value creation and collaboration, both current themes in the field. To understand how data collaboratives can add value in a public governance context, the authors exploratively studied the qualitative longitudinal case of an infomobility platform. They investigated the ability of a data collaborative to produce results while facing significant challenges and tensions between the goals of parties, each having the conflicting objectives of simultaneously retaining control whilst allowing for generativity. Taken together, the literature and case study findings help us to understand the emergence and viability of data collaboratives. Although limited by the study’s explorative nature, the authors find that conditions such as prior history of collaboration and supportive rules of the game are key to the emergence of collaboration. Positive feedback between trust and the collaboration process can institutionalize the collaborative, which helps it survive if conditions change for the worse.

Read the full article here.

A Decision Model for Data Sharing

Silja M. Eckartz, Wout J. Hofman, Anne Fleur Van Veenstra

International Conference on Electronic Government

2013

This paper proposes a decision model for data sharing of public and private data based on literature review and three case studies in the logistics sector. The authors identify five categories of the barriers to data sharing and offer a decision model for identifying potential interventions to overcome each barrier: Ownership, Privacy, Economic, Data Quality, and Technical.

Read the full article here.

The Potential of Social Media Intelligence to Improve People's Lives

Stefaan Verhulst and Andrew Young

This report explores the premise that data—in particular, the vast stores of data and the unique analytical expertise held by social media companies—may indeed provide for a new type of intelligence that could help develop solutions to today’s challenges. In this report, developed with support from Facebook, the authors focus on an approach to extracting public value from social media data that they believe holds the greatest potential: data collaboratives. Data collaboratives are an emerging form of public-private partnership in which actors from different sectors exchange information to create new public value.

Read the full article here.

SELF-ASSESSMENT

QUESTION 1

True or False

To get access to private sector data that can be helpful in solving a public problem, government’s first choice should always be to regulate that companies provide it to government.



Correct! A key advantage of data collaboratives is that they create mutually beneficial arrangements that incentivize the voluntary sharing of private sector data.

Sorry, that’s incorrect.

QUESTION 2

Choose the correct answer.

The sharing of data by the National Institutes of Health, the U.S. Food and Drug Administration, biopharmaceutical companies and non-profit organizations to create better therapies for patients is an example of what kind of data collaborative?





Correct! This is a common type of data collaborative where corporations and other important dataholders group together to create shared data resources via “data pools”. With so much data being generated and held by the private sector - such as online purchasing, web searches, mobile phone records and satellite location data - they can be a powerful resource for solving a public problem.

Unfortunately, that’s not correct. Hint: It’s the simple act of sharing data that defines this type of collaborative.

QUESTION 3

Select all that apply.
There are several types of data collaboratives, including:





That’s right! Generating insights and improving their reputation can be powerful incentives for private companies to participate in a data collaborative but they are not what we would describe as a type of data collaborative. In contrast a), c), and d) ARE types of data collaboratives focusing on building technical device (e.g. a dashboard or an app), supporting software product development and data analytics, and academic research, respectively.

Sorry, that’s not correct. There’s one here that’s not a type of data collaborative but is better described as an incentive for participating in a data collaborative - the rest ARE types of data collaborative.

You're right but there's more to this answer. Please try again!

QUESTION 4

Select the best answer.

When potential partners in a data collaborative are wary about the risks of sharing data with others they may not know well, the type of data collaborative that can be a good solution is a ___________ data collaborative.





That’s correct! In this type of data collaborative corporations share data with a limited number of known partners. For example, we discussed Social Science One, which brokers academic researchers access to Facebook data.

Sorry, that’s not correct. That may be a feature of this type of data collaborative though. Hint: We’re looking at the level of confidence owners and users of data might have in each other and how we might remedy circumstances where that discourages collaboration.

QUESTION 5

True or False

Data collaboratives are public-private partnerships directed at addressing public challenges. As such, under no circumstances should data from private sources be acquired by means of payment to private sector entities.



That’s right! Paying for data can be a legitimate means to incentivize private sector participation in a data collaborative, especially in business to business and business to government arrangements. However, we also need to be mindful of available resources and we should abide by the principles of openness and transparency whenever we pay for data.

Unfortunately, that’s not correct.

You're right but there's more to this answer. Please try again!

QUESTION 6

Choose the correct response

When corporations donate data to help governments, international organizations and NGOs solve problems more effectively, it can be considered a form of corporate _________.





That’s right! Building new partnerships around data sharing to derive value from socially responsible behavior, similar to that sought through traditional philanthropic efforts, can be an important incentive for some corporations to participate in a data collaborative.

Unfortunately, that’s not correct. Hint: Sometimes corporations can benefit from simply doing a public good.

You're right but there's more to this answer. Please try again!

QUESTION 7

Choose the correct answer.

While sharing data for public good may help to attract new customers, we shouldn’t forget other important reasons that some companies are interested in data collaboratives, such as access to ________ skills and _________retention.





Correct! The benefits of a data collaborative can include access to technical skills in organizing and analyzing data while also giving employees the opportunity to work on projects they can find more meaningful and of personal interest.

Sorry, that’s not it. Hint: We’re looking for skills specific to a data collaborative.

You're right but there's more to this answer. Please try again!

QUESTION 8

Choose the best answer

Once we have determined that a data collaborative could be of value in helping to address the public problem we’re trying to solve (the demand side of the initiative) we can start assessing the “supply” side by conducting a data _______.





Correct! A data audit can help you to understand how useful the data you already have access to is and will inform your search for external data sources (i.e. data you need but don't have). Another consideration is what data science skills you have or don’t have. When your assessment of supply is complete you can see if there’s a suitable match with the demand side, which will indicate if your data collaborative can be effective.

Sorry, that’s not correct. Hint: First we need to understand what data we do and don’t have.

You're right but there's more to this answer. Please try again!

QUESTION 9

Choose the correct answer.

Sam is working to establish a data collaborative associated with a major road project. The data collaborative is aimed at better understanding both freight and commuter routes, prevalence of light vs heavy vehicles at different hours, etc. To date, Sam has confirmed that a data collaborative can make a vital contribution by bringing together data and data science skills from in-house and industry partners who are all on board with the initiative. As a group they have identified data sources they will use. The next step Sam and the group should take is:




Correct! One of the key steps in setting up a data collaborative is to identify risk, plan to mitigate it, and make provision for this in your budget. Then you’re ready to outline the steps you will take in an implementation memo and prepare a communication strategy.

Sorry, that’s not correct. This step comes soon after.

You're right but there's more to this answer. Please try again!

QUESTION 10

Select all that apply.

As part of getting a data collaborative up and running it’s important to build in opportunities to assess the impact it is having and improve it over time. This can include:






That’s right! All of these are important things to consider - and focusing on them can you help to maintain existing and/or secure new funding for the work.

Sorry, there’s more to this answer.

You're right but there's more to this answer. Please try again!