The Businesses of Open Data and Open Source : Some Key Similarities and Differences

Open source and open data both have a focus on "openness", and most developers and researchers could easily identify similarities between the two phenomena. For example, both open source and open data are enabled – or at the very least greatly helped – by the Internet, which provides a backbone for collaborative development efforts, communication infrastructure, as well as a means to support the sharing of both application and data. However, open source and open data are distinct phenomena with significant differences, and these differences clearly impact how commercial success can be achieved in each domain.


Introduction
Open source and open data both have a focus on "openness", and most developers and researchers could easily identify similarities between the two phenomena.For example, both open source and open data are enabled -or at the very least greatly helpedby the Internet, which provides a backbone for collaborative development efforts, communication infrastructure, as well as a means to support the sharing of both application and data.However, open source and open data are distinct phenomena with significant differences, and these differences clearly impact how commercial success can be achieved in each domain.Given that TIM Review readers tend to be more familiar with open source than open data, our goal is to explore the concept of open data through a comparison with open source and with an emphasis on the similarities and differences that are relevant to technology businesses.We focus on three key questions: 1. Are the phenomena similar? 2. Are the licenses of software and data similar?3. Are the businesses and revenue models similar?Understanding these two phenomena is useful to managers and entrepreneurs interested in the business po- The structure of the article is as follows.We first describe key characteristics of open source and open data.We then compare these two phenomena from three business-oriented perspectives: licensing, commercial aspects, and relevant actors.Finally, we provide some takeaways for managers and entrepreneurs.

Comparing Open Source and Open Data
In computer science, in theory as well as in practice, the distinction between data and application is critical.Therefore, the most obvious and fundamental difference between open data and open source is that the former focuses on the data and the latter focuses on applications.
Data has multiple meanings, including any endproduct of measurement, but in this investigation, we use a slightly more technical definition of data: data refers to stored symbols.Data is considered a resource -raw material for the application.Open data means data that is technically and legally made available for re- Open data and open source are phenomena that are often automatically grouped together, perhaps because they share the word "open".A careful analysis of what open means in each of these cases is a stepping stone towards building viable businesses around both open source applications and on open data.Although there are, indeed, elements they share through their openness, the ways in which they differ are significant.In this conceptual paper, we aim to outline the differences and similarities of the two phenomena from a commercial perspective.
It's difficult to imagine the power that you're going to have when so many different sorts of data are available.To summarize: the first significant difference between open data and open source is that of data versus application.Data can be numbers, locations, names, etc.In and of itself, data does nothing.Source code, or rather an application, is something that uses or produces data.These two aspects, although they rely on one another for their significance, are different in both essence and purpose.Indeed, it is some of these differences that this article seeks to point out and clarify.

Comparing Licensing Aspects
The For open data, a similar "access principle" provides access to the data (and metadata) and it provides the opportunity to reuse it in applications.Data also needs to be maintained and updated.The actor that collects or mashes up the data from different sources usually has the option to stop providing access or maintenance to the data (i.e., to "close" the data).
Source code can be copyrighted (or copylefted: tinyurl.com/qygb2)but, in some cases, data falls outside copyright protection.Whether particular data can be copyrighted is subject to national legislation.However, copyright is not the only law that applies to data; depending on its content, other laws may also regulate its use.Laws may govern the collection, storage, maintenance, access, use, and representation of data.For example, laws relating to sensitivity, privacy concerns, or national security may apply to different datasets.In contrast, the business of open data is a young field, but it holds promise for service innovation.Discussion on the viable revenue sources is still ongoing.The data publisher or the user of the application pays the costs related to the collection, maintenance, and enrichment of data, but customers normally do not pay subscription fees for accessing data.
In the following subsections, we compare the expenserelated and income-related considerations of businesses that rely on open data or open source .

Who pays the bill, and why?
Open source can save firms money if they are able to attract free community participation.However, companies may also be willing to support open source development, for example by paying a developing group or foundation, or by assigning its own developers to an open source project.Even though anyone, even the firm's competitors, can then benefit from any improvements to the code, this approach is common in open source development.Typically, firms use this strategy to develop aspects of their product offering that would be considered "table stakes" (tinyurl.com/5u4aut).By collaborating -even with competitors in the same or similar markets -to develop non-unique foundational aspects of an application, companies save time and development effort, which can then be redirected into developing the aspects of their offering that will differentiate them from their competitors.
In addition to the costs of collecting data, open data providers must often spend money and effort both to clean up the data for publishing as well as for keeping it open.With such tasks, providers may benefit from community participation, just as in the case of open source software.Issues related to "community management" are therefore similar in both cases.

Who makes money, and how?
Openness usually means that an application or dataset can be acquired free of charge.In the case of open data, the publishers are normally considered to have given permission for others to build services on top of their released dataset.The services provided by these other parties may add value above and beyond just the provision of data, and the costs of designing the applications, collecting the data, and maintaining the services are covered by various different arrangements depending on the motivations of the other parties, their possible business models, and the nature of the value created.
Value can stand for both economic value (i.e., money) or a wider benefit.The openness of both open source and open data can potentially offer either one of these two types of value.However, it is notable that the dynamics that produce value are different.In Table 2, we list some of the benefits perceived by the key actors in each case.The table is not exhaustive list, but it provides an illustration of topical issues.
One of the main differences in the business aspects of open data and open source is that, at least currently, it is rare for the data provider to make money on open The Businesses of Open Data and Open Source: Some Key Similarities and Differences Juho Lindman and Linus Nyman data.The main perceived sources of benefits are related to public service or to situations where the data needs to be collected and maintained anyway.Releasing the datasets would then enable others to benefit from them and may result in new and useful services.Typically, the funding for the collection and maintenance of the data in such situations also comes from public sources.Normally, a company waives the possibility of data sales when it decides to release a dataset.However, we speculate that open data could also be accompanied by a "premium access" option, meaning access to more real-time data, faster access, or access to datasets that contain both open and closed data.
For open source, the commercial actors have established business models that rely on, for example, duallicensing.Open source has tried and true ways to cut costs and evade lock-in situations.However, many open source contributions are driven not solely by commercial interests, but by the desire for useful software that addresses specific needs, among other motives.
Open data can offer opportunities for downstream service provision.In such situations, some actors that provide open data might be keen to share their costs with the downstream actors that make a profit.It is possible to sell downstream applications, such as closed source software subscriptions.Developers might also have other motivations in writing software that uses open data, such as increased transparency, new visualizations, service provision, etc.

Comparing Elements and Actors
In comparing the elements and actors involved in open data versus open source, we limit our investigation to the four questions illustrated in Figure 1: 5. What national and international legislation poses obstacles to the service?Whereas software is relatively free of these concerns, many datasets may contain sensitive information.Open datasets can also be combined with other (also private) datasets, and this combination may raise new legal issues.
tential of the released data sets.This understanding is also useful for open data proponents who are interested in the business aspects of open source and the lessons that the business of open source offers.Designers of related services may be interested what potential open data and open source have to offer in terms of novel and better service opportunities.

6.Figure 1 .
Figure 1.Comparing actors and activities of an open data versus open source project

The Businesses of Open Data and Open Source: Some Key Similarities and Differences
open, and are there degrees, or types, of openness?For open source software, the openness primarily means a guaranteed access to the application's source code as well as an arrangement that makes sure that the code can be forked, modified, and redistributed.(For more on the significance of the right to fork, see Nyman and Lindman [2013; timreview.ca/article/644].) key similarity between open data and open source lies in the prerequisite of openness.But what, exactly, is it that is

Table 1
compares different licensing aspects of open source versus open data.The legal arrangements (i.e., copyright, licenses, original publisher, and role of contracts) for the two phenomena are different.For a more thorough discussion on data licensing, see the "Guide to Open Data Licensing" (tinyurl.com/lkhg6df),which is published by the Open Definition project.

Table 1 .
Licensing of open data versus open source www.timreview.caTheBusinesses

Table 2 .
Examples of key value sources in open source versus open data www.timreview.ca

of Open Data and Open Source: Some Key Similarities and Differences
The output is also different: in open data, the data ultimately remains the same through the process, whereas the open source development process aims to change the software.The software is an end in itself, whereas the released open dataset is just the first step in providing the service to the customer.Do you have sufficient familiarity about the dynamics of open data ecosystems and the required technical capabilities?Open data is a significantly different field from open source; in-depth knowledge of one does not automatically guarantee sufficient knowledge of the other, although open source experts will easily find similarities.2.If the data is not yours, how certain are you that it will continue to be provided openly in the future?How can you safeguard the relationship between the data collector/maintainer?
The BusinessesJuho Lindman and Linus Nyman Insights for managers and entrepreneurs When evaluating business models based on open data, managers and entrepreneurs should consider the following key questions: 1. 3. What is the legal status of the data?Does it allow fees, and what would happen if fees were introduced? 4. What is the license of the software application?Is it possible to gain software subscription revenue from a proprietary application built on the open data stack?