What Does it Mean to be an Innovative Public Organization?

The Basis for Innovation

Innovation comes in all different forms. From genre-breaking musicians, prolific writers of fiction, or even, to a certain extent, any kid who’s ever picked up a loose set of Legos and imagined a multi-colored and blocky airplane or skyscraper, though there is a clear divergence between creative and practical pursuits. Such is the definition of public sector innovation in Osborne and Brown (2005, as cited in de Vries, Bekkers, & Tummers, 2016, p.12) which defined public sector innovation as “the introduction of new elements into a public service—in the form of new knowledge, a new organization, and/or new management or processual skills, which represents discontinuity with the past.” Innovation can be understood both as a process, often focused on adoption and diffusion, and as specific characteristics, such as its newness (de Vries et al., 2016). With both discontinuity and newness at the forefront, there is commonality in innovation through being inspired enough to create something both abstract and new from existing things and things that have yet to be. Societal advancement, nevertheless, continually presents more complex challenges, and it falls on governments to adapt, though improving existing solutions isn’t always necessarily the ideal solution. Modern governments are beginning to look at innovation that prioritizes individual and social outcomes for policy approaches (OECD, 2017, p. 19).

From what has been seen during this course from a personal perspective, while broadly observing Smart City Data Hubs and on a specific level, Boston’s CityScore platform, governments, agencies, and other public adjacent entities are adept at instilling the principles of open innovation and external resources into public-facing innovations. As defined in Pedersen (2020, p.2), open innovation refers to a model in which organizations open their innovation processes and integrate both internal and external ideas and technologies to create value. As described by Mergel and Desouza (2013, p.882), “Open innovation encourages organizations to search for solutions outside their organizational boundaries to address core management problems.” Smart City Data Hubs broadly use the principles of open data to encourage abstract public analysis or even pointed internal analysis of data points collected by an entity. The Open Knowledge Foundation defines open data as “data that can be freely used, re‑used and redistributed by anyone – subject only, at most, to the requirement to attribute and sharealike.” (Open Knowledge Foundation, n.d.) Romualdo-Suzuki and Finkelstein (2020, p.1) discuss the inherent value of smart city data hubs, writing, “Many cities and local governments alongside technology organizations and research institutes have already done work to link smart cities solutions to policy goals and initiatives. The availability of large-scale cross domain city data has the potential to drive economic growth, improve government transparency, and create innovative new value-added city services.” The value in these services is inherent as its imaginable that any curated dataset would have value, however, generally, Smart City Data Hubs also feature openly available endpoints, which increase their value via the accessibility of their data to enable it to be molded into newer, more innovative forms, compounding the ability for innovation at multiple potential junctures. This isn’t anything new, as it bridges directly from the open source community, which directly benefits public sector entities, as according to OECD (2017), the challenges facing society are often too difficult for the public sector to solve alone. As Mergel and Desouza (2013) note, the implementation of open innovation can lead to many positive benefits, like more effective practices, increased trust, and improved awareness of social problems. According to OECD (2017, p.11), “Public sector innovation is about finding new and better means to achieve public ends.” For Donahue (2006), this only increases the value of public innovation as they write that, because of the vast public responsibility of public organizations, their entrustment with critical societal tasks, the value of innovation far outpaces the value of public sector organizations. At crucial times where budgets are scrutinized, needs are both critical and compounding, and governments are asked to do more with less. At times like these, innovation can seem both a solution and as frivolous, as Pedersen writes (2020, p.2) “open innovation might make public sector organisations ‘look cool’,” which to some budget hawks might not be an efficient use of public dollars if not justified thoroughly with quantifiable data. Pedersen further writes of a lag effect when attempting to quantify value creation. All in all, there is pressure on innovators to produce measurable value with their projects. 

Trends in Technology

A hallmark of modern technology is the exponential increase of its capabilities (Moore’s Law), which is outpacing both the diffusion and scholarship surrounding the impact of innovation and the adoption of new technologies and methodologies in public sector organizations (Pedersen, 2020; de Vries et al., 2016). However, as technology marches on, it is incumbent on governments to both adapt and innovate to maintain a high quality of service to their constituencies. OECD (2017, p. 12) writes, “Innovation that harnesses the power of data, information and knowledge has the potential to transform all sectors, and the public sector is one of the most data-intensive parts of the economy.” Further elaborated on by OECD (2017, p.215), Machine Learning is creating new frontiers in data analytics, which is finding innovative deployments within government organizations. Machine Learning is defined as using algorithms to let computers “learn” from data (OECD, 2017, p.215), which enhances the ability of a machine to make inferences and predictions regarding future situations. These systems, if employed by municipalities, would implement some form of a Smart City Data Center. Further, various governmental examples cited in the OECD all utilize some variation of a Smart City Data Center, whether it’s to predict the best time to sow crops, detect birth defects, or even identify tax evaders. New York City, for example, built a platform called DataBridge, which is a city-wide platform meant to share analytics across city agencies. Information that was previously siloed is now accessible via automated data feeds, which are being used to combat fraud, identify illegal housing conversions, and help crack down on pharmacies that are contributing to drug abuse. 

Choi and Chandler (2020) discuss that with the latest adoption of innovations and technology, a knowledge vacuum is created, which creates forces either pushing toward innovation exploration and adoption or pulling forces toward resistance and psychological safety. Internally, Smart City Data Hubs would be managed by teams with an already high level of technical aptitude; however, there is always a tendency to resist change in information technology if an organization isn’t prepared for a departure from the status quo. From my personal ancillary experience, I’ve met and worked with many an IT Director who was resistant to change for the simple fact that it was something different that could only increase their workload while doing nothing to decrease it. As such, cities and organizations have implemented strategies to help foster a culture of innovation and avoid the pulling forces of psychological safety. Following the deployment of DataBridge, New York City deployed an Analytics 101 course for its employees, which provided training on statistical and data management. It additionally trained employees on the newly made available data and tools (OECD, 2017, p. 219). Choi and Chandler (2020, p.7) similarly mention a case where enough training wasn’t provided where a rollout of a new Medicaid system caused employees to feel like they were responsible for mistakes that they weren’t making which was a direct result of the State Legislature of Hawaii not providing enough funding for both training and staff to supplement the new rollout. This, in turn, caused resistance from both state employees and the public to this new innovation.

As technology grows and changes, it’s essential to remember concepts like the knowledge vacuum as leaders set expectations for culture and the coming changes. Governments are increasingly expected to adapt, and as cities become smarter, more data will be collected, which makes it ripe for analysis. Systems will grow and change, and as OECD states, “learning is increasingly essential for public sector jobs” (OECD, 2017, p.82). Setting expectations for what is to come is thereby crucial for any organization that is expected to innovate or adopt new emerging technology in the near future.

The True Measure of an Innovative Public Organization

Given the basis for innovation and the emerging trends in technology, both from the perspective of advancement and adaptation, what, in this context, does it mean to be an innovative public organization? Innovation is a buzzword, much like “cloud,” “web3,” “blockchain,” the list goes on. Since the foundations of information technology brought us operating systems that made it easy to “point and click” and “surf the web” people have been hanging on to the latest internet trend whether its warranted or unwarranted. Whether it’s a company using machine learning and large language models to interpret heaps of structured and unstructured data, or a fly-by-night programmer who created a text box with 50,000 lines of “if/then” statements, both may be marketed as AI. Similarly, organizations can have redundant filesystems with strict security standards, which allow simultaneous editing of documents on the fly and be set next to a small entity which uploads Word Docs onto a server and both can be considered to operate “in the cloud.” As such, what does it mean to be truly innovative in the public sector if the gauge for technical aptitude is so skewed toward “whatever works?” 

After all consideration, to be a truly innovative public organization means to be prepared to meaningfully adapt processes and technologies to create public value by removing barriers, expanding capabilities, and enabling the diffusion or inspiration of improvements to other organizations. Going back to the original definition as posited by Osborne and Brown (2005, as cited in de Vries, Bekkers, & Tummers, 2016) I believe there is something to be said about “ discontinuity with the past” and being able to successfully navigate the integration of any policy or framework which runs up against existing practices to produce a new outcome that can be replicated across similar entities. Speaking from a governmental perspective exclusively, it’s admittedly interesting to see the various implementations of data hubs across a multitude of sizes of government. Whether it’s the national or city examples as cited in OECD, or my research with Boston’s City Score, what is most impressive is the divergence of solutions that are implemented across the board. Innovation, as it were, is not only about discontinuity but also about injecting “new elements into a public service,” which should be idiosyncratic, to paraphrase Donahue (2006, p.6), and tailored to fit the specific nature of the originating entity. While Smart City Data Hubs, generally speaking, are ubiquitous solutions with a multitude of potential applications, the individual applications of how to use them in the context of a larger problem or goal for a particular entity, generally through novelty, are a determining factor of whether or not something is truly innovative. Donahue’s additional two pillars in their Three Generic Models of Diffusion Dynamics additionally provide context around the development of innovation. However, I would argue that aside from competition or “for the good of the order,” the idiosyncratic nature of a development is a primary hallmark of an innovation. 

For the second part of my definition, there is merit in both the inherent measurable value and whether or not your solution is novel enough that it inspires future innovation after its deployment, both internally or externally. I briefly referenced New York City’s DataBrige project and how it removed information silos, leading to innovations citywide, which had a measurable impact on savings in revenue  (OECD, 2017). Boston’s CityScore initiative has similarly helped to analyze and continues to investigate a multitude of city metrics, which have helped to improve street signs and decrease EMS response time. Whether or not an innovation is worthwhile can be largely pinpointed by its actual intrinsic value or whether or not its existence has produced a measurable positive impact downstream. Though from the perspective of a larger ecosystem, I would also argue that if an innovation inspires the next wave of technologies or innovations that creates new innovation, whether or not the project can be labeled as a success for the institution, I believe it should still be labeled an overall success. To bring it back to genre-breaking artists like the mostly unknown Silver Apples, which inspired the likes of Jimi Hendrix and the Beastie Boys, attempts at innovation like the Community Analysis Bureau (CAB) in Los Angeles in 1967, which was far ahead of its time, but paved the way for innovation to follow.

In conclusion, to be an innovative public organization just isn’t to be forward thinking — it’s to be creative, prepared, and groundbreaking in a way that drives both results and future innovation. It’s a culture of creation that isn’t afraid to challenge the status quo in a meaningful and sustainable way, with a focus on creating value while also improving the outlook for both those inside and outside the institution. Public sector innovation can and should be hard by nature, considering the scope of service and the inherently political nature of public institutions. However, the overall reward is in knowing the potential impact of public sector innovation and the overall improvement in service that everyone in a public institution should strive for.

References

Choi, T., & Chandler, S. M. (2020). Knowledge vacuum: An organizational learning dynamic of how e-government innovations fail. Government Information Quarterly, 37(1), 101416. https://doi.org/10.1016/j.giq.2019.101416

De Vries, H., Bekkers, V., & Tummers, L. G. (2016). Innovation in the Public Sector: A Systematic Review and Future Research Agenda. Public Administration, 94(1), 146–166. https://doi.org/10.1111/padm.12209

Donahue, J. D. (2006, February). Dynamics of diffusion: Conceptions of American federalism and public-sector innovation. Ash Institute for Innovation and Democratic Governance Occasional Paper Series.

Mergel, I., & Desouza, K. C. (2013). Implementing Open Innovation in the Public Sector: The Case of Challenge.gov. Public Administration Review, 73(6), 882–890. https://doi.org/10.1111/puar.12141

OECD. (2017). Fostering Innovation in the Public Sector. OECD. https://doi.org/10.1787/9789264270879-en

Open Knowledge Foundation. (n.d.). What is Open Data? In Open Data Handbook. Retrieved August 9, 2025, from https://opendatahandbook.org/guide/en/what-is-open-data/

Pedersen, K. (2020). What can open innovation be used for and how does it create value? Government Information Quarterly, 37(2), 101459. https://doi.org/10.1016/j.giq.2020.101459

  Romualdo-Suzuki, L., & Finkelstein, A. (2020). Data as Infrastructure for Smart Cities: Linking Data Platforms to Business Strategies. https://doi.org/10.48550/arxiv.2005.11414