Data-Driven Innovation: 1 of 3

This is the first in a series of three articles exploring data-driven innovation, what it means for New Zealand, and the design thinking behind the formation of the i4 Institute.

Others in the series 2 of 3 and 3 of 3

Data-driven innovation is the process and consequent economic and social value that arises from the collection, analysis, use, and sharing of data by private and public sector organisations.

This Data-driven Innovation (DDI) could easily deliver $4.5 billion in benefits to the New Zealand economy (Winspear, 2021), however:

“To truly gain the potential benefits of Data-driven Innovation, businesses and government entities will need to change how they operate, from decision-making to organisational structures, in light of the huge increases in the volume and usefulness of data for decision-making in recent years, and the ease with which this data can now be stored, analysed and shared” (COVEC, 2015)

As such, there’s a need to help New Zealand businesses to see and harness the power of data as a strategic business asset. Enabling the use of data-driven technologies to improve operational efficiency, develop new products and services, and make better decisions around strategy and investment.

One avenue to increase this data literacy or ‘capability maturity’ is Learning by Doing. That’s a concept where people couple the development of new data skills with real-world problems and opportunities, connecting the people, skills, knowledge, and data resources inside their organization with multiple outside sources of Data-driven Innovation using an Open Innovation Model.

This article explores the idea of Open & Data-Driven Innovation, and how it was implemented in the New Zealand via the i4 Program of work across 2021–2022. The following sections of this article:

• Provides some insights into the potential of and barriers to Data-driven Innovation in the 21st Century version of a Networked Economy.
• Introduces the i4 Program, which was designed to help organisations unlock the value of their data and better collaborate with the digital technologies sector to accelerate data-driven innovation and competitive advantage for New Zealand.
• Introduces The i4 Institute as a purpose-built platform designed to help people build their data literacy and competencies and then integrate these new learnings with real-world problems and opportunities.
• Provides insights into how we used a ‘System Thinking’ approach to craft The i4 Institute Open Innovation Model, exploring the benefits of using this approach to sustain a mutually beneficial relationship between New Zealand businesses and the digital technologies sector.
• Finally, we introduce the launch and execution runway for the DDI Exchange, including a brief outline of current thinking for its long-term sustainability beyond July 2022.

We hope you enjoy!

Malcolm Fraser, Chief data steward, The i4 Institute

Dear reader, the Internet changed everything (or did it?)

In the 21st Century, our global economy can be viewed from several perspectives: transition from the industrial economy, digital and information infrastructure, global scale, value networks, and intellectual property rights (World Economic Forum, 2020).

However, in this era of networks, hyperconnectivity, and digitization of fast-growing, real-time connections among people, is a ‘Network Economy’ really something new, or is it merely something old with a digital flavor?

Hierarchies, Markets, and Networks

As a society, we are now more connected and resourceful than ever. However, it has been suggested that even in Greek and Egyptian times, Hierarchies, Markets, and Networks existed. Not only that, but they were representative of the three basic forms of economic coordination (Manning, 2011). Namely, environments where royals, governments, private enterprise, local tribes/communities and individuals all exploited their social networks and knowledge to unite capital with labor, turn raw material into finished goods, and distribute these ‘innovations’ into the hands of the customer.

Some social scientists (Thompson et al, 1991) have generally drawn lines between these networks, hierarchies, and markets. Others, such as Granovetter (1985), suggest that this distinction is no longer very useful and that “all economic activity is embedded within social networks, and that networks ‘penetrate’ to different degrees in different locations and among different kinds of relationships.”

Entrepreneurs and Networks

This perspective on the role of networks in economic activity is also supported by Manning’s work (2011) on the early Ptolemaic Economy of Egypt, which includes a study of an individual [sic. entrepreneur] involved in a textile market of ancient Memphis who ranged across a ‘cloth network’ of flax producers, dyers, weavers, and consumers in the second century BC.

In this work, Manning concludes that there are no ‘substantive’ differences between these ancient and our modern market economies and that the thinking on hierarchies, markets, and social networks can redraw as a nested relationship, rather than overlapping:

From this perspective, networks can be seen as always having served as means of connecting humans, be those networks physical, social, or digital, and that the modern economy would not exist without the transportation, communications, information, energy, and railroad networks we have today (Oteniya et al., 2020).

Hierarchies and Information Concentration

Hence, we can view the vertically integrated firms born from the first industrial revolution driven by steam (1760 to 1820) as an upgrade or iteration of the ancient hierarchy as a basic form of economic coordination. Further, while the development of new communications technology such as the phone, telegram, TV, radio, and fax machine over these last 200 years have helped increasingly connect individuals and social networks across vast geographic distances, they have also tended to concentrate and commercialize the production and exchange of information within these industrial hierarchies where:

“The mass-media model of information and cultural production and transmission became the dominant form of public communication in the twentieth century” (Benkler, 2006).

The Internet: Disrupting the Hierarchy?

Towards the end of the twentieth century, the Internet or “information superhighway” began to disrupt these vertically integrated firms (hierarchies), linear supply chains, and mass production/mass consumerism paradigms by ‘decentralizing’ the capital structure of production and distribution of information, culture, and knowledge (Benkler, 2006).

What we now know as the Fourth Industrial Revolution (Park, 2018), powered by the Internet, Cloud Computing, Big Data, and Artificial Intelligence technologies, is now transforming the global supply chains of the 20th Century and changing the competitiveness of nations, regions, and industries, via an upgraded digital version of the Networked Economy.

This is evident in the form of online networks or ‘platforms’ of supply and demand such as Google, Amazon, Netflix, Airbnb, and Uber, where “the many terabytes of user behavior data is a clue to how consumers will behave in markets of infinite choice” (Anderson, 2006).

Therefore dear reader, welcome back to the ‘new’ Networked Economy of old, where Hierarchies, Markets, and Networks still exist. However, ‘networks’ rather than hierarchies are the new dominant economic coordinator, and data is to the 21st Century what oil was to the last.

A Data-driven World?

The challenge is helping businesses, communities, and governments truly gain the potential benefits of this new Data-driven and globally networked economy. How can we help them innovate and change how they operate in light of the huge increases in the volume and usefulness of data for decision-making in recent years?

How do we exploit the ease with which this data can now be stored, analysed, and shared across global and intensely integrated networks of production and consumption?

- Welcome to the Data-Sphere

The Fourth Industrial Revolution, Big Data, Artificial Intelligence (AI), Internet-of-Things (IoT), and Trusted Data Platforms are driving the transformation of global value chains and changing the competitiveness of nations, regions, and industries.

In addition, the proliferation of social media and tracking technologies has dramatically increased the volume and variety of data in the world. The sum of data created, captured, and replicated in any given year worldwide is projected to grow from 33 zettabytes in 2018 to over 183ZB by 2025 (IDC 2021).

In short, welcome to the Data-Sphere!

Data is now a key resource and of critical importance. Astonishingly, over ninety percent of the data that has ever existed in the world today was generated within the last two years. Just like oil and computer power defined the Third Industrial Age, we’re now witnessing a rapid intensification in the “datafication” process, where the use of data will grow increasingly critical (Verhulst, 2014).

Data-Driven Growth

The OECD (2015) also suggests that this data explosion will form a key pillar in 21st century sources of growth. The confluence of several trends, including the increasing migration of socio-economic activities to the Internet on platforms like Facebook, Google, and YouTube, coupled with falling costs for data collection, storage, and processing, will lead to data-driven innovation fostering new industries, services, processes, and products, for instance:

• In business, data exploitation promises to create value in various operations, from optimizing value chains in global manufacturing and services to more efficient use of labor and tailored customer relationships.
• The adoption of ‘smart-grid’ technologies generates large volumes of data on energy and resource consumption patterns that can be exploited to improve energy and resource efficiency.
• The public sector is an important data user but also a key source of data. Greater access to and more effective use of public sector information (PSI), as called for by the OECD Council Recommendation on PSI, can generate benefits across the economy (Ubaldi, n.d.).

Data-Driven Innovation

While there are many definitions of innovation, this article uses the following one:

“the intentional introduction and application within a role, group or organization of ideas, processes, products or procedures, new to the relevant unit of adoption, designed to significantly benefit the individual, the group, organization or wider society” (West & Farr, 1990).

In regards to Innovation driven by the collection, analysis, use, and sharing of data, the publication Data-driven Innovation in New Zealand (COVEC, 2015) proposed that Data-driven Innovation be defined as:

“The innovation and consequent economic and social value that arises from the use of data analysis by private and public sector organisations to make better decisions and create new products and services.”

Data, Platforms, and Open Innovation

As outlined in the previous section, the combination of datafication, networks, and data-driven innovation opportunities for new approaches to markets has given rise to data-centric business models, where online platforms become enablers for groups of companies to jointly develop new products and services and the notion of co-innovation or ‘Open Innovation’ is gaining wider acceptance (Abreu & Urze, 2016).

These ‘platforms’ of the new Networked Economy can be defined as a business model based on enabling value-creating interactions between external producers and consumers (Parker et al., 2016).

An increasing number of organisations are adopting the platform business model in order to remain competitive (Chan et al., 2018). For example, companies such as Airbnb, Uber, Amazon, Google, Salesforce, and Facebook have created online networks that facilitate digital interactions between people by providing an open, participative infrastructure for multi-sided interactions while setting the governance conditions for these interactions (Parker et al., 2016).

The wider economic aggregation of these online networks, platforms, and emerging organisational models has been given a variety of names such as ‘the creative economy,’ the ‘sharing economy,’ the ‘gig economy,’ or the ‘peer economy’ (Chan et al., 2018).

The process also leads to emerging organisational concepts like Entrepreneurial Ecosystem Enabling Organizations (EEEOs) such as Haier’s Micro-Enterprises and Amazon’s two-pizza team models (Cicero, 2019). For example, both Haier and Amazon use a platform and collaborative network approach that removes the rigid boundaries between the inside and outside of organisations. That’s achieved by adopting open innovation strategies such as user-led idea crowdsourcing, innovation crowd-funding, validation through pre-sales, ‘kick-starter’ prototyping, and external entrepreneurs pitching creative ideas for new micro-enterprises (Cicero, 2019).

In this regard, we are seeing the accelerated emergence of new products and services, developed and taken to market, not by a single firm, but rather ecosystems of networked relationships and subsystems comprising multi-sided platforms, research, business, government ‘triple helixes’ (Etzkowitz & Leydesdorff, 1995) and Collaborative Innovation Networks.

Collaborative Innovation Networks

These ecosystems of networked relationships are pushing organisations to become more and more reliant on collaboration in distributed, cross-disciplinary, cross-cultural, virtual teams (Boughzala & De Vreede, 2015). These impacts and the new forms of systemic/open innovation and ‘collaborative innovation network’ models (Camarinha-Matos et al., 2019), coupled with data democratization, has also increased significantly as an “inescapable and powerful vehicle” for implementing corporate social responsibility and for achieving social and economic missions (Austin & Seitanidi, 2012).

In this context, the quality of collaboration will directly affect the quality of an organization’s outcomes, whether that be for-profit, not-for-profit, or for community good. That means the disposition and capabilities of an organization’s management and workforce to collaborate across networks and with the myriad of private and public sector actors external to their organization will directly affect organisational performance and thus organisational productivity and profitability (Boughzala & De Vreede, 2012).

- Data-Driven Innovation in New Zealand

Data-driven Innovation, Collaborative Networks, and online Platforms can support a wide range of innovations for New Zealand, including improving firm operational efficiency, developing new products and services, making better investment and strategic decisions, and more effective government interventions.

New Zealand Data-Driven Innovation Benefits

For New Zealand, Data-driven Innovation could easily deliver NZ$4.5 billion in benefits (COVEC, 2015) via new startups, value chain productivity lift, improved and customized goods and services, improved competitive advantage in key industry sectors, and greater well-being through better outcomes across the public sector.

However, research focusing on the New Zealand context (COVEC, 2015) has identified several significant barriers to the realization of these data-driven innovation benefits…

Read #2 in the series of three

Bibliography

Abou Hana, M., Axente, M., & Joseph, M. (2019). Building the Data Economies of the Future.

Bawden, D., & Robinson, L. (2018). Curating the infosphere: Luciano Floridi’s Philosophy of Information as the foundation for library and information science. Journal of Documentation, 74(1), 2–17. https://doi.org/10.1108/JD-07-2017-0096v

Camarinha-Matos, L. M., Fornasiero, R., Ramezani, J., & Ferrada, F. (2019). Collaborative networks: A pillar of digital transformation. Applied Sciences (Switzerland), 9(24). https://doi.org/10.3390/app9245431

Delgado, M., Porter, M. E., & Stern, S. (2014). Clusters, convergence, and economic performance. Research Policy, 43, 1785–1799. https://doi.org/10.1016/j.respol.2014.05.007

Art. 68 GDPR — — GDPR.eu, (2018) (testimony of European Union). https://gdpr.eu/article-68-what-is-the-european-data-protection-board/?cn-reloaded=1

Fairlie, S. (2009). A Short History of Enclosure in Britain. The Land Magazine. https://thelandmagazine.org.uk/sites/default/files/enclosure low res.pdf

Frischmann, B. M., Marciano, A., & Ramello, G. B. (2019). Retrospectives tragedy of the commons after 50 years. In Journal of Economic Perspectives (Vol. 33, Issue 4, pp. 211–228). American Economic Association. https://doi.org/10.1257/jep.33.4.211

InnoData. (2019). 4 Steps to Build Truly Intelligent Machine Learning Models WITHOUT DATA, ARTIFICIAL INTELLIGENCE IS PRETTY DUMB.

Kuenkel, P. (2019). Stewarding Sustainability Transformations in Multi-stakeholder Collaboration. In Stewarding Sustainability Transformations (pp. 141–205). Springer International Publishing. https://doi.org/10.1007/978-3-030-03691-1_6

Lepore, D., & Spigarelli, F. (2020). Integrating Industry 4.0 plans into regional innovation strategies. Local Economy: The Journal of the Local Economy Policy Unit, 35(5), 496–510. https://doi.org/10.1177/0269094220937452

Marmolejo-Saucedo, J. A. (2020). Design and Development of Digital Twins: a Case Study in Supply Chains. Mobile Networks and Applications. https://doi.org/10.1007/s11036-020-01557-9

McPhillips, M. (2020). Trouble in paradise? Barriers to open innovation in regional clusters in the era of the 4th industrial revolution. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), NA-NA. https://doi.org/10.3390/JOITMC6030084

Nugroho, R. P., Zuiderwijk, A., Janssen, M., & de Jong, M. (2015). A comparison of national open data policies: Lessons learned. Transforming Government: People, Process and Policy, 9(3), 286–308. https://doi.org/10.1108/TG-03-2014-0008

OECD. (2011). Divided We Stand Why Inequality Keeps Rising an Overview of Growing Income Inequalities in OECD Countries: Main Findings. https://doi.org/10.1787/888932315602

Park, S. C. (2018). The Fourth Industrial Revolution and implications for innovative cluster policies. AI and Society, 33(3), 433–445. https://doi.org/10.1007/s00146-017-0777-5

Plechero, M., & Rullani, E. (2019). Beyond Local: The Role of National Innovation Networks Within the 4th IR. SYMPHONYA Emerging Issues in Management.

Publishing, O. (2015). Data-Driven Innovation Big Data for Growth and Well-Being [Unknown]. OECD Publishing, Paris.

Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum.

Susha, I., Janssen, M., & Verhulst, S. (2017). Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development. Proceedings of the 50th Hawaii International Conference on System Sciences (2017). https://doi.org/10.24251/hicss.2017.325

Unger, R. M., Stanley, I., Gabriel, M., & Mulgan, G. (2019). Imagination unleashed Democratising the knowledge economy. www.nesta.org.uk

Verhulst, S. (2014). Mapping the Next Frontier of Open Data: Corporate Data Sharing. The GovLab. https://blog.thegovlab.org/post/mapping-the-next-frontier-of-open-data-corporate-data-sharing

Verhulst, S. (2021). Mapping the Next Frontier of Open Data: Corporate Data Sharing. The GovLab. https://blog.thegovlab.org/post/mapping-the-next-frontier-of-open-data-corporate-data-sharing

Xu, M., David, J. M., & Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9(2), 90–95. https://doi.org/10.5430/ijfr.v9n2p90

Young, A., & Verhulst, S. G. (2020). Data Collaboratives. In The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs. https://doi.org/10.1007/978-3-030-13895-0_92-1