Data-Driven Innovation: 3 of 3

This is the third 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 1 of 3 and 2 of 3

Designing the i4 Institute

With the significant acceleration in the complexity and interconnectedness of our new Digitally Networked Economy, online Platforms and organisational forms look more like complex adaptive systems and non-linear networks of value creation than the linear, vertically integrated supply chains of the 20th Century.

Those structures were based on the ‘supply economies of scale’ (using Push Strategies), which spawned significant monopolies such as Ford Motors, General Electric and BASF (Parker et al., 2016).

Value in the 21st Century is being created by ‘demand economies of scale’ (using Pull Strategies) where online platforms and collaborative innovation networks that take advantage of technology advances and are driven by efficiencies in social networks, demand aggregation, app development, and the power of networks (Brinton & Chiang, 2019). These networks become more valuable to their users as the number of users expands (Parker et al., 2016).

i4 Institute Strategy and Growth Model

While many of these platforms are for-profit (Uber, Airbnb, et al.), the i4 Institute believes that the same online platform approach and “power of networks” can be used to create a sustainable not-for-profit model.

Using this approach to strategy and growth, the i4 Institute be viewed as a data-driven innovation platform, comprising a series of components (engines, users, and orchestration) that can be configured and re-configured in response to new ideas and ‘wicked’ problems that required a ‘joined-up’ approach. Thus, the i4 Institute as a platform becomes an adaptive system that is continually evolving and oriented towards the application of data-driven innovation for both economic and social value.

This adaptive system design approach is consistent with the wider bodies of knowledge for Open Innovation outlined above, where interconnected industries can gain advantages from sharing knowledge for the purpose of developing new products, as well as services (Abreu & Urze, 2016).

It is also consistent with the ‘coupled open innovation’ process archetype proposed by Gassmann and Enkel (2004), as it is coupling both outside-in and inside-out approaches in alliances with partners, rather than:

• Outside-in: enriching the company’s own knowledge base through the integration of suppliers, customers, and external knowledge sourcing in internal innovation and knowledge creation processes.
• Inside-out: providing new ideas from sources of knowledge and innovation internal to the firm to external users in the outside environment.

Systems Thinking, Platforms and Reinforcing Causal Loops.

The non-linear growth, driven by 21st Century network effects and the emergence of pull-focused adaptive online platforms, results from ‘reinforcing causal loops’ acting within a system. Perhaps the most famous example of a causal loop is the business model for Uber drawn on a napkin by David Sacks:

Figure 2Source: https://otherspecify.wordpress.com/2017/02/02/david-sacks-famous-napkin-sketch-for-econ-101/

Today Uber is valued at $65 billion based on the network effect or ‘flywheel’ of reinforcing causal loops where:

• the more users that join Uber, the more drivers will come to service them, which
• increases density of drivers and riders in an area, which
• results in faster pickups and more users that come to the platform, which
• results in more rides for a driver and better income, leading to lower prices, which
• leads to more users on Uber!

It’s also worth noting that Uber has leveraged the demand economies of scale. When Uber launched its platform, it seeded demand by giving away free-ride vouchers. Thus, the company demonstrated that in a world of platforms, pull strategies designed to encourage virality are more important than the push strategies of the 20th Century, such as advertising and Public Relations (Parker et al., 2016).

Platform Systems as Competitive Advantage

Based on these insights, we realized the potential of taking a systems thinking approach to designing the i4 Institute, focusing on creating a ‘lock-in’ of a reinforcing causal loop flywheel. That can build an advantage for the i4 Institute and drive demand economies of scale for both Seekers and Solvers in solving their Data-Drive Innovation Challenges across networks of private, public, NGO, and not-for-profit organisations.

That notion is consistent with commentators such as Singh (2020), who suggested that the six most common advantages of reinforcing causal loop flywheels of Systems Thinking are:

Figure 3 Source: https://www.growth-catalyst.in/p/flywheels-the-unstoppable-force-of

Designing the i4 Institute using Systems Thinking

Systems Thinking is based on the belief that by examining the linkages, inter-relationships, and interactions between the components that make up a complex system, one can see the impacts of change in a single part of the system upon the system as a whole.

Systems Thinking also stands in contrast to reductionist thinking by considering the effects emerging off the interactions between the various subsystems/components of the whole and acknowledging that the whole and its parts exist simultaneously, but at different levels (Tani et al., 2018).

For the initial design of the DDI Exchange ‘system,’ the following system map is proposed with a single unit of value exchange being the DDI Challenge and three Reinforcing Causal Loops centred on the communities of DDI Seekers, Solvers, and Stewards.

Figure 4: i4 Institute Systems Map Version 1.0

i4 Institute Unit of Value: The Challenge

Just as the value transaction for Uber is ‘the ride’ and for Airbnb, ‘the booking,’ and borrowing from the Singapore Open Innovation Platform ‘Challenge,’ the DDI Exchange supports Data Stewards to work with Seekers within both private and public sector organisations to design and launch a ‘DDI Challenge.’ The primary purpose is to search for innovative data-driven products, processes, and/or service solutions most in line with a Seeker’s needs.

Seekers: The Demand Side of Value Exchange

Launching a DDI Challenge allows Seeker organisations to take advantage of the external data-driven innovative potential of Solvers made up of individuals, startups, and creative businesses and then:

1. Accelerate and implement their new products, services, business processes, and models based on an open and data-driven innovation model, which in turn
2. will bring change to the organisation and market, which will
3. increase the need for new talent and capability, which will
4. increase engagement with the external innovation ecosystem via the DDI Exchange platform, which will,
5. increase both DDI Capability and collaboration for the Seeker organisation, which will in turn
6. continue to accelerate the development of new DDI innovations driven by a change in market demand and the need to define further DDI Challenges, curated by the Data Steward.

Noting that this change in market demand, coupled with accelerating change in digital technologies, will have a negative impact on the availability of DDI skills in the market, which typically presents as skills shortages or mismatches (MBIE, 2021)

Solvers: The Supply Side of Value Exchange

Responding to a DDI Challenge provides Solvers with the opportunity to:

1. Apply their DDI Skills to an industry defined problem or opportunity, which in turn
2. increases their experience in collaboration and the successful application of DDI, which in turn
3. increases their relational networks across industries and businesses, which in turn
4. increases their reputation and visibility to Seeker organisations as new talent and via the market/organisational change in the demand component of the system, which will
5. increase the need for further development of their DDI Skills

Data Stewards: Curators, Collaborators, and Coaches

The primary role of the Data Steward is to help overcome the coordination problems associated with:

• DDI Challenge design that arises from searching for and information about ideas that can be valuable to the challenge, and
• overcoming the divergent objectives of all stakeholders engaged in the DDI Challenge design that could create difficulties in launching the challenge and implementing the solution.

With these in mind, the Data Steward will have the following three impacts on the DDI Exchange System:

• Curating Successful Challenges: Acknowledging that New Zealand ranks poorly in terms of managerial competence (see Section Two), the Data Steward works primarily within the demand side of the DDI Exchange (with Seeker Organisations) to help them clearly define their problem or opportunity and identify the value of unlocking their data to assist in the innovation process. That will ensure the Solvers can understand the Challenge and respond appropriately, thereby increasing the potential for a successful challenge outcome.
• Collaboration Facilitators: Acknowledging that New Zealand’s weak collaborative capability (See Section Two) leads to a lack of innovation diffusion across and between industry value chains, the execution of a DDI Challenge via an Open Innovation model will bring change to the organisation and increase the need to work with external parties. Data Stewards will act as ‘collaboration facilitators’ across these industry networks, thereby increasing collaborative innovation capacity across the DDI Exchange system as a whole.
• Coaches: As industry practitioners and supported by the DDI Learning engine for professional development, the Data Steward will also act as a coach in Data Stewardship, Governance, Collaboration, Ethics, and Literacy across both the Seeker and Solver communities, thereby having a positive impact on increasing DDI skills and capabilities across the system.

Systems Thinking: Outcomes for the New Zealand Innovation Ecosystem

A key objective in the i4 Program funding application with MBIE was that it did not intend to duplicate or churn out what is already available in New Zealand’s regional innovation ecosystems. Instead, it seeks to identify data-driven innovation patterns and practices from across New Zealand and around the world and then help incorporate these patterns and practices into the local innovation ecosystem.

Figure 5: Systems Thinking Impact Map Version 1.0

Taking a systems thinking approach to the design of the DDI Exchange supports this objective. It does that by identifying the key ‘reinforcing causal loops’ and critical components across The i4 Institute and data-driven networks and communities of practice (DDI Seekers, Solvers, and Stewards) and helps consider the impacts and complementarity of other groups from across New Zealand to:

• Increase external collaboration and data sharing capability across industries by unlocking more data, leading to more digital adoption and helping the New Zealand Digital Technology Sector to identify DDI opportunities with Kiwi businesses, in turn increasing Data-driven Innovation and competitive advantage for New Zealand (outer reinforcing causal loop below).
• Help New Zealand businesses see and harness the power of data as a strategic business asset and use data-driven technologies by developing their internal data skills and capabilities to improve operational efficiency and make better decisions around strategy and investment (inner reinforcing causal loop below).
• Partner with Government Agencies and Research Institutes to craft Data Collaboratives between industry and the Digital Technology Sector, where public and private sectors exchange data across industry supply chains to create value.

DDI Exchange Sustainability

Identifying these reinforcing loops in the same way David Sacks articulated the Uber business model also helps identify the key capabilities and resources required to self-sustain the DDI Exchange beyond the i4 Program duration (June 30th 2022).

This sustainability is predicated on the continuation of the various DDI Challenges because they meet the needs of industry (demand side) and the development of additional collaborative DDI Projects with industry where the DDI Exchange provides a means of linking collective resources with tangible outcomes for NZ Inc.

DDI Challenges Leading to Collaborative Projects

These virtual DDI Projects will be resourced and executed separately by their curated virtual teams and supported by the DDI Exchange Platform. Projects will have two modes, some simple, some complex:

The i4 Institute: A Long-term Collaborative Innovation Network

In this approach, the DDI Exchange becomes a self-sustaining ‘Goal-oriented network’ characterised by “intense interaction among its participants aiming at reaching a common goal” and, as proposed by Camarinha-Matos et al. (2019), comprising:

• Platform: a continuous production-driven network (the DDI Exchange Online Platform) that remains stable for a long period with well-defined roles for its participants.
• Networks: opportunity-driven DDI Labs across regional New Zealand and industry networks that are “dynamically created to pursue some business opportunity within a limited time window” via virtual teams of the DDI Challenges and Projects, and
• Community: professional virtual communities/networks composed of individual professionals (Seekers, Solvers, and Stewards) that “get together on a long-term basis to be prepared to rapidly react in response to business opportunities through the dynamic creation of temporary virtual teams.”

For more, see The i4 Institute

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