Accelerating Innovation: How the Hub & Spoke Model Empowers Enterprises Data Teams To Innovation

In today's rapidly evolving business landscape, innovation through data and AI has become a key differentiator for enterprises seeking sustainable growth. To foster innovation, organizations are turning to the Hub & Spoke model, a strategic approach that enables them to create a technology environment conducive to innovation. This article explores how the Hub & Spoke model empowers enterprises by leveraging a pull and push approach, establishing trust and sponsorship, and aligning prioritization to drive speed-to-value.

Enabling Innovation like a pumping heart:

The Hub & Spoke model enables a pull and push approach to innovation, facilitating the flow of ideas and solutions between business units (spokes) and, typically, a central Technology function or a CoE hub. At the spokes, business units play a vital role in generating innovative ideas and initiatives, driven by their deep understanding of market dynamics, operations, and customer needs. The central hub acts as the catalyst, collaborating closely with the spokes to evaluate, refine, and implement these ideas effectively.

There are two effective modalities and ways of working to foster this collaboration and connection: (1) "Coordinated" - Where the hub is providing standards, tools, access to data, best-practices, etc,. Typically this way of collaborating would be perfect for experimentation and demonstrating feasibility like in a localized Proof of Concept. (2) "Integrated" - Where the hub is actually co-creating and building those advanced digital data products with the spokes due to their Global Scale potential and impact on the whole organization, beyond the spoke localized scope. In this case, those projects will either be vetted from day one by the hub or started in the spoke and got great results that can be scaled. The Coordinated schema has an important role in creating innovation and expiration, while the Integrates schema plays a major role in scaling innovation for advanced data projects.

To facilitate this collaboration, a culture of open communication and knowledge sharing is fostered, encouraging business units to pull the technical expertise and resources they require to bring their innovative ideas to life, this goes along with standards, tools, and obviously data. Simultaneously, the hub pushes technological advancements, best practices, and solutions back to the spokes, empowering them to innovate further in the edge. This iterative approach ensures a constant feedback loop between the business units and technology center, fueling innovation and driving transformative outcomes.

Creating Trust & Sponsorship - Building a Strong Alignment:

Trust and sponsorship are at the core of Technology. The hub marching together with the business units at the spokes are fundamental to the success of the Hub & Spoke model and for realizing the value behind data innovation. Establishing trust requires strong alignment with the spoke's Business Strategy and a deep connection to Executive Sponsors (either in the C-Level or just below) to take an innovative idea from a "lab" to full deployment and adoption at scale. The hub must act as an enabler, actively seeking input from the spokes and providing the necessary support to turn ideas into reality.

Sponsorship plays a crucial role in gaining support and resources for innovative projects. By securing sponsorship from senior leadership, the hub can ensure that innovation initiatives receive the attention and backing they deserve, especially due to heavy change management that is typically required to implement a cutting edge technology project. This sponsorship also helps in overcoming potential resistance to change and encourages a culture where risk-taking and experimentation are embraced. I found it critical to nominate and establish prominent and stable business leaders from the spoke that have great relationship and cadence with the hub.

Aligning Prioritization - The right Sponsor, at the right Time, with the optimal Value, and Technical Feasibility:

Innovation requires a clear alignment of priorities between the technology hub and the business units. A Speed-to-Value Prioritization approach ensures that innovation initiatives are prioritized based on their potential to deliver tangible business outcomes within the desired timeframe. The Prioritization Framework is consistent of the following pillars:

1. Business Sponsorship - Must originated at the Spoke, a C-Level or just below with enough political power and ability to move and navigate intrusive change management open-heart surgery type of operations.
2. Value - A Data & AI innovation project should be able to contribute to a business KPI in either direct business value ($) or other crucial metrics such as Customer Experience, Health & Safety, etc,. It has to be "in the millions", otherwise the change management efforts and investment will not justify the initial effort. Aligning and agreeing on the ROI before starting a project is critical.
3. Business Readiness - The business must be prepared to receive, utilize, and adapt current processes based on the new technology project. If the business fails to adjust and fully embrace the technology, we cannot expect to derive any value from it. This step is critical in the prioritization process, often overlooked.
4. Technical Feasibility - The innovation data project must fulfill all technical requirements, including data availability, computing power, mathematical problem-solving capabilities, ethics, and project execution. This aspect typically poses fewer challenges within the framework if data exists and is vetted.

By focusing on high-value opportunities and investing resources accordingly, enterprises can maximize the impact of their innovation efforts through a very dynamic and typically venerable Innovation Life Cycle. Additionally, close collaboration enables the hub to provide the necessary technical expertise, consulting, and support to ensure the successful implementation of data innovative solutions within the business units.

Conclusion:

The Hub & Spoke model has emerged as a powerful framework that empowers enterprises to create an environment conducive to innovation, especially around data and AI related projects. By adopting a pull and push approach, organizations can tap into the creative potential of their business units while leveraging the expertise of a central technology hub. Establishing trust and sponsorship between the hub and the spokes creates a strong foundation for collaboration, enabling the seamless flow of ideas and resources. Furthermore, aligning prioritization based on a speed-to-value approach ensures that innovation initiatives are strategically aligned with business objectives and the readiness of each business unit.

In a competitive business landscape, the Hub & Spoke model serves as a catalyst for transformative innovation, helping organizations stay ahead of the curve. By embracing this model, enterprises can foster a culture of continuous improvement and drive meaningful change that propels them toward future success.