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CHAPTER 6
Scaling innovation – using innovation labs as a testing ground to scale
Data Innovation Labs are a wonderful cohesion of Data, Technology and People that work together to rapidly discover and deliver innovative data solutions. Previously, we have discussed Data and Technology elements and their fundamental principles. In this edition, we focus on perhaps the most important part of the equation; People.
Innovation Labs at their core work by changing business processes in a meaningful and measurable way. Such change cannot be affected without careful coordination of people with business domain knowledge, technical know-how and data knowledge. As we have seen, through the provision of Data Culture, it is ultimately people, not technology, who will facilitate a data-driven culture at an organisation. Our reference model for the Innovation Lab team design reflects this mentality. 

The lab team consists of 5 reference roles

Lab Customers Set the strategic direction for data in the organisation. Define the KPIs by which the CDO will hold itself and others to account. Lead and live the Data Culture within the organisation – driving data-led behavioural change.
Specialist Support Set the strategic direction for data in the organisation. Define the KPIs by which the CDO will hold itself and others to account. Lead and live the Data Culture within the organisation – driving data-led behavioural change.
Innovation Enablement Set the strategic direction for data in the organisation. Define the KPIs by which the CDO will hold itself and others to account. Lead and live the Data Culture within the organisation – driving data-led behavioural change.
Insight – Discovery Set the strategic direction for data in the organisation. Define the KPIs by which the CDO will hold itself and others to account. Lead and live the Data Culture within the organisation – driving data-led behavioural change.
Initiative Sprints  Set the strategic direction for data in the organisation. Define the KPIs by which the CDO will hold itself and others to account. Lead and live the Data Culture within the organisation – driving data-led behavioural change.
1. Value
  • Enablement teams reach out to potential business customers. They understand the value that access to the lab could help them to deliver
  • Workshops take place to understand value goals and agree on a sprint map, sprint questions and user stories
  • This workshop then leads to the discovery of Business Value Goals (BVGs), Sprint Questions and User Stories
2. DISCOVERY
  • User Stories are investigated and assessed against a set of criteria
  • The Lab Manager and Discovery Team Lead use these inputs to define which initiatives need to be prioritised
  • The team works across functions to set up the initiatives (e.g. sourcing data, governance approvals)
  • At the end of the discovery stage, the team would have gathered all the data, know-how and designs required to start conducting data-led experiments
3. EXPERIMENT CYCLES
  • Initiative sprints run for 4 weeks
  • A team consisting of a data scientist, analyst and visualisation specialist work closely with the business initiative owner to test a number of hypotheses
  • A sprint answers a business question, developing a minimum set of outputs to provide the value the business customer requires
4. Customer proving
  • The MVP developed in an initiative sprint is presented back to the customer and other business teams
  • The business customer takes ownership of the MVP and uses it to determine whether to continue to scale the initiative based on the value it provides
  • The MVP may drive requirements for new lab initiatives, which are added into the backlog process (Value Step)
5. SCALE delivery
  • When customer proving demonstrates the value and viability of the MVP, this drives the business case for scaling up the initiative through enterprise project teams
  • Any further support required from the lab can be requested through the value standard backlog mechanism
Process In order to achieve optimal performance of the lab, the teams need careful coordination via an agile operating model. To achieve scale, our operating model for the lab ensures that the teams can work fluidly and minimise the number of blockers on them. It does this by pipelining and combining flexible experiments like early stages with rapid factory-like later stages in the cycle. It is this combination of hypotheses, experimentation, iteration and, ultimately, scaling that allows the Innovation Lab concept to deliver value quickly to the wider organisation.  When in motion, each proposed use case or innovation goes through the following stages. Each stage in the process involves a cross-functional team to work in close coordination.
It is ultimately people, not technology, who will facilitate a data-driven culture at an organisation.