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Jaguar Land Rover

Designing an enterprise-wide operating model and structure for Data Analytics

Jaguar Land Rover (JLR)
Client Jaguar Land Rover
Region UK
Sector Infrastructure, Defence and Transport
Offering Organisation Design, Technology
Data analytics Team structure Ways of working

The Challenge

For Jaguar Land Rover (JLR) the potential for data analytics to transform business performance is significant. During 2018, the central data analytics team generated £150m through their work and now the size of the prize is even bigger – the value of future analytics driven work across JLR in the short term has been measured at over £1bn.  

Today data analytics is fragmented across JLR so is not optimally set up to deliver this value. Coordination and cooperation between central and functional analytics teams is inconsistent and at times inefficient, there is limited formality around prioritisation of work and measuring the short term vs. long term benefit of the outcome, and there is a high attrition rate.

Q5 were asked to support a cross functional team to design i) an operating model for how data analytics should work at JLR, ii) optimum team structure, and iii) ways of working for Data Analytics cross enterprise, to drive value for JLR.

Our Approach

  • Using the Q5 OD methodology – the Metro Map – and working as part of blended team with JLR HR, we facilitated the central and functional data analytics teams to create a vision for the future of analytics at JLR and then design the model and teams needed to deliver this vision
  • We initially interviewed a cross section of data analytics and broader business stakeholders to understand the strengths and challenges of analytics at JLR –this drew out specific problems to resolve through design
  • With the cross functional team we identified the future work that data analytics needs to do to achieve its vision, and used this to iterate a ‘hub and spoke’ operating model
  • Using the operating model, we worked with Data Analytics teams to identify the optimum team structures and roles needed to deliver the work
  • To bring the OD to life, and make sure the Data Analytics teams were clear on how to work together, we collaboratively worked through several scenarios for how the hub and spoke model will work in practice including demand prioritisation

Key Outcomes

  • An agreed hub and spoke operating model to clearly define the central (hub) and functional (spokes) teams relationship with one another and the business, and what they deliver
  • Clear matrixed team structure for the hub and spokes, enabling optimum shape and size analytics teams to deliver on projects and programmes that represent greatest value for the business, and measure returns
  • Instilled a ‘one team’ mentality between analytics hub and spokes which agreed to transparency and collaboration as key working principles
  • Defined roles and accountabilities for future hub and spokes to operate together successfully
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