Cross-government initiatives have been a response to the need to adapt to the digital era, where data creation increases at an exponential rate and there is an increasing expectation and demand for real-time information. 

New Zealand is a good example of this. A few years ago it was identified that government agencies were operating in an ad hoc way, creating inconsistency in how data is described and recorded, managed, accessed and stored, reused and shared. 

The Chief Executive of Stats NZ, New Zealand’s official data agency, was assigned the role of Government Chief Data Steward (GCDS) to support the government's priority to get more value from data.

As part of this cross-government role, in 2018 the GCDS produced a new Data Strategy and Roadmap. The roadmap aims to provide a joined-up approach to the significant amount of data-related activity underway across government, enabling organisations to collectively work towards, and align their efforts towards generating the maximum impact through data. 

The roadmap identified four focus areas, aimed to generate maximum impact. These are expected to be delivered over 3-5 years, from 2018 onwards:

1. Invest in making the right data available at the right time 
2. Grow data capability and supporting good practice 
3. Build partnerships within and outside government 
4. Implement open and transparent practices. 

The strategy and roadmap is supported by a mature set of frameworks and guidance for government agencies, covering: data leadership, statistical capability, data standards, data stewardship, open data and data investment.

As many other cross-government initiatives, the new Data Strategy and Roadmap have challenged the New Zealand Government. Many are both the successes and the lessons that the Stats NZ team can identify in the processes of providing a joined-up approach and fostering collaboration to generate the maximum impact through data

Overall it had positive feedback across government. By defining shared goals and coordinating activity across the system, it supported others to succeed and link in existing initiatives by highlighting how they contribute towards the defined focus areas. 

Departments are using it within their own data strategies. For instance the Ministry of Business, Innovation and Employment has reframed their internal data strategy around the four focus areas to better focus their effort and understand how they contribute beyond their own agency. Stats NZ is using it for its own internal information and data strategy. The Roadmap has also been used to provide advice into the Budget process, the data system priorities and has informed the data investment framework.

It had also had an impact beyond government. The AI Forum and Iwi Leaders Chairs Forum are using the data strategy and roadmap as a part of their work.

Between the lessons learned, they identified the need to: 

• Articulate value to better engage with the agencies and how to deliver an appropriate message for each level of management, from analysts to CEOs
• Promote universal ownership of the strategy by giving other agencies a clear role in achieving the vision, to help system actors become partners with jointly set goals and priorities by highlighting how Stats NZ is only one of the contributions to this outcome. 
• Have a clear understanding of the steps to take in the short and medium-term to reach the goals, better encourage system-wide engagement and bridge the gap between the current state of the data system and the future vision they are working toward.
• Learning means of measuring strategy and metrics to evaluate success
• Provide clarity of alignment of the Data Strategy with other key initiatives and strategies, which requires a coordinated activity by all functional leads to ensure a greater system focus on the strategic outcome of achieving public value. 
• New leadership demands internal transformation, a way to adapt governance structures to sustain the work of cross-functional leaders and deepen the transformation sought.

Are cross-functional initiatives an effective solution for data governance in public organisations?

What are the key success factors that enable a good data governance framework?

Which are the main blockers towards a joined-up approach?