Key Takeaways

1. Complexity of network integration and different data structures.
2. How do you integrate solutions for transport, rail and water?
3. What question will the data I collect answer? Being pragmatic with what I collect.
4. Data structure is important, what and why are we collecting.
5. Key players collaborating and not just one supplier.
6. Infrastructure to support data capture becomes assets to manage as well.
7. How are we going to share data?
8. Data - what are the questions are trying to answer.
9. The importance of asking the right question before deciding on digital solutions.
10. Change is inevitable, in relation to data access what is the boundary between proprietary and public information?
11. Behaviour change will be needed to embrace these technology opportunities.
12. How do we ensure we have vision guiding data collection when technology is developing first?
13. Asking the same Iggy questions, getting and using the appropriate data and technologies.
14. We need to create an environment which drive digital change.
15. Decentralised assets, technology and efficiencies.
16. Traditional data and new data where we don't know the quality that we need make decisions on.
17. If so many innovations fail, how do we choose what to invest in?
18. The risks of not innovating.
19. Monitoring sensor being driven or sold by sales.
20. Technology is moving/changing quickly. How do we ensure skills are being matched with changing technologies?
21. Trends of automation and data exchange and inter connectedness.
22. Data are assets that need strategies and management plans like any other asset.

Actions

1. Reinforces need for common and consistent approach. NZTA can show leadership in this space - possibly needs REG support for messaging.
2. Collaboration and funding a cross all infrastructure bodies. Committee should be lobbying for funding and cross sector communication.
3. Help agencies understand risks around data ownership.
4. Driving best practice, resulting in consistency. Making sure the information is readily available and easy to interpret.
5. NAMS can help establish standardisation and drive it.
6. Provide training associated with new technologies.
7. Survey the industry, identify which sectors have a master data standard, are they similar, working.
8. National advisory on best practice for digital/data, LG with limited resources can't prioritise this. Facilitate the best practice via TA's.
9. Facilitating sharing of case history examples.
10. Produce a position paper on new technologies such as machine learning, etc to give guidance to members; what are new business drivers?
11. Focussed collaboration on understanding the provenance and quality on the data and its acquisition and distribution.
12. Provide communications across what industries are doing to enable agility and learnings from others.
13. Support ISO5001 by NAMS running CAMA qualification courses for people
14. A national forum for digital change helping local authorities embrace and achieve future vision and objectives.
15. Moving with technology, what are we leaving behind?
16. When planning asset replacement ensure you are set up to collect the right data
17. Promote/advocate for seed funding to really establish benefits of agile development
18. What are the risks of not innovating? Huge opportunities of automated tech savvy, but are we NZ - ready and prepared and able?
19. Common forum to discuss use of new technology.
20. Can name provide a feedback loop from technology developments in industry to skills learning institutions?
21. Supporting automated, interconnected AI decisions and working.
22. Treat data as assets in guides, examples.

Risks / Challenges

• Willingness to collaborate in this space. Size of tasks significant.
• Keeping momentum, and how do you apply standards across all.
• Data ownership is often unclear and consensus on a continuity of oversight of these issues.
• Resistance to collaborate due to commercial sensitivity I.e. No reward to be innovative as a contractor.
• Takes too long, can optimise infrastructure agency, local government impact.
• Change might be too dynamic to be able to "manage"
• Time keeps moving.
• Time, restriction on innovation or regulation. Framework not a policy, but how do you enforce it, and keep up with digital change.
• Resourcing ($ and people), communication to get relevant examples.
• Data ownership and not being too constrained in our solution, to be sure we don't remove innovation.
• Audit requirements will be more challenging.
• Time vs technology developing.
• Consistent global view on best AM practices.
• LA buy in and funding.
• Need data standards. Consistent standards. Standardisation ac
• Contractors/ TLA/ government not aligned when sharing lessons learnt
• Demonstrating benefits in business case - could be mitigated through staged approach
• Skill shortages- no one coming into the industry. People retiring. People heading overseas- better prospects. Huge gaps in industry, skills and understanding of the opportunity of smart, tech savvy solutions.
• Common forum.
• Can we respond fast enough / will we already be outdated before we change?
• Not knowing how best to take and make the opportunity.
• Data ownership, security, IP.

Skill or Training Gap

• Lot of skill gaps in local authority but acknowledge data standards will enable sharing of resources more effectively.
• Knowledge gap between agencies. What is LGNZ role in this?
• Yes there is - very intimidating to ask the question of who owns what.
• Not so much a technical issue but more of an emotional intelligence/awareness to commercial drivers.
• New agency risk, NZTA going live in new year but hearing about it very late, who is doing what when, autonomy to make own rules.
• Yes - even awareness that these are assets too!
• Not specifically. Need communicators between industries.
• If regulated there are legislative limitations.
• It is a global community.
• Current education system, we need to share analytics across industry to promote system innovation.
• Need to draw on subject matter experts to translate data to customers
• Technology literate.
• Consistent global best AM practices.
• Not likely that local authorities can hire future or smart city experts. A national support group would help drive this.
• Consistent, at scale, Apprentice degrees, through polytechnic not universities. Learning in the job, on top of skills taught centrally. Skills needed in 5-10 years are known or taught yet.
• Spend more time failing fast.
• Understanding agile development.
• Match vision to learning, to emerging technologies.
• What skills do we need in the future, to make these things happen?

Quotes

• State highways touching 68 local networks
• moving people not machines
• Make it better for what......
• What's the relevance of the data
• Data sharing is open and accessible and helpful to data validation
• Managing the batteries in our sensors....
• Data rich and knowledge poor
• Power is with the collector of the data
• Using data for different purposes - condition rating vs maintenance need
• No longer need preventative maintenance, but predictive and prescriptive analytics to enable smarter working.
• We need to know what information we require when we replace with new
• 90% of innovations fail
• Sales driven
• Enabling lean decisions and working