16 February 2018 Aide memoire to the Minister of Statistics: Launch of Data Ventures R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t Purpose 1. We have recently established a small ‘Data Ventures’ group within Stats NZ, with the appointment of Drew Broadley as Executive Director. The aim of this group is to partner to commercialise Stats NZ’s knowledge and expertise, and in so doing encourage the development of new and innovative ways to grow data access and analytics services for New Zealand. 2. Attached to this Aide Memoire is a slide pack that outlines their work so far and their vision for the future. Data Ventures will be added to a future officials’ meeting agenda for a more in depth discussion. 3. A soft launch of Data Ventures will take place on 20 February 2018, with an article in the NBR, and possibly in the New Zealand Herald and Dominion Post. 4. All queries should be directed to Stats NZ, however potential questions and answers are below. Questions and answers that may arise. Are we selling Stats NZ data? 5. No. Data Ventures will generate revenue by creating or licensing products and services built on top of data. What is Data Ventures doing with the revenue it generates? 6. Data Ventures will reinvest any revenue gathered back into its venture pipeline. Is Data Ventures all about making money? 7. No, Data Ventures will have both commercial to non-commercial ventures, with the commercial ventures proceeds funding the non-commercial ones. There is a 3:1 ratio of commercial to social good ventures. 8. All ventures that are not taken forward for commercialisation by Stats NZ will be packaged up and open sourced, so NZ citizens and businesses can take the opportunity and succeed where we could not. 9. All data acquired and created by Data Ventures is fed to Stats NZ for noncommercial benefits, such as improving existing data and statistical outputs. What is an example of the sorts of things Data Ventures might do? 10. Data Ventures will look to partner to gain access to commercial or private data sources, like those from accounting products, for example. These will then be brought together with Stats NZ expertise to more insights than Stats NZ could do alone. 11. Data from accounting products, for example, could be brought together to develop a mapping and classification of accounting data across all businesses to allow the development of more accurate business benchmarking tools. The result of this R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t would be better comparison and insights available openly to accountants and advisors to help both emerging and existing businesses flourish. 2 DATAVENTURES Stats? el ea ia se lI d nf u or nd m e at r t io h n Ac t We have a focus to experiment with Stats NZ to create economic “what ifs” and are teaming up with value for New Zealand in ways others to create partnerships others have not explored. delivering new ideas. R Data Ventures uses the best of el ea ia se lI d nf u or nd m e at r t io h n Ac t Why does Data Ventures exist? Stats NZ typically focuses on creating If you take a different perspective that is official government statistics to support away from the day to day of Stats NZ, use it critical decisions. to create new and aligned set of priorities, take the overall Stats NZ responsibility of However, at Stats NZ there are no lack of unleashing data to change lives and build ideas and opportunities that can be economic value... realised beyond that with the right R people and data. This is where you find Data Ventures. el ea ia se lI d nf u or nd m e at r t io h n Ac t Our vision is to be the place where data is valued. The value can be from the money that is made from developing commercial products and services, it can be the value gained internally for the people who work at Stats NZ, it can be through the partnerships (NZ Govt and private sector) it creates for others to do things, the currency it creates or the intangible value where people are better off R for knowing it. el ea ia se lI d nf u or nd m e at r t io h n Ac t What are we going to do? Data Ventures’ focus is on creating joint Instead, in the Data Ventures model the ventures with other parties, those being client and supplier negotiate their businesses and/or government for contributions as partners, invest commercial gain. accordingly and receive a share of returned value (typically revenue). We’re not talking about the traditional model of a client paying a supplier for developing a product for us and receiving R a discount. True partnerships. el ea ia se lI d nf u or nd m e at r t io h n Ac t What aren’t we going to do? R Sell Stats NZ data. el ea ia se lI d nf u or nd m e at r t io h n Ac t As a Data Ventures partner... You get access to data scientists, You can also rely on the trust and analysts and SME’s from a wide assurance that comes with the Stats range of disciplines on top of access NZ brand – to give confidence in your to Stats NZ IP of data, metadata, product or service making it to the methods and models to build market successfully. R products and services upon el ea ia se lI d nf u or nd m e at r t io h n Ac t A unique approach We will be testing not just the ventures to fund social good ventures. opportunities, but the partnered team. Every data source acquired or created by Any opportunity that fails to pass a gate at Data Ventures will be provided to Stats any point in the pipeline is packaged up NZ for non-commercial benefits such as (excluding any data) and then released as improving CPI/GDP. open source to the NZ public. R 3:1 investment ratio of commercial el ea ia se lI d nf u or nd m e at r t io h n Ac t Our mission is to find value where others have not looked and create a viable set of products and services based on working with others. We won’t always be the experts so that is why we will work with others to build on resources we don’t have to form joint ventures using data that R create amazing products and services. el ea ia se lI d nf u or nd m e at r t io h n Ac t Our gates pipeline 1st 2nd 3rd Clearly articulate Prove the concept, Secure first consumer, opportunity by interacting opportunity market fit, confirming value and and identifying potential technology, what future funding. customers through a lean partnerships are canvas. required and size of R effort to launch. el ea ia se lI d nf u or nd m e at r t io h n Ac t Our core team Our board Our advisory board Drew Broadley Liz MacPherson Internal Director Data Ventures Stats NZ CE / GS / GDS Five board members sourced from Hollie Kane Kelvin Watson Venture Coordinator Stats NZ Deputy CE Robert Chiu Victoria MacLennan Venture Manager Independent Board Member inside of Stats NZ External Five board members sourced independently of Stats NZ Customer Blair Willems Venture Manager Aimee Whitcroft CX Manager Gary Dunnet R DV to Stats NZ Advisor A range of customers, partners or ** Seeking two more sponsors interested in current and independent board future Data Ventures products and members services el ea ia se lI d nf u or nd m e at r t io h n Ac t What have we achieved so far? · Interviewed over 40 Stats NZ people to help form the vision and mission · Developed core business model and has been tested for interest · Designed the initial Data Venture pipeline based on parts from successful models used elsewhere R · Formed the core team el ea ia se lI d nf u or nd m e at r t io h n Ac t R What does success look like? · 10 Ventures reaching “second gate” · 10 Partnerships formed across private and government · 1+ Venture reaching “third gate” in market with customers · At least 5 Ventures being released as open source to the public · 3:1 ratio of commercial to social good ventures · Improved and proven Data Venture gates pipeline · 10+ customers on Customer Advisory Group · 20+ staff of Stats NZ as been part of the Data Ventures experience el ea ia se lI d nf u or nd m e at r t io h n Ac t What’s next? 20th Feb Brand launch · 19th Mar Dry run of gates pipeline that has been developed · 30th Mar Customer Group formed and active · 2nd Apr Run opportunities through lean canvas, highlighting top 10 · 30th Apr First venture hits the start gate R · el ea ia se lI d nf u or nd m e at r t io h n Ac t R Drew Broadley dataventures@stats.govt.nz +64 21 519 711 Memorandum Hon James Shaw Date: 6 April 2018 R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t To: Subject: Update on Data Ventures 1. As mentioned by Drew Broadley (Director of Data Ventures) when he last met with Minister Shaw on 26 February 2018, it was signalled the Minister would be supplied with the first ventures that Data Ventures will be focusing on. 2. As part of Data Ventures’ open principles and open standards, we presented on Open Data Day a timeline of milestones. Aligned with those timings, we have released a high level blog post and twitter update of the first ventures. 3. Over the next three weeks (9th April – 27th April) we will be releasing the ten lean canvases to our blog (https://medium.com/data-ventures) and through our twitter account (https://twitter.com/dataventuresnz) relating to these ventures. 4. The ten lean canvases are attached. Improved aerial surveys - lean canvas Time horizon: 12 months/start of MVP. Reference: adapted from Problem New Zealand - as a whole - lacks a source of freq uently-updated, high-quality aerial imagery. High-resolution satellite imagery is currently very expensive to buy, especially at a national scale. There is currently a demand for better aerial imagery for monitoring water quality, vegetation (including crops) and planning-related matters (eg land use, buildings). Existing alternatives Imagery: Commercial satellite imagery providers, existing small plane and drone providers. Monitoring: various private and government providers, mostly using sensors. Solutio Make use of existing regional airplanes, and other aviation groups. Develop an inexpensive, high-definition camera solution that can be attached to the undersides of planes etc. [Further down line, we can add other measuring devices, too.] Value proposition Access to frequently updated aerial imagery, taken multispectrally. Access to storage and analysis facilities through Stats NZ. Key metrics Affordable (less expensive than commercial satellite imagery). Updated once a week (minimum). Cross-country uptake. High level concept Affordable, up-to-date, high-quality aerial imagery for use in your industry or sector. Adva nta ge Access to Stats NZ data experts and facilities. Customer segments Agricultu ral/horticultural sector. Local government. Environmental monitors, including govt. Cha nels Networks, cu rrent strategic relationships. Industry groups. Fieldays and other industry events. Early adopters Farmers. Urban planners/city councils. Complexity: 2. Risk: 3. Effort: 3. Acquisition: 2. Cost structure l1 lowest- 5 highest) Revenue streams Commercial industries will want it for monitoring and ef?ciency purposes. Government will want it for monitoring and reporting purposes - eg water quality. Local governments etc will want it for up to date aerial views of buildings, land use etc. Community groups and the public will want it for any numbers of uses, many as yet unknown. All of govt business Time horizon: 12 months/start of MVP. Reference: adapted from Problem Businesses find it a burden have to enter the same data multiple times for different government agencies. Public agencies don't have the most up-to- date data for businesses, as the data's scattered across agencies. Existing alternatives TBC. Solutio Investigate if the fragmented business register across government can be combined Investigate if the attributes captured by combining the business register meets needs across government Investigate how to centrally maintain this business register Value proposition Businesses will only have to enter business data once when dealing with government. Business data will always be up to date for government agencies. Key metrics Increase in the number of agencies supporting (through access and use) of the single register Improve business customer experience through survey reduction in the number of instances of errors due to dated information High level concept Improving the all-of-government business register with data of value and lower data burdens on businesses. Advantage Stats NZ maintains a unique business register that has IP with its modelling. Channels Government forums. BusinessNZ, other industry bodies. Customer segments Central government. Businesses that interact frequently with government. Early adopters Businesses that are legally required to supply business data to government. Agencies that collect or create outputs from business data. Cost structure (1 lowest. 5 Complexity: 2. Risk: 1. Effort: 2. Acquisition: 1. hiahestl Revenue streams Other government agencies who maintain their own business register will contribute to this venture, as it Central government will fund the register's maintenance, as it has value for developing other products and Business data for Time horizon: 12 months/start of MVP. Reference: adapted from Problem Retail and hospitality businesses that are looking for a physical location don't often have the data to identify the best place to set up. They may also not know of available properties in the best areas. Existing alternatives QV, Homes, Trade Me, realestate.co.nz. Local councils. Solution Acquire the valueable data sources from various providers. Place data onto a visualisation that customers can customise their own data preferences. Key metrics Number of referrals from the application to rental agencies. Value proposition Consumers will have better data to inform the optimal location to successfully set up their business. They'll also have options for available rental spaces. High level concept A tool that helps businesses ?nd the best available place to set up shop. Adva nta ge The knowledge of relevant available data and the expertise to present this data in a useable way Channels Retail and hospitality consultancies. Customer segments Retailers (bricks and morter), hospitality, consultancy organisations, loan providers. Early adopters Consultancy organisations. Complexity: 3. Risk: 1. Cost structure (1 lowest- 5 highestl Effort: 2. [There's a dependency factor on another ventu re.] Acquisition: 2. [There's a dependency on another venture] Revenue streams Consumers will have have access to free data and will pay a small amount for select data sets. Rental agencies will pay a referral fee for traffic to rental listings that leads to sales. Community data Time horizon: 12 months/start of MVP. Reference: adapted Working _with_ communities to build capabilities and skills. Offer background support. [There should be deliverables, notjust research papers.] providers contractors. Anything that communities agree to publish, will be published openly under Existing alternatives Alternatives for capability: consultants. Alternatives for capability: contractors. No alternatives for data: Stats NZ have access to data that no one else does, and at a depth (eg years of it) that others don't. They also have access to non-open sources of other govt data. Key metrics Set for each community as part of the work programme. That Stats NZ have got at least one running by December 2018 (indicatively). High level concept Providing deep data expertise to local government and community from Problem Solution Value proposition Advantage Customer segments Communities lack expertise with respect Embed Stats NZ experts in communities Stats NZ comes with data. Using Stats NZ data, data experts and data Local govt. to data and analytics (from infrastructure (min 6 months). access. to understanding). Communities lack access to/ knowledge of Help communities write IDI applications, Stats NZ has expertise in bringing different Stats NZ is a government agency, so no lwi. where to ?nd data to help them improve and do the research. kinds of data together (can integrate theirs "private" or pro?t motives, and have decision-making. and communities'). lgovemment commitment to behaving well, including things like the OGP commitments. They're cheaper than commercial NGOs. Communities/ people interested in particular issues etc. Cha nels Relationship manager to interact between community and Stats NZ. Networks, current strategic relationships. IKnown community groups and NGOs. Universities (see pipeline venture). Govt innovation pipelines (eg Westpac). International partner (CBS). Early adop ters Keen to pilot. Have a speci?c problem statement (ie Iknow where they'd like to begin). Have the necessary funding. lntemal organisational buy-in (on their side). Access to appropriate technologies? Cost structure (1 lowest, 5 Complexity: 2. Risk: 3. Effort: 2. Acquisition: 1. highest) Revenue streams Cost recovery from the organisations in which we're embedding experts. Grants and other funding streams. Data science brokering - lean canvas Time horizon: 12 months/start of Reference: adapted from and generated from Data Ventures lean ranvac Problem Small agencies have problems they're trying to solve that would bene?t from data science. Some don't know about the value data science might add. The problems may also be too small to go through a procurement process. Recent data science graduates don't have opportunities for practical experience around their theoretical knowledge, and some may get out of practice with recently learned skills. Existing alternatives TBC. Solution Partnerships with agencies and education providers to supply problems and grads. Create environments (eg IT, physical) for grads to apply their training and help solve problems. Key metrics Survey of agencies about the experience of service. Survey of grads about their experience of the service. Growth in the number of grads and agencies participating in the venture. Value proposition Small agencies will be able to test the value of data science in their problem- solving processes. Recent graduates will get an opportunity to apply what they've learned to real-world scenarios. High level concept For agencies: Dip your toes into the bene?ts of data science. For grads: gain real-world experience in solving problems with data science. Adva ntage Stats NZ's knowledge of the data science needs of the public sector. Stats NZ's ability to provide input on accreditations and their curricula. Channels Organisations that support data science capability. Customer segments Small agencies, local councils, iwi, NGOs. Education and data science education providers. Stats NZ. Early adopters Small agencies. Data science grads. Cost structure (1 lowest, 5 highest) Complexity: 3. Risk: 4. Effort: 3. Acquisition: 3. Revenue streams Central government agencies that are incentivised to grow data science capability - sponsorship. Education providers interested in increasing the value of their accreditation - sponsorship or fees. Agencies will pay grads - Data Ventures will clip the ticket Data Ventures' lean Time horizon: 12 months/start of MVP Reference: adapted from canvas template om/. Problem Solution Value Proposition Advantage Customer segments Used car buyers and sellers don't have easy access to benchmarking tools which allow them to set/pay a fair price. Existing alternatives Buyers/sellers' own market research. Provide a benchmarking tool for used cars. Visually display car sale/pu rchase histories. Buyers and sellers will be able to make better-informed decisions, based on more accurate data, about what to ask or pay for Stats NZ and NZTA have the data. Anyone buying or selling used cars. Key metrics Sales metrics - length of time to sell/buy. Return users/purchases. Increased trust with car dealers. High level concept Take the guesswork out of buying and selling cars. Channels App store. Advertising on TradeMe. LMVD. Awareness outside car yards. Early adopters Marketplaces where cars are bought and sold. Cost structure Complexity: 1. Risk: 1. Effort: 2. Acquisition: l. Revenue stre Commission on sales. Referral fees. Advertising. ams Subscription to app/service. . - Time horizon: 12 Reference: adapted from and generated from Data Dy II a IC rates, lea ca nvas monthS/start of Ventures lean canvas template: b.com/dataventu resnz/ventu re-dv MVP. Problem Solution Value proposition Advantage Customer segments Often tenants of retail type businesses are A dynamic model that indicates the A retailer receives appropriate pricing Increased frequency to adapt to market Local government affected by unforseen/unplanned appropriate rate/rental/lease for the through more frequent rates/levies/rental changes Commercial property owners circumstances or planned infrastructure location according to the factors that could changes according to their current Commercial property managers upgrades/changes. This can be anything affect this, at a period of time that is at the opportunity market. Real estate agents from an earthquake, a mall opening up day/week range rather than the long term nearby or a roading/transport change. lease range of many years. These cause a change in the opportunity market for the retailers and could be the difference between surviving or closing due to high rental prices even though it's no longer high street retail due to these changes (even if they are only temporary) Existing alternatives Key metrics High level concept Channels Early adopters Colliers Retailers impact is reduced during A retailer is impacted by a mall opening up Through local governments Local government. Market research performed by infrastructure changes (as recorded by a few streets away, removing a large landlord/commercial property manager council complains levels) amount of normal window shopping traf?c. This retailer relies on this foot traf?c Decrease in number of businesses closing. to fund their six year lease, but the mall (resilience) plans were not available at the time they Cost structure (1 lowest, 5 highest) Revenue streams Complexity: 3. This model can be adapted by commercial property managers as a way to sustain longer term customers and Risk: 3. Effort: 2. [There's a dependency factor on another ventu re.] Acquisition: 2. [There's a dependency on another venture.] Improved Time horizon: 12 months/start of MVP. Reference: adapted from Problem Current environmental risk modelling - for insurers, banks, councils etc - is generally based on historical events. Climate change is bringing increasing uncertainty to these models - historical models no longer work as well, and research suggests the models need to improve. Existing alternatives Risk forecasting tools which can pull data from a number of sources. Existing risk and systems. Solution Pull together data from a number of sources, including resilience research, risk models, GIS maps. Value proposition Banks and other insurers can more accurately insure physical assets. End-users can be charged more accurate insurance rates, and have a better idea of the environmental risks to their physical assets. Key metrics Number of customers using the tools. Reliability/accuracy of forecasting models. Gained efficiencies in civil defense situations. High level concept Providing more accurate information about the risks from environmental Adva nta ge Access to Stats NZ data that may not be generally available. The ability to broker between different data holders. The ability to integrate different datasets. Cha nnels Existing networks and strategic relationships. Industry groups and events. Customer segments Banks and insurers. Local govt. Ememergency and civil defense organisations. Early adopters TBC. Cost structure (1 lowest, 5 highest) Complexity: 5. Risk: 4. Effort: 4. Acquisition: 4. Revenue streams Various customer groups. Platforms and organisations which already deal with property and resource use. Location data Time horizon: 12 months/start of MVP. Reference: adapted from Problem Government agencies aren't sure about how best to use location data in solving some of their problems. At the moment, the price for accessing this data is too high to justify exploring use cases. Existing alternatives TBC. Solutio Explore current potential use cases for government. Work with data partners to set up a secure test environment for agencies to test technical designs and concepts. Value proposition Government will identify new use cases for location data, and will be able to accurately determine the value of this data. Pooling government resources will mitigate some cost concerns. Key metrics Number of agencies requesting access. Number of use cases identified. High level concept A sandpit environment for government Adva nta ge Data Ventures have been nominated by a number of central agencies to lead this venture. Channels Government data forums. Location data providers. Customer segments Central government, local government, data suppliers, iwi, NGOs. Early adopters Central government. Location data providers. Cost structure (1 lowest, 5 highest) Complexity: 3. Risk: 5. Effort: 3. Acquisition: 2. Revenue streams Agencies will pay for value-add services (eg data science). If partners provide additional services, they will pay a referral fee. Government agencies will pay a small subscription fee to access the data. Proof of purchase - lean canvas Time horizon: 12 months/start of MVP. Reference: adapted from and generated from Data Ventures lean canvas template: re-dv The problem When consumers purchase products at a store, they are often without the habit to keep the receipt as proof of purchase. Because ofthat many products are either never returned when they should, never able to be successfully returned as the product could be bought at countless stores (even online) or take a very long time for the service team to look up a past purchase while the consumer at the same time looks through their internet banking to ?nd that purchase (but not everyone has access to this). Existing alternatives Paypr by Paymark (recently closed) Briscoes Group (and others) who currently offer this using last four digits of card used to purchase Many online retailer platforms such as Shopify Our solution We will match an electronic payment to a purchase in the POS when it wasn't previously connected to maintain as much privacy and security as possible. This focus is not on the consumer to have the technology, but the retailer so there is accessibility beyond people who have smart phones. Key metrics Time saved Accuracy of returns Reduction of fraud Unique value proposition Reduces the time needed to prove a purchase, on the consumer no longer needing to ?nd a receipt and the retailer no longer having to look up wildy through their previous purchases. Not relying on a single payment method. Reduces the risk of receipt fraud for the merchant. Saves paper. High level concept As consumers make purchases everyday around the country, there is a percentage of these that will be returned either within the next 24 hours, or the coming years. This timing also relies on you maintaining a receipt for a proof of purchase. Now, a customer is able to forget any habit needed to remember their receipt. They're Unfair advantage The past experience around proving purchase electronically, and Ieaming from the past ventures from ourselves or others we are able to bring together a simple solution. Channels Retail NZ Banks Point of Sale Providers Customer segments Retailers (online bricks and mortar) Consumers Insurers Early adop ters Retailers who tend to aim for the lower cost stock that tend to service a lot of broken products Consumers who purchase at these stores regularly Cost structure Complexity: 3. Risk: 2. Effort: 3. Acquisition: 2. Revenue streams Revenue model, life time value, gross margin, etc. A retailer would typically pay for a product like this on a base access fee graded to a teir based on A small fee could be asked from the consumer as a time saver if they didn't retain a receipt Standard for accounts - lean canvas Time horizon: 12 months/start of MVP. Reference: adapted from and generated from Data Ventures lean canvas template: Problem Business advisors/accountants and their clients work within their own codings of how their money is used in a business. This means a business adviser/accountant cannot get a single look into the way their various clients ?le their activites. This problem doesn't exist at a local environment, but a regional and national view. Existing alternatives Reporting tools such as Spotlight Reporting, Castaway Forecasting Accounting integration tools such as Common Ledger, MYOB Portal Practice Manager Solution A classi?cation that can translate various business accounting data (including journal entries) into a standard view so others can then create their own look into the data. Value proposition Ability to translate any businesses accounting data from any accounting software into the chart of accounts that the business advisor/accountant understands. Key metrics Number of businesses accounts categorised Number of accounting products using classi?cation High level concept As the number of accounting software packages increase, so do the vast number of ways clients tag the view on their business activities. We will help by creating a standard classi?cation wrapped with Adva nta ge We have the experience across many businesses accounting data to benchmark and become an authority to create a standard classification for translations. Customer segments Business advisors Accounting product and tools Cha nels Chartered Accountants Australia and New Zealand (CAANZ) Certi?ed Public Accountant (CPA) Accounting products and tools Early adopters Any existing benchmarking products produced or used by business advisors Cost structure (1 lowest, 5 Complexity: 4. Risk: 2. Effort: 3. Acquisition: 2. highest) Revenue streams Our ?rst impressions of this is an open source opportunity to generally help NZ businesses and business R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t Report to the Minister of Statistics: Weekly report for the period to 23 February 2018 Date: 23 February 2018 Priority: Medium Security level: In confidence File number: MM1771 Contact details Name Position Telephone Grace McLean Private Secretary to the Minister of Statistics 9(2)(a) 9(2)(a) Matthew Bloomer Manager, Office of the Government Statistician and Chief Executive 9(2)(a) 9(2)(a) First contact X Purpose ▪ The weekly report is prepared by officials every Friday (unless otherwise specified). The report provides you with a regular update on the business of Stats NZ. ▪ No action is required from you; officials are available to brief you further at your request Regular progress updates Officials will provide you regular progress updates on the following topics, as appropriate: ▪ Role and activities regarding the Government Chief Data Steward ▪ 2018 Census ▪ Census Transformation ▪ Statistics Legislative Review ▪ Engagement with the Iwi Chairs Forum: Leadership Group on Data ▪ Government Priorities (including: Measuring child poverty, foreign property ownership and a comprehensive set of environmental, social and economic sustainability indicators) ▪ Stats NZ’s accommodation For your information D5 events this week Digital Nations 2030 Summit in Auckland 1. Stats NZ representatives attended the Digital Nations Summit in Auckland this week, with the Government Statistician Liz MacPherson being on a panel discussing Big Data for predictive outcomes. R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t 2. One of the main impressions from the D5 sessions was remarkable consistency in the views of panellists that capability, culture and education were all critical to enabling us to become a truly digital nation. 3. NZ is well placed in its journey, and there are many examples of digital innovation across the country. It was commented a number of times that innovation is part of the kiwi ‘number 8 wire’ psyche. 4. Technology is changing fast, driverless cars and blockchain are only two examples, and we need to be ready to embrace disruptive changes of this kind through enabling legislation and regulation. 5. Trust and social license, encompassing privacy and security, are fundamental to enabling citizens to embrace a digital government. Digital Government Showcase 6. Stats NZ’s Integrated Data display at the D5 Digital Government Showcase was a great success. Many national and international government officials made it a priority to visit our stand, showing the amount of interest there is in this world-leading technology. 7. International visitors included government officials and ministers from Estonia, Uruguay and the Canadian Government’s Chief Information Officer. They were specifically looking out for the integrated data stand, as their governments are wanting to learn from us how they can implement integrated data infrastructures. Tongan officials, and the UK High Commissioner to NZ were already familiar with the Integrated Data Infrastructure (IDI) and interested to find our more. Government Statistician Liz MacPherson was on hand to talk about how Stats NZ started the IDIand the next stages. 8. It was also a great opportunity to discuss with officials from other New Zealand government departments how Stats NZ both empowers and safeguards the use of integrated data as part of our aim to be as transparent as possible about how New Zealanders’ data is being used and for what outcomes. Data Venture launch 9. On the 21 February, Stats NZ launched our Data Ventures group through a NBR article, with the aim to create greater awareness of this new venture. At the same time, the Data Ventures website, https://dataventures.nz/, went live. Data Ventures will partner to commercialise Stats NZ knowledge and expertise, and in so doing encourage the development of new and innovative ways to grow data access and analytics services for New Zealand. 10. Prior to this launch, Drew Broadley, the Executive Director, has been building the core team of Data Ventures, while also testing, taking feedback and shaping the story of Data Ventures. This has been tested with Stats NZ people internally, other government agencies (particularly a great opportunity at Digital Nations 2030) and a select set of companies and business people. 11. This was all about testing the Data Ventures offering and validating there is interest from the potential partners and customers working with Data Ventures. 12. Response has been good, including comments around it being progressive and forward thinking, and successfully creating inbound contacts with people looking to partner with us. 13. We are working towards building a pipeline workflow, recruiting for a customer advisory group (which Data Ventures will use to validate ventures) and starting the first venture by 30 April. 14. Drew will attend Monday’s officials’ meeting, to update you further on Data Ventures and its plans. R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t 2018 Census ramping up to the big day 15. This week, 2018 Census field workers have started delivering letters with access codes to 20 percent of the country’s households. Every household needs their code to complete the 2018 Census online, on or before 6 March. 16. From today (Friday, 23 February) onwards, the remaining 80 percent will start to receive letters containing the codes. Parliamentary Questions None for this period Departmental Official Information Act requests Date received Requester Subject Due date Date completed 03/02/2018 9(2)(a) Advice to the Minister and the GS regarding sexual orientation and gender identity in the 2018 Census 5/03/2018 23/02/18 Open Data Funding 16/03/2018 9(2)(a) 16/02/2018 9(2)(a) 9(2)(a) 17/02/2018 9(2)(a) Advice to the current 16/03/2018 23/02/18 minister regarding spirituality and religion in the census 9(2)(a) 22/02/2018 9(2)(a) 9(2)(a) Census exception reports or change requests, and 2018 census programme team meeting minutes 22/03/2018 Public relations None for this period 3 Cabinet Papers received for departmental consultation R O e ffi le ci as al e In d u fo n rm de at r t io he n Ac t 9(2)(g)(i) 4 For your attention Brie?ngs I Parliamentary Question OIA Other Stats NZ Cabinet Papers for Minister?s signature I Meeting I Event Public Relation 19 February 2018 20 February 2018 21 February 2018 22 February 2018 23 February 2018 I Stats NZ of?cials meeting March Baseline update I D5 conference (chairing I Meeting with Rhema session on emerging issues in Viathianathan d'gltal trade) Agriculture production survey I D5 cocktail function approval 26 February 2018 27 February 2018 28 February 2018 1 March 2018 2 March 2018 I Stats NZ of?cials meeting I SEEA release briefing I Meeting with Sir Peter I Open Data Day (opening) . . Gluckman I Meeting With Hon Scott Simpson 5 March 2018 6 March 2018 7 March 2018 8 March 2018 9 March 2018 I Stats NZ of?cials meeting CENSUS DAY I Ministerial meeting on 2018 Budget decisions 12 March 2018 I Stats NZ of?cials meeting 13 March 2018 14 March 2018 15 March 2018 16 March 2018 Release calendar I Stats NZ release I Analytical report 19 February 2018 I Births and Deaths: Year 20 February 2018 I Business Price Indexes: 21 February 2018 22 February 2018 I Productivity Statistics: 1978? 23 February 2018 I Retail Trade Survey: I Alcohol available for consumption: Year ended December 2017 I Linked employer-employee data: December 2016 quarter 5 March 2018 I New Zealand cohort life tables: March 2018 update 12 March 2018 I Accommodation survey: January 2018 I Overseas merchandise trade: January 2018 I International travel and migration: January 2018 6 March 2018 CENSUS DAY 13 March 2018 I Food price index: February 2018 I Overseas trade indexes (prices and volumes): December 2017 quarter (provisional) ended December 2017 December 2017 quarter 2017 December 2017 quarter I New Zealand Abridged Period Life Table: 2015?17 (provisional) 26 February 2018 27 February 2018 28 February 2018 1 March 2018 2 March 2018 I Building consents issued: January 2018 I Goods and services trade by country: Year ended December 2017 I International visitor arrivals to New Zealand: January 2018 7 March 2018 I Local authority statistics: December 2017 quarter I Value of building work put in place: December 2017 quarter I Wholesale trade survey: December 2017 quarter 8 March 2018 I Economic survey of manufacturing: December 2017 quarter 9 March 2018 I Electronic card transactions: February 2018 14 March 2018 I Balance of payments and international investment position: December 2017 quarter 15 March 2018 I Gross domestic product: December 2017 quarter 16 March 2018 I Transport vehicle registrations: February 2018