--- title: Government Case Studies | Datamine description: Data analytics for Government and FMCG businesses improves engagement, investment ROI, policy and strategy. Case studies for data science success. image: https://www.datamine.com/hubfs/Imported_Blog_Media/Government%2017.png --- [![Datamine](https://www.datamine.com/hs-fs/hubfs/Imported_Blog_Media/Logo-for-website-white-01.png?width=417&name=Logo-for-website-white-01.png "Datamine")](https://www.datamine.com/) - [Working together](https://www.datamine.com/how-to-drive-commercial-growth-with-data-analytics-and-ai-datamine) - [How we work together](https://www.datamine.com/how-to-drive-commercial-growth-with-data-analytics-and-ai-datamine) - [Our Workshops](https://www.datamine.com/envisaging-and-development-workshops) - [Our Pricing](https://www.datamine.com/how-much-does-data-analytics-and-ai-cost) - [Solutions](https://www.datamine.com/oursolutions) - [Case Studies](https://www.datamine.com/industryexpertise/case-studies) - [Retail](https://www.datamine.com/industryexpertise/case-studies/retail) - [FMCG](https://www.datamine.com/industryexpertise/case-studies/fmcg) - [Travel](https://www.datamine.com/industryexpertise/case-studies/travel-tourism) - [Not for Profit](https://www.datamine.com/industryexpertise/case-studies/not-for-profit) - [Media & Entertainment](https://www.datamine.com/industryexpertise/case-studies/media-and-entertainment) - [Telco](https://www.datamine.com/industryexpertise/case-studies/telco-and-technology) - [Utilities](https://www.datamine.com/industryexpertise/case-studies/energy-and-utilities) - [Health & Wellness](https://www.datamine.com/industryexpertise/case-studies/health-and-wellness) - [Government](https://www.datamine.com/industryexpertise/case-studies/government) - [Finance](https://www.datamine.com/industryexpertise/case-studies/banking-and-finance) - [Insurance](https://www.datamine.com/industryexpertise/case-studies/insurance) - [Real Estate](https://www.datamine.com/industryexpertise/case-studies/real-estate) - [Industries](https://www.datamine.com/industry) - [Retail](https://www.datamine.com/industry/retail) - [FMCG](https://www.datamine.com/industry/fmcg) - [Travel](https://www.datamine.com/industry/travel-and-tourism) - [Health & Wellness](https://www.datamine.com/industry/health-and-wellness) - [Not for Profit](https://www.datamine.com/industry/not-for-profit) - [Media & Entertainment](https://www.datamine.com/industry/media-and-entertainment) - [Telco](https://www.datamine.com/industry/telco) - [Utilities](https://www.datamine.com/industry/utilities) - [Government](https://www.datamine.com/industry/government-and-public-sector) - [Finance](https://www.datamine.com/industry/banking-and-finance) - [Insurance](https://www.datamine.com/industry/insurance) - [Discover](https://www.datamine.com/resources) - [Products](https://www.datamine.com/what-we-do/our-products) - [WeekAhead](https://www.datamine.com/weekahead) - [Retailwatch](https://www.datamine.com/ourservices/retailwatch) - [Site Spotter](https://www.datamine.com/ourservices/sitespotter) - [NZ Fuel Prices](https://www.datamine.com/data/fuel-price-nz) - Insights - [Executive Insights](https://www.datamine.com/executive-insights) - [The Boardroom Table](https://www.datamine.com/theboardroomtable) - [Retailwatch Dashboard](https://www.datamine.com/reports/retailwatch-dashboard) - [Telcowatch Report](https://www.datamine.com/telcowatch) - [Downloadable Guides](https://www.datamine.com/resources/datamine-guides) - [Datafix Blog](https://www.datamine.com/datafix) - [The Boardroom Table](https://www.datamine.com/theboardroomtable) - [About us](https://www.datamine.com/about-us) - [How we work together](https://www.datamine.com/how-to-drive-commercial-growth-with-data-analytics-and-ai-datamine) - [The Datamine Story](https://www.datamine.com/the-datamine-story) - [Our People](https://www.datamine.com/being-a-dataminer/our-people) - [Our Values](https://www.datamine.com/en-au/about-us/our-values) - [Our Partners](https://www.datamine.com/company-we-keep-tech-partners) - [Join Us](https://www.datamine.com/being-a-dataminer/join-us) - [Contact us](https://www.datamine.com/contact-us) # government case studies ## GOVERNMENT ![Government-1](https://www.datamine.com/hs-fs/hubfs/Iconography/Black/Government-1.png?width=45&name=Government-1.png)   Datamine has worked with numerous Kiwi and Aussie government and public sector organisations over the years, including the Ministry of Health, IRD, Ministry of Transport, Fire and Emergency and more.  We provide the government sector with state-of-the-art predictive tools and enhanced analytics. Scroll down to find out how Datamine has helped a number of public sector organisations respond to industry challenges and trends.   # AUCKLAND TRANSPORT BASELINES ECONOMIC REDEVELOPMENT FOR ONE OF THEIR BUSIEST URBAN CORRIDORS **THE CHALLENGE** To keep up with the demands of a growing city, [Auckland Transport (AT)](https://at.govt.nz/) is looking to redevelop the Great North Road corridor to deliver a safer, more appealing route for pedestrians and bike riders, while making bus journeys in peak times more reliable and reducing congestion along the route. Auckland Transport wanted to understand the key metrics to track for the development and to proactively manage the impact of its urban redevelopment project with local communities and businesses. **THE SOLUTION** Datamine created an economic baseline using social and economic metrics such as the usual spend in the area, retail and hospitality category performance over time, and retail and hospitality category performance in popular local hot spots along the corridor. It looked at how the business/retail mix was changing over time, the impact of Covid lockdowns, and how long customers spent shopping or dining in the area. **THE RESULT** The dashboard and key metrics delivered to the Auckland Transport team were well received and equips Auckland transport to assess the impact of the redevelopment project pre, post and during development. The findings also enable Auckland Transport to proactively collaborate with community stakeholders impacted by the change along the way.   # auckland transport gets insight into redevelopment impacts **THE CHALLENGE** In order to keep pace with the growth rate of New Zealand’s largest city, Auckland Transport has around 30 redevelopment projects planned over the coming 10 years, some of which have already begun. These projects can last a long time and impact surrounding areas, so Auckland Transport wanted to determine a set of metrics they could use moving forward that would help them proactively manage the impact on the local communities. They approached Datamine about providing a proof of concept for a dashboard that could be implanted across all redevelopments moving forward, beginning with the project in Mount Albert that finished mid-2019. **THE SOLUTION** The Datamine team worked with Auckland Transport to come up with six key questions to serve as the base of the social and economic impact analysis of the Mount Albert redevelopment. The questions centre around impacts on travel, retail sales, time spent in the area and more, and they can be applied to all redevelopments moving forward. From there, we used a combination of AT HOP data, census information and geo-demographic data to answer the questions for Auckland Transport and identify the communities that have felt the greatest impact. **THE RESULT** The proof of concept that we delivered to Auckland Transport team was well-received by the wider team. The analysis provided by Datamine outlined the redevelopment’s impact on dwell times, traffic, retailers, public transport, catchment and more, providing Auckland Transport with empirical evidence about the effects of the project. For example, the data revealed that there was an impact on local retailers, but it was smaller than generally perceived, and the recovery rates after the redevelopment were strong. We also discovered that the traffic has halved post-redevelopment and peak period dwell times (how long people are spending in the town centre) have increased by nearly 10%. Auckland Transport is now able to use all this information to assess the impact of the project and communicate those impacts with the community appropriately moving forward. # Ministry of Health gains insight into pay equity **THE CHALLENGE** In 1960 the Government Service Equal Pay Act gave women working in the public sector the same pay as men when they were doing the same job. Since then, women have moved into the workforce in increasing numbers, and in new areas.  While significant progress has been made, there is still a gap in the average earnings of women and men, and various occupations are still dominated by one gender. As part of a government wide initiative to investigate why the gaps exist, the Ministry of Health asked Datamine to contribute to and inform their pay equity review process by conducting some detailed analysis of their pay and entitlements profile, with regard to gender representation and distribution. **THE SOLUTION** After some initial exploratory analysis of the data, Datamine built nine individual models for each of the Ministry’s workgroups such as Policy Analyst, Advisor etc.  Using multiple linear regression, the models measured the effect of the following factors on pay equity: - Gender - Ethnicity - Age - Role - Job size - Directorate - Organisational unit - Region - Manager's gender - Agreement type - Permanent/Temporary - Annual leave - Performance rating - Tenure **THE RESULT** In five of the models, some evidence of gender differences was found, including gender interactions (relationship between the gender of the manager and employee).  In all cases, the size of the job explained the majority of the variations in total fixed remuneration.  For most workgroups there was insufficient evidence to suggest that the Ministry of Health paid male employees any more or less generously than females.  The analysis helped inform the remainder of the Ministry’s review process and helped eliminate potential misinterpretations of the data. # DATAMINE FORECASTS FUTURE COSTS FOR THE DEPT OF CORRECTIONS **THE CHALLENGE ** On occasion, prisons have to operate at over capacity, with more prisoners than available beds.  While the correlation between inmate numbers and prison officers is normally linear, Corrections had observed that this changed when a prison was full - with more staff time required for things like finding temporary accommodation and co-ordinating prison transfers.  Although this could be managed using overtime, it was costly and not sustainable.  Concerned that the appropriate staff funding was in place to manage occasional over capacity situations, Corrections tasked Datamine with creating a robust business case for Treasury that accurately estimated the extra funding required to cover projected increases in prisoner numbers. **THE SOLUTION** Datamine developed a data-driven approach to ensure a sound business case.  The solution correlated payroll data, hours worked, inmate numbers, facility capacities and Ministry of Justice forecasts to predict future staff costs associated with an increase in prisoner numbers.   **THE RESULT ** The analysis revealed that staff costs increased from less than $380 per prisoner per week when occupancy was below 99% to over $420 per prisoner when occupancy rose to over 102%.  When the prisons are nearly full, a 3% increase in the number of prisoners results in a 13% increase in staff costs.  Quantifying these costs allowed a clear case to be put to Treasury. To help Corrections continue to manage the increasing prisoner numbers, and provide an early warning of expected increases, Datamine also delivered an easy-to-use forecasting tool.  Using this application Corrections now has a six-month forecast of expected prisoner numbers which assists in operational planning. # GOVT INSURER EVALUATES CUSTOMER SATISFACTION THROUGH SEGMENTATION **THE CHALLENGE** With overall responsibility for providing no-fault personal injury cover, a government-owned insurer wanted to develop a more customer-centric approach to the way it engaged with small businesses and asked Datamine to clearly establish two things: 1. What was most important to small business clients in relation to the delivery of the insurer's services? 2. How was the insurer performing in meeting those needs? Being able to measure these variables and then identify distinct groups of customers with similar needs, the company hoped to gain actionable intelligence that would enable it to develop services that would increase overall customer satisfaction. **THE SOLUTION** By combining the insurer's existing small business customer data with BRC Research survey data relating to a sample of those customers, Datamine was able to develop a small business customer segmentation model for the company that answered both questions.   **THE RESULT** Datamine analysis delivered the following rich information: - Primary and secondary client drivers – in terms of what aspects were most important to customer groups in their dealings with the company (by extrapolating the survey data across all customers). - Business customer demographics – including industry, number of employees, turnover, and company age. - Transactional satisfaction data – with regard to previous interactions and outcomes around dealings with the insurer. # FORENSIC SCIENCE UNIT UNDERGOES A DISCOVERY ANALYSIS **THE CHALLENGE** ESR is the sole provider of forensic science services to the New Zealand Police, and frequently undertake work for other Government agencies and commercial partners.  Within ESR, the Forensic Science Unit (FSU) analyses human tissue, crime-scene trace evidence, bodily samples and any other evidential material, with their comprehensive knowledge of the presence and interpretation of DNA utilised across the country and around the world.   Offence Against Person (OAP) cases are assigned to the FSU with a single accompanying time-based Service Level Agreement (SLA).  This assumes that all cases are uniform in their complexity, when in reality there are multiple inputs of varying sizes, quality and types per case, over a period of time.  In addition there are external drivers that lead to ad hoc prioritisation of cases. ESR tasked Datamine with providing a comprehensive understanding of cases, including patterns of inputs, timeliness and case profiles.  ESR plans to use the results to improve efficiency of the FSU, and enable the informed setting of SLAs. **THE SOLUTION** Using anonymised operational OAP metadata, Datamine defined and analysed key measurements and variables for TAT (turnaround time) of cases across a defined period of time.  ESR had recently updated their internal processes, thus the goal of the analysis was to quantify whether the updated processes had led to an improvement in TAT, as well as identifying additional areas for improvement. Datamine’s discovery analysis identified patterns and lags between time events and pathways in each value chain.  For example: - the number of business days from first exhibit received and last report sent, - numbers of containers, analysis per case, exhibits and sub-exhibits per case Key interpretations reported the number of cases completed within the prescribed SLA and the sort of cases where the SLA was infeasible.   **THE RESULT** Recommendations made to ESR include: - Setting individual SLAs for cases based on case-specific variables such as the number of exhibits in the case - Aiming to minimise time between exhibits received and analysis start, in order to reduce the average TAT Datamine’s analysis has been used as a bench mark for other case types and has enabled discussions between ESR and the police to address operational lags in the process. ESR’s focus is now on understanding the interrelationships between cases and the impact they have on each other with the goal of being able to predict the time a case will take depending on the inputs to the case in any given time so that realistic expectations can be set. # OPTIMISING RETAIL OFFERINGS IN A TOURIST CITY **THE CHALLENGE** With the introduction of an international airport, a small tourist city had become a gateway to its region and local government leaders wanted to use this opportunity to create economic growth in the region – particularly in its retail sector.  To do so it needed to understand the top-line differences between the retail sectors of itself and other tourist cities.  Anecdotally, the Council believed it didn’t have the right ‘retail mix’ to service the new tourists, and by identifying both opportunities and threats, appropriate action could be taken to better target the region’s retail offering. **THE SOLUTION** Using its exclusive Business Insight data, Datamine sized the market and spending habits of residents of each destination and analysed the current retail offering in all the requested tourist cities - highlighting the critical differences between them. For example, Datamine identified that while mainstream clothing & footwear retailers dominated in the client city, there was a noticeable lack of specialist stores present – a key driver in the ‘out of town shopping trip’ - which saw 11% of the city’s residents regularly driving a significant distance for retail therapy in another town. Datamine also noted that the top three restaurants in a similarly sized tourist town represented about the same share of ‘top 10 food destination sales’ as the three big fast food chains in in the client city, signalling an opportunity for additional food revenue if city improved its ‘tourist food destination’ offering.   **THE RESULT** The District Council understood what it needed to do to increase spend in the city by locals and tourists alike, and began working towards attracting specific retailers to fill the gap in its retail offering and fuel economic growth in the region. # How data-driven modelling changed the way FENZ prepares for emergencies **THE CHALLENGE** Fire and Emergency New Zealand (FENZ) has a simple purpose: protecting and preserving lives, property and the environment by assisting in a range of emergency situations. Managing the staggering $1.19 billion worth of property, equipment and fire appliances required to provide these services isn’t quite so simple. The agency needs to accurately assess risk, predict a range of emergency scenarios and allocate resources efficiently across New Zealand. FENZ found that its risk assessment framework wasn’t quite up to scratch. Planning focused on fire and hazardous materials scenarios, while emergencies like natural disasters, medical events, vehicle accidents and rescues weren’t included. This meant risks were not being assessed accurately – and communities were missing crucial resources. **THE SOLUTION** FENZ needed a new approach to risk prediction – and that’s where Datamine came in. Using emergency call-out data and census information, the team was able to create several detailed, highly specific models to predict future incidents. With a wider range of emergency scenarios and localised predictions, the predictive models offer an accurate picture of likely future emergencies in specific areas, helping FENZ allocate resources to match. **THE RESULT** With new risk profiling in place, FENZ has a far more accurate view of projected emergencies in different areas, so funding and resources can be distributed where they are needed. For example, should more funds be spent on thermal imaging cameras or gas detection sensors? Which areas need which resources? What breakdown of spend would produce the best possible results? Datamine’s projections help the leadership team answer these questions, and act as an objective source when talking through resourcing issues with community groups, unions and other stakeholders. For an agency dealing with life-and-death scenarios and major funding, they’re powerful tools. 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