Every organisation has obligations under the privacy act and it's easy to not meet those obligations when it comes to technology and IT systems.  Let's say you have a customer database, or you have a payroll system, or perhaps a system that your clients login to, well you now have information about people and now you have obligations around how you are collecting that information, looking after that information and destroying it.

The Privacy Commission has a great site describing your obligations here, worth a read!  You'll see there are 13 principles that need to be considered.  All of this raised a bunch of questions right?

Let's unpack some of these things but the good news there is a great tool that you can use to assess any dataset / system for its compliance with the 13 principles in the privacy act! That great tool? A Privacy Impact Assessment or PIA.  This is a document that looks at various areas to help you assess whether you are treating your data in accordance with the Privacy Act and helps you identify additional actions you need to take to get into compliance. 

So what do we need to look at?  There are five areas around a data set / system:

• What data are you collecting?  Is it personal information, is it health information?
• What processes are around the collection and processing of the data?  
• Where is the data stored? How is it moved between systems or from the client to you?
• What contracts or agreements do you have in place for data collection?  Do the people know you are collecting the data? How are you letting them know
• How are you keeping it safe? Technology and processes around your data?

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All a little overwhelming right?  Well...
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In the evolving landscape of early education, the effective use of data analytics has become a cornerstone for enhancing the service delivery and outcomes. In New Zealand, where the socio-economic landscape is diverse, understanding how resources are allocated and utilised is crucial. 
 
For an extended period, we have been analysing data from early education services across New Zealand, focusing on achieving operational excellence, improving childcare quality, and supporting the community. Our approach involves aggregating data from various systems used by these services and identifying correlations within the datasets. 
 
Geographic Information Systems (GIS) are widely recognised for their robust applications in fields such as geology, natural resource management, and urban planning. Its use extends to health through the GeoHealth Laboratory, established in 2004 as a collaboration between the University of Canterbury and the Ministry of Health in New Zealand. However, its application in education planning remains limited. 
 
We see an opportunity to leverage GIS in the education sector to enhance outcomes for children in early education services. Driven by curiosity, we conducted an exploratory study using publicly available data to determine whether early learning services in different socio-economic regions receive the intended equity funding. Equity funding from the Ministry of Education is available to eligible early learning services, particularly those serving lower socio-economic communities, children with special needs, non-English-speaking backgrounds, services provided in languages other than English, and those with high isolation indexes. 
 
Our hypothesis posits that early learning services in highly deprived areas would have an equity index of 1 or 2, thereby attracting funding to support these children and their communities. For our analysis, we utilised deprivation data from 2018 and an Early Childhood Education (ECE) directory, which offers valuable information such as location and the current equity index of most services. 
 
We focused on South Auckland for two main reasons: we have clients in the region, and data is available to establish correlations between the equity index and the origins of Tamariki (children) for each centre. The map we developed currently represents data for all early education services in South Auckland. ​

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​Data sources:

• ECE Directory - https://catalogue.data.govt.nz/dataset/directory-of-educational-institutions/resource/a9d65b07-8483-4b05-bdfd-d2abe4f38827
• Deprivation Index - https://www.otago.ac.nz/wellington/research/groups/research-groups-in-the-department-of-public-health/hirp/socioeconomic-deprivation-indexes-nzdep-and-nzidep-department-of-public-health#2018

 
Preliminary findings indicate that most deprived areas have a concentration of centres with an equity index of 1 or 2, while more affluent regions typically feature centres with an equity index of 5 or higher. Our interest for further investigation lies in conducting a deep dive into regions with a deprivation index of 8 and above, alongside centres with an equity index of 5 or higher.
 
Our exploratory study highlights the potential for GIS to enhance data analysis and decision making in early education services. While our initial findings suggest a relationship between deprivation levels and equity index ratings, further research is needed to paint a more comprehensive picture. These areas may require further mapping of Tamariki locations, which, although not providing a complete picture, could offer additional insights. To refine our analysis, we need more information about parents, including their income, skills, qualifications, benefits received, and household crowding levels.
 
As we continue to explore these avenues, our goal remains to support early education services that enables equitable access to quality early education, ensuring that all children regardless of their circumstances, have the opportunities they deserve.
 
 
About Resolution8
Our vision is to deliver positive transformation and enduring outcomes.
We love data and curiosity drives our passion for it. We are continually learning and adapting to understand data, assist in improving quality, and derive actionable insights from them to achieve our vision while collaborating with our partners.