How does your project differ from other databases available e.g. TrapNZ, Walk-the-Line and CatchIT? We are all environmental data pioneers. It’s a new very complex field of work and we’re all still working out how to do it. Xero built their system on top of 50 years of IT knowledge and 500 years of accounting knowledge and accounting is relatively easy. The above databases are great systems and a huge step forward over Excel hell. Great visualisations. I’m especially keen on CatchIT’s time-lapse videos. The above systems fulfil the need for recording predator control based on current best practice. However, there are several differences between their systems and ours.

1. Agile Cloud is a conservation engagement portal. It is a platform for providing environment data capture and data analysis. For example, we recently launched our tree loss app with the tree council. Turns out they are also interested in Myrtle rust and we have a Myrtle rust sighting app in our portal, so today for tree council logins, they now use Myrtle rust sighting as well as tree work. The dynamic nature of Agile Cloud means the system could be altered to suit their needs, and that changes could happen overnight They didn’t have to wait for the next release. It was ready the next day.

2. If a scientist wants to test out their idea, then we can make it available on the portal for just a couple of test groups. If a group wants to try something different, we can make something to their specifications and add it to the portal. No need to download a new version. Log on, it’s there. Responsive, tailored to individual groups.

3. For a system to be fully engaged, it needs to be of direct and immediate benefit to the group entering the data. The systems mentioned above provide a partial benefit, but every group that I have talked to still has a hard drive full of excel data that is disconnected from predator data. When the group has all their data in one place, they can use it for funder reports, for planning, community engagement and for communicating their story. They are more likely to record this effort if it can go into one place and it is more likely to be accurate because they themselves use this data for their own purposes. This accurate reliable information then flows through the information lifecycle and becomes useful as aggregated data to more people such as scientists, groups, territorial authorities and conservation friendly politicians.

The above screenshot describes two examples of direct and immediate benefit, from our desktop system.a. The council does a rapid ecological assessment for a significant ecological area. The excel data is

copied into Agile Cloud. It is then imported as relational data against the NZOR (which we have imported).

b. A professional environmental organisation receives an excel file from their nursery for supplying trees for their community event. It’s a 10 second job to copy the excel data into Agile Cloud and then import it as proper accurate relational data. This data is now directly and immediately available for the environmental organisation to use this data along with their attendance data for the funder report and for conservation planning. The territorial authority has visibility of both datasets to provide them with a more comprehensive view of the environment. The Mayor can use this data accurately to count his 1,000,000 trees and can drill down to what type of trees. We need more Kowhai for the Tui.

I’d like to compare this to Trees That Count. Trees That Count is a great idea but the community group has to go into all those Excel files to count their trees and Trees That Count only want trees, not shrubs so the group has to split their data by hand. There is absolutely no benefit to the group doing the work to supply Trees that Count with data, which is why their data in so inaccurate and meaningless. Trees that Count should support our mission and take our highly accurate data and use it for the purposes that they are so skilled at.

4. As predator control evolves with new science, we need to benchmark new ideas against current data. When current systems are asked to add new environmental processes, the response is they will try and get the request out in the next release.

We offer continuous delivery, which means the next release happens when we sit at the table discussing the requirement. It is faster for us to make the requirement than it is to write a document. Often, through the process of experimentation, there might be a several releases in a session as we test different ideas. The database must serve the science.

5. We can take the data from any known data source, Excel, legacy systems, other on-line systems, phone apps. This is a monster technical challenge but one we have solved. It’s really important that these apps are developed as they serve slightly different purposes but more importantly have a different culture and reach and are an expression of a person’s vision. But this data should be collated into a data collective including in the likelihood that many apps will disappear. We can be the central data collective.

What is the innovation that will be a game-changer for community conservation above the products already available? Our system currently runs 176 discrete environmental processes, from predator and weed control to ecological units and the entire New Zealand organisms register. We have never seen a system that can run so many different environmental processes.

We’ve added engagement processes, reminder emails and methods for communicating with your groups.

The above example describes the rule for sending a reminder email to your group. The rules are not hard-coded, so we can have an unlimited number of rules for sending emails. BTW, the rule says Send an email to everyone in the Ark in the park buffer Zone who hasn’t logged on since 2017-06-30.

We’ve imported every property in the country and given them a logon, speeding up the process of getting started and removing barriers to entry. We’ve created street groups and given street co-ordinator’s the tools and documentation so they can engage with property owners. Auckland alone will need 10,000 street co-ordinators, they need to be set up, trained and assisted with an infrastructure of support and specialised knowledge.

We’re building a robust and sustainable infrastructure of specialist skills such as facebook experts, communications experts, iPhone experts, neighbourhood predator control gurus, for setting up people in your neighbourhood, again in order to remove the barriers to becoming a predator control volunteer.

In the screenshot above, you can see that for the Otitori Sanctuary project, we have a group of people who can help others in the area. Klaus helps people who have issues with their phones. Sita is the local predator control specialist and also helps with creating email rules which is quite a simple but specialist skill.

We have hundreds of common reusable set of lists for all environmental processes to use and share. For example, we have a list describing weather. It doesn’t matter if it is a site visit for predators, wasps, Kauri Dieback or freshwater, the weather is described in a common format. The weather description doesn’t change just because it is connected to different environmental processes. This is another small step to accurate reliable data. It may sound obvious, but a professionally made system I reviewed described weather in two different ways depending on what species was involved.

What specific functions will your system provide to better engage volunteers in conservation and support them to work more efficiently and effectively, as well as helping with reporting to funders? As mentioned, we have 176 different functions, so here are a few examples.

Example – Better Engage Volunteers

As above, the system records volunteer groups and their involvement. In the example above, the group have regularly engaged, but haven’t engaged in the previous year. Reporting on this (via the Engagement module) allows the conservation organisation to better engage volunteers in conservation, to make sure they maintain on-going relationships with their volunteer groups and maintain a willing workforce. They only need 50 groups, church groups etc to have a community event every week, which they do.

Example - Support them to work more efficiently and effectively

In this example, the Neighbourhood network co-ordinator is provided with a map showing the performance of the street groups for predator control and number of the properties engaged in predator control.

The dark red indicates that the street group has more than half the properties engaged. The green groups are tracking ok with more than 1 in 4 properties engaged, keeping in mind this area is

highly motivated and engaged. The blue street groups are where the neighbourhood network co-ordinator needs to focus their

attention as there are less than 1 in 10 properties engaged. The neighbourhood network co-ordinator needs to help the street group co-ordinators to engage the property owners and this information is display clearly for the co-ordinator.

This idea will evolve and will become much more sophisticated over time.

Example - Helping with reporting to fundersOn page 2 of this document, we have described how tree planting counts are auto imported based on the excel file sent by the nursery.

We also record who came to the event, for how long and how many. This includes pre-event prep that may happen during the week and post event cleanup such as when a few people return to finish the planting.

We also record how the work effort was distributed amongst the volunteers. Event organisers say that this is an ideal balance between having no data and having an excessively time-consuming process that records too much detail.

Please note that we (as information designers) do not decide what is recorded. We follow the requests of groups. This data above provides them with the information they feel they need to create their funder reports accurately and without manual intervention. We are unable to provide a meaningful example of a report for this document without giving away private organisational information, so you just need to believe us that it happens.

What kind of data will be collected and how will it be collated and shared?As mention above, we currently collect 176 different environmental processes and soon it will be over 200. Agile Cloud is an environmental information platform for the ever-changing requirements of environmental data. Therefore, the answer to what kind of data we collect is every type of environmental data required to become predator free and improve our conservation values.

We are data scientists not environmental scientists. Our job is to enable environmental scientists to do their work and so we are neutral on what is being collected and how, we make sure it is accurate, reliable and reuseable, and applies to the rules of data.

A more specific answer needs to be split into three, collected, collated and shared.

CollectedAgile Cloud accepts every known type of data.

Entered directly into our desktop system. Using our phone app. Importing from other on-line systems such as Waicare and CatchIT. Lots of one-off Excel files (more than 100 for Kauri Dieback from 4 councils, MPI, DOC and Iwi). Repetitive imports of Excel files (such as rapid ecological assessments). Web Services, Web mapping Services. Imports of very valuable but hard to work with reference data such as NIWA’s weather station data.

ExampleBelow is a screen shot of how we import CatchIT data

1. We have a code free mapping process where we connect the data we are importing with our data structure, for example, the pink line shows that the control point name is made from the Area, Line and TrapName fields.

2. Our process then automatically writes the code to import the data (pink again). This allows us to import vast quantities of data quickly and easily and without the need to use expensive data specialists (like me). Because we are automating the code writing, the code is accurate and reliable and applies the rules of data and has no human error.

CollatedConservation processes are and should be in a constant of change as the science evolves. We collate data into concepts not conservation processes.

If we can record a rat at an X-Y location, then we should also be able to record any other species, weed, tree broken fence/gate, or freshwater attributes. We call this a Point of Interest.

Conservation also wants to record species at an ecological unit level. We call this SpeciesPolygon. It doesn’t matter what the polygon is so its very dynamic.

The Species Polygon and point of interest data is recorded separately from the socio-political data, so it doesn’t matter if the species is recorded as a rapid assessment or via a community planting day.

SharedThe primary complexities of sharing data are

1. Knowing the data exists.2. Making the data meaningful full to the data analysts so that the data is easy to use and to read.

Knowing the data exists - Maps

In the example above, we provide every area with a map. We are not trying to be GIS, we’re purely mapping. Sticking to mapping means we can make the system easy to use and available to non-technical people. The legend on the left is dynamic. It allows a person to see all the data based on their permissions for the area. This means that predator control, weed control and bird counts can all be shown in one place without anyone needing to touch the data.

Council biodiversity and biosecurity departments have visibility of what the community groups are doing and can plan accordingly. Local Board members (Auckland’s version of council wards) are looking to use these maps to advocate for more funding.

Example - Export to Excel Reports

The second method for making the data sharable is that we export all the data to Excel. Different Excel files for different processes. This means that groups can always get their data for analysis. Excel is a not a tool for relational data but great for slicing and dicing, planning and thinking.

Making the data meaningful; Example 1 - The human readable databaseThe third method for sharing data is we create what we call the human readable database. There is a lot of skill available in the conservation community for dealing with data, creating reports, visualisations and analysis. But none of this can happen if the data structures are not meaningful. If the data has field names like ‘Name’ and ‘Type’ then the data has no meaning.

The image above shows that we create a human readable database based on the labels on the form. On the left, is our user interface. On the right is a table from the human readable reporting database.

1. The table name is created directly from the form name, in this case ‘Bait Station Visit’.2. The field name comes directly from the label. The ‘Bait Remaining’ label is used to name the field in the

table.

For any user wanting to work with the data, the naming is very clear and based entirely on the language of the environmental process. We have never seen another system in the world that knows how to solve this problem.

We make the Human Readable Database available to any organisation that has the capability to work with databases. Based on their permissions, we create a database for them on the Microsoft Azure server SQL database platform that includes all the data they are allowed to see. This is updated each night. We are also looking at organisations updating their data directly to their servers based on Web Mapping Services protocols.

Making the data meaningful; Example 2 – Aggregated data for communicating storiesAs part of sharing the data, we need to provide groups with meaningful data. Below is an example of where we are heading, this is a mock up and clearly we need a designer and communicator. The point is, we will be creating the infrastructure and templates that include the data that is needed for co-ordinators to easily present to their volunteers with information that is exciting, rewarding and motivating. We have a plan to make this happen, just need more time.

The data collection can also be split another way. A high level summary is as below. It is a technical answer. We basically we break our system into two types of data collection. Both types are equally important to a predator free New Zealand.

Socio-Political Enviro-Centric

Private Data Public Data

Short Term Data Long Term Data

Operational Standards based

Experimental Experimental

Your Business Your Legacy

1. Enviro-centric data. Enviro-centric data is pretty straight forward, a rat was caught here. Its putting pins on a map. However, when you start adding additional enviro-centric data, it becomes very complex, which is why (until now) we don’t see a predator control process, weed management process, freshwater, litter and bird count in the same app, nevermind all the other environmental processes.

Enviro-Centric data is very long term data. It is data that will be used by generations to come, by scientists not yet born for environmental issues not yet imagined. So it has to be accurate and reliable and designed in such a way that it can be used to answer questions not yet asked.

Enviro-centric data needs to be based on modern protocols and standards, so everyone participating is working to a common format. This is pretty straight forward and all current systems should be able to do this. The hard part is experimentation. We know the science will change and the database must serve the science. No system can serve the science and experimentation better than Agile Cloud. Agile Cloud can allow for experimentation and compare the experiment to a benchmark, without too much fiddling with the data.

We have an MOU with our community groups, that all enviro-centric data is made public. However, all data visibility above a neighbourhood network is aggregated and summerised. No property data is shared. There are some exceptions such as Kauri Dieback and Myrtle rust on private land which is never shared.

2. Socio-political data. This is the data involving community engagement, street groups, community groups and third-party contractors.

Socio-political data is private data. It includes how community groups engage with their volunteers and sponsors, how much they paid for plants, timesheets.

This data is short term data. This data is needed to provide the groups with a complete view of the organisation. For example, which church groups had planting days last year that haven’t participated this year.

This is operational data. This data always remains private to the groups and details are never shared and has no value to any other group. However, aggregated data can be made available to data scientists looking for stand out game changers. For example, comparing outcomes to social media strategies. This will allow the distribution of new ideas to other groups.

This data is also experimental. What communications and social media strategies work best for different demographics.

One Last CommentWhat I have presented here in this document is a lot of complexity and technical discussion.

However, for each user, the system is very simple. Above is the entire user interface for the Willow Bay Street Group. They see about 2% of the system and only what they need to see, in this case just 6 buttons. The system has a large number of user types having access to very small parts of the system to provide for one unified environmental data collective.