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Earlier in 2015 we began hosting a team of Bachelor of IT students from Weltec so they could complete their 450 hours of practicum working on a real customer project. They were co-located with us and we inducted them rapidly into our Agile shared responsibility ways. You can read more about the team – Ella Liang, Javen Liu, John Graham and Dave Smith – here. This post is about the project deliverable itself.

The Vision for our Project – Code named Winnebago

We elected to engage the student team in developing a Minimum Viable Product (MVP) version of the Metadata Visualisation tool in our road-map, visualising data as it embarks on it’s journey through the ODE (Optimal Data Engine) structure. From their own Product Establishment document they described this as:

The end user/consumer will understand what the data is measuring, where the data entered our system from, where it has been in its journey through our system, changes that have occurred over time, and how it relates to other data. This will include dates and times.

This will illustrate the following information to an end user/consumer:

  • History – changes over time
  • Heritage – where it has been in its journey, how long did it take to transition between two points.
  • Transformations – Data is selected and changed according to business rules to represent what the business owner needs to see.
  • Relationships – How data is associated with other data in a given situation.

Tools and Technology

The student team were given a few constraints. They must use the Atlassian product set we use for project management (Jira), code management and collaboration (Bitbucket) with GIT integration and communicate with our team when they weren’t all co-located using Hipchat. Documents were all stored in Google Drive.

We also specified the deployment platform must be AWS, and they adhere to our development principles of shared, checked in and commented.

They then chose the tool set themselves which was

  • An application written in Java allowing for installation on any platform that supports the Java runtime,
  • The visualisations were developed with D3,
  • Neo4J was used as a graph data store and
  • The ODE source database is Microsoft SQL Server. 

Minimum Viable Product as delivered

The team identified a Business Analyst as their MPV’s focus customer and worked up a Persona for “Antonio the Personable Business Analyst”. Through the course of six sprints iterations of design, develop, deploy/test and refactoring resulted in a demonstrable prototype, documented and checked in for future investment.

The solution is broken into several core parts:

  1. Data-in engine which takes the configuration data from source
  2. Relationship Engine prepares the data for the visualisation
  3. Data Out Engine, which is a REST API for the front end application
  4. Front end where the user interacts with the application

winnebago

Conclusion

We were thrilled to hear the team received a B for this project. Alongside our deliverable requirements they had a vast array of project documentation for their course. They demonstrated the MVP solution at an OptimalBI team meeting and faced a range of technical questions well. I will follow this blog post with another on the process we took them through, the value of this investment for us as a business and the opportunity for others to do the same.

Great result. Vic.

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