Our Projects | THE DATA FOUNDRY

Sydney Swans and The Data Foundry

The Sydney Swans AFL Club (The Swans) engaged The Data Foundry (TDF) to build an AWS Data Lake based on TDF’s accelerated data lake pattern to unify data across its player development programs.

For the project, TDF onboarded data from a variety of platforms, including the Protrac player tracking and analytics platform as well as building the initial AWS Data Lake, which became known internally as “Swan Lake”.

TDF worked with AWS and The Swans to establish an Amazon QuickSight capability to enable The Swans to build player insight dashboards. Swan Lake was also connected to The Swans existing business intelligence toolset to allow for improved data reporting, data analytics, and data visualisation.

“We were connected with the TDF team via AWS and were immediately impressed with their energy, capabilities and client-first mindset. Paul Macey and the team spent the time to truly understand the needs of our high-performance environment, and delivered a product set that not only exceeded expectations, but was on time and on budget. We are already exploring opportunities to extend our engagement of TDF and look forward to the outcomes this will realise in 2023 and beyond.”

Drew Arthurson – Chief Operating Officer – Sydney Swans

As a result of the successful work to date, The Swans are now working with TDF on scoping subsequent projects to onboard more datasets and provide additional capabilities, including a Player Readiness dashboard and enhanced performance analytics capabilities.

Department of Justice & Community Safety (Vic) and The Data Foundry

The Victorian Department of Justice & Community Safety (DJCS) engaged The Data Foundry (TDF) to build an AWS Data Lake based on TDF’s accelerated data lake pattern.

TDF onboarded data into DJCS’ Enterprise Data Lake (EDL) from a variety of platforms, ranging from field operations-supporting Azure workloads to highly visible statistical workloads.

TDF also established an Amazon SageMaker machine learning capability to enable DJCS to improve its predictive COVID risk modelling capability.

“The Data Foundry (TDF) has been a highly valuable partner in delivering a complex enterprise data platform with very demanding security requirements. Working with the team at TDF, my team feel we and TDF are part of the same team. That allows us to feel confident that we are fully supported in our endeavour. TDF has been able to understand our diverse requirements and translate those into an effective and working platform that delivers high value to the organisation.”

Mark Dobroff – Senior Project Manager – Data & Analytics – DJCS

As a result of the initial successful EDL project, TDF has been consistently engaged by DJCS to further enhance the EDL, onboard additional workloads and re-factor them to take advantage of AWS’ cloud-native service capabilities.

RMIT University and The Data Foundry

RMIT University (RMIT) engaged The Data Foundry (TDF) to support the development of the RMIT Amazon Web Services (AWS) Cloud Supercomputing (RACE) Hub, designed to provide scalable computing infrastructure to researchers and students at the university, allowing them to access the necessary compute resources for their tasks easily and on-demand.

TDF built and implemented the pilot solution that would become the RACE Hub, and assisted in onboarding the initial workloads from a variety of platforms, demonstrating the out-of-the-box capabilities and customisability of Service Workbench on AWS, and it’s suitability to meet RMIT’s research needs.

“The Data Foundry offers an exceptional level of expertise in an easily accessible manner. They know where to focus energy, what to prioritise and when to raise an issue. I’ve been very happy with their performance to date.”

Nick Balkin – Senior Project Manager – University Operations – ITS – RMIT University

The RACE Hub is currently being used by the Integrated Photonics and Applications Centre to perform compute-heavy and complex tasks faster than was previously possible, in such applications as simulating and visualising the propagation of light on an integrated photonic chip.

Golf Australia and The Data Foundry

Golf Australia (GA) engaged The Data Foundry (TDF) to build an AWS Data Lake based on TDF’s accelerated data lake pattern to unify data across its athlete development programs.

For the project, TDF onboarded data from a variety of platforms, including three global golf ranking websites, the Australian golf handicapping service (i.e., GOLF Link), athlete management software (i.e., Smartabase), golf technology products (i.e., TrackMan), and third-party data providers (i.e., DataGolf).

TDF established an Amazon SageMaker capability to enable GA to build predictive Machine Learning models and identify and track emerging golfing talent leading into the 2024 and 2028 Olympic Games. The Data Lake was also connected to GA’s existing Tableau toolset to allow for improved data reporting, data analytics, and data visualisation.

“The team from TDF were easy to work with, took the time to understand our desired use cases, and produced a high-quality solution. By reaching our target state of all data in one place, we can now automate manual processes and keep our team focused on what they do best: producing world-class athletes. We recommend TDF to similar customers looking for a high-performing data and technology partner.”

Dr Jarred Pilgrim – Data & Analytics Manager – Golf Australia

As a result of the successful Phase 1, GA is working with TDF on scoping Phase 2 to onboard more datasets and add additional capabilities, including an Athlete Performance Portal, improved Data Curation, and enhanced Machine Learning capabilities.

New Zealand (NZL) Sail GP and The Data Foundry

The Data Foundry (TDF) was engaged by NZL Sail GP to demonstrate the art of the possible of taking streaming data from F50 yachts, and then aggregate and transform the data in real time. The streaming data was being produced at a rate of 50 events a second by over 1000 sensors. This enabled NZL Sail GP to perform advanced analytics at the end of each race so the crew could make immediate changes to their strategy and how they interacted with their yacht.

The solution was build in under a week using AWS data services.

As a result, the NZL SailGP team were able to ingest yacht data into their data visualisation tool so they could make immediate changes before each race.

Sail GP website

“The Data Foundry has been an amazing team to work with while trying to maximize the utilization of data while racing Sail GP. Their team has brought a massive amount of experience and expertise to create user-friendly solutions.

Peter Burling, NZL Sail GP team skipper

RMIT Online and The Data Foundry

The Data Foundry (TDF) was engaged by RMIT Online to build an AWS Lake House comprising a Data Lake and Data Warehouse, together with an integrated catalogue that included their existing ELDAR data warehouse.

The project included onboarding data from a variety of platforms including Google Analytics, Facebook, and LinkedIn. An Amazon SageMaker capability was established to enable RMIT Online to start building a range of Machine Learning models and the Lake House was also connected to their existing Tableau toolset to allow ongoing reporting, analytics, and visualisation of the Lake House data.

As a result of this successful Phase 1 project, RMIT Online has already started working with TDF on scoping Phase 2, where they intend to onboard more datasets, including their Canvas Learning Management System data and PeopleSoft (SAMS) Student Administration data.

“The Data Foundry has worked beside our fantastic data and tech teams in RMIT Online several times, lending a hand for heavy lifting projects, cutting different sources of data together and extracting powerful insights from it. As a digital business, insight drives our student experiences. I could not recommend this team more, for having some of the best approaches, tech and skills anywhere, to extract and use those insights from diverse sources and helping even accomplished data experts, take data engineering and insight to the next level.”

Helen Souness – CEO – RMIT Online

The University of Melbourne and The Data Foundry

The Data Foundry (TDF) was initially engaged by the University of Melbourne (UoM) to support the marketing campaign associated with their Virtual Open Day project. TDF designed, built, documented and handed over a series of Amazon QuickSight-based dashboards that presented data elements sourced from a range of backend UoM systems. Since then, TDF has gone on to work with UoM on a number of other data-related projects including data engineering, data pipelines, enterprise data lake, data onboarding and additional data visualisation solutions. They have integrated a range of backend systems into UoM’s AWS-based enterprise data lake (EDL) including Salesforce, Google Analytics, Canvas and StudentOne and assisted UoM in implementing a DataOps approach to managing the EDL, including supporting processes and toolsets.

“The team from TDF have become an integral extension of our team over a very short period of time. They are easy to work with, know their stuff and produce high-quality solutions and documentation. Our end-users love the way they just get the job done, without any fuss or unnecessary delay. TDF call themselves a ‘One Stop Data Shop’ and they’ve certainly lived up to that reputation amongst my team. We would have no hesitation recommending TDF to similar customers looking for a trusted data partner that knows both the AWS and data space, inside out.”

Jason McKay – Director Strategy, Innovation & Assurance – University of Melbourne

RMIT University and The Data Foundry

The Data Foundry (TDF) was engaged by RMIT University to support the deployment of an AWS Accelerated Data Lake (ADL) Proof of Value (PoV) solution. The project included onboarding Student Management (PeopleSoft Campus) and Learning Management (Canvas) data into the ADL, as well as the replication of a number of existing Student Retention Machine Learning (ML) models.

The project involved bringing together many stakeholders from multiple RMIT teams for a short, sharp, six-week sprint. The “one team” approach worked really well and people felt at ease from Day One. The team bult a secure, robust, AWS-based data lake then converted the existing ML algorithms to AWS SageMaker, allowing RMIT to run its student retention models in less time and at a lower cost than the former on-premise approach, whilst adding improved security, scalability and reliability.

“Brad and The Data Foundry team demonstrated great capability, dedication and enthusiasm in delivering the ADL PoV. The approach was really inclusive and created a great ‘one team’ vibe, drawing together multiple vendors with multiple groups from the Uni. The PoV was seen as a success, giving the internal teams confidence in moving to a cloud-based analytics environment.”

Ben Stevens – Director of Data Architecture and Engineering – RMIT University