One Defence Data Overly Ambitious

The ABC reports that "$100m Defence contract with KPMG rife with governance failures, review finds" (Linton Besser, Andrew Greene, ABC, 20 December 2023). The contact concerned Defence ICT2284 "One Defence Data" (1DD). 1DD is an ambitious project to unify all Defence data. This project appears to have been overly ambitious, and should have been scaled back.

As it happens I was the Senior Policy Adviser on Data Administration Standards at DoD from 1990 to 1994, and know how hard bringing disparate data sources jealously guarded by different stakeholders is. 

1DD includes an enterprise-wide data catalogue.  

"The One Defence Data Program (Program) will establish and deliver the governance, standards and change management to drive information management transformation across the Department of Defence (Defence). Tranche 1 will develop the foundational and technical enablement capabilities that will continue to be built out over future tranches of the Program." ICT 2284 – One Defence Data Program – Tranche 1, KPMG Australia Technology Solutions Pty Limited (KTech), for DoD, Mon 02 May 2022. 

The Department has a Defence Data Strategy 2021-2023:

Flat-pack Learning Analytics

Greetings from ASCILITE 2023 where Leah Macfadyen just spoke on 'The “IKEA Model” for Pragmatic Development of a Customizable Learning Analytics'. The idea is a kit of code to do analysis from the learning management system, in this case Canvas. 

I spent the session wondering where I knew the speaker from. Perhaps when I gatecrashed a UBC staff meeting nine years ago.

Data Drives the Smart City

The World Wide Web Consortium (W3C) and the Australian National University (ANU) are holding a half day event on "Data Drives the Smart City", in Melbourne 7 May, Canberra 8 May and Sydney 10 May, 2018.

"The half day conference will explore the challenges and progress made in the technology and underpinning standards framework needed to enable smart cities. You will hear from leading experts in the field on how challenges are being tackled. ...

Our world is increasingly being shaped by the vast amount of data being produced in every aspect of our lives. As more devices get connected through the Internet of Things (IoT), harnessing big data in an integrated way can offer valuable insights that can help achieve smart city goals. This comes with important and interesting challenges to solve in order to actualise the smart city vision.Challenges include data collection, integration and privacy.

TOPICS:

Topics to be addressed include perspectives from Government, tech industry leadership, Web standards for spatial data and city sensing, technical solutions to privacy management, and smart grid futures. A panel session will discuss capacity building for smart cities.

SPEAKERS:

Speakers include Dr Ian Oppermann (NSW Chief Data Scientist), Dr Ole Nielsen (ACT Chief Digital Officer), J. Alan Bird (W3C Global Business Development Lead), Dr Mukesh Mohania (IBM Distinguished Engineer in IBM Research), Dr David Hyland-Wood (Blockchain Protocol Architect, Consensys), Dr Lachlan Blackhall (Entrepreneurial Fellow and Head, Battery Storage and Grid Integration Program), Dr Kerry Taylor (Chair, W3C Spatial Data on the Web), Dr Peter Christen (Professor, Data Mining and Matching, ANU), and Dr Armin Haller (W3C Office Manager, ANU)."

"

Gates Foundation report on Student Data for Personalized Learning

The Bill & Melinda Gates Foundation has released the 36 page report "Teachers Know Best" along with a two page summary "How Teachers Approach Data". The report advocates the use of "... student data to tailor and improve instruction for individual students..." (p. cov2). Such an approach, I suggest, may do more harm than good, by diverting resources away from the design of quality instructional materials and by setting unrealistic expectation as to the level of tailoring possible with the resources available to teachers.

The report divided teachers into Data Marvens 28%, Growth Seekers 20%, Aspirational users 17%, Scorekeepers 11 %, Perceptives 14 %, and Traditionalistss 10%. The report's authors clearly believe that the marven's data-driven personalized instruction is preferable. However, where are teachers going to get the data and will they be given the time to personalize student's instruction? Assuming no more resources are provided, the funding to provide data analysis tools will come from the education budget and reduce resources for course materials. Similarly, more time by teachers taken on a personalized approach will result in lass time for class teaching.

I suggest what is instead required is instructional design, which individual teachers do not have the time or  resources to do. Statistical analysis can be used to crunch the numbers on large numbers of students to see what educationally works and what does not work and what aspects of subjects students have difficulty with. These insights can be built into the educational materials and teacher training. Teaching can be given help in identifying what students will have difficulty with and how to help them. But each teacher does not need to become a statistician to do this.

There are ways to use personalized learning to help students. However, it also has to be done in a way which helps teachers and is affordable. As an example, last year I used peer assessment in my ICT Sustainability course. This helps students, as by having to assess their peer's work, students gain insight about their own work. As a by-product this reduces the assessment load of the teacher (and also reduces student appeals, as students are less likely to object to the marks their peers give).

Learning Data Analytics

Data Analytics is the search for meaningful patters in data. This has become a skill in demand with large amounts of data on the behavior of users of the Internet becoming available as a byproduct of online system. This is referred to as "Information analysis" (INAN) in the Skills Framework for the Information Age (SFIA) Category: Strategy and architecture, Subcategory: Information Strategy at skill levels 3 to 7 :

"The validation and analysis of information, including the ability to discover and quantify patterns in data of any kind, including numbers, symbols, text, sound and image. The relevant techniques include statistical and data mining or machine learning methods such as rule induction, artificial neural networks, genetic algorithms and automated indexing systems." From SFIA Skill Descriptions, BCS, 2011

SAS sponsored a UK report on "Big Data Analytics: Assessment of Demand for Labour and Skills, 2013-2020" (November 2014). Some universities have assembled whole degrees around analytics, but these are largely repackaging of existing university IT courses (Voorhis, Trovati, & Self, 2014). Deakin, La Trobe and other universities are offering Masters and diplomas in data analytics (mostly "business analytics").

Many university courses involve the analysis of large amounts of data, but usually not using the term "Data Analytics". The R Programming Language is popular for these. There are specialized courses and packages for specific disciplines (I attending a short course today on the analysis of learning management system data from Moodle).

UCSD offer an online "Introduction to Big Data Analytics" as part of their Big Data Certificate, through Coursera.


ANU offers "Engineering Data Analytics" (ENGN8535), as a full semester masters course. There is also a two day Professional Development short course "How to communicate your data story.

References

Voorhis, D., Trovati, M., & Self, R. (2014). Designing Big Data Analytics Undergraduate and Postgraduate Programmes for Employability. http://computing.derby.ac.uk/wordpress/wp-content/uploads/2012/11/Designing-Big-Data-Analytics-Programmes-for-Undergraduate-and-Masters-Students.pdf

Privacy Preserving Data Integration Strategies

Professor Bradley Malin, Vanderbilt University (Nashville, USA), will speak on "Towards Practical Private Data Integration and Analysis" 4pm 26 August 2013, in the famous room N101 at the Australian National University in Canberra.

Towards Practical Private Data Integration and Analysis

Assoc Prof Bradley Malin (Vanderbilt University, Nashville)

DATE: 2013-08-26
TIME: 16:00:00 - 17:00:00
LOCATION: CSIT Seminar Room, N101

ABSTRACT:

Over the past decade, it has been repeatedly demonstrated that data devoid of explicit identifiers can be linked back to the identities of the individuals from which it was derived. This has made organizations increasingly apprehensive about sharing person-specific information. Yet, with the dawn of the big data age upon us, it is imperative that data sharing proliferate to ensure that researchers can validate published research findings, combine datasets to discover novel associations, and comply with open data initiatives. In this talk, I will review recent research on privacy preserving data integration strategies that are efficient, effective, and obscure personal identities in the process. This talk will further illustrate how such integration can enable biomedical association studies while obfuscating the identities of the corresponding participants.

BIO:

Bradley Malin, Ph.D., is the Vice Chair for Research and an Associate Professor of Biomedical Informatics in the School of Medicine at Vanderbilt University. He is also an Associate Professor of Computer Science in the School of Engineering and is Affiliated Faculty in the Center for Biomedical Ethics and Society. He is the founder and current director of the Health Information Privacy Laboratory (HIPLab), conducts technologies that enable privacy in the context of real world organizational, political, and health information architectures. Dr. Malin's research has been cited by the U.S. Federal Trade Commission and featured in popular media outlets, including Nature News, Scientific American, and Wired magazine. He has received several awards of distinction from the American and International Medical Informatics Associations and, in 2009, he was honored as a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor bestowed by the U.S. government on outstanding scientists and engineers beginning their independent careers. Dr. Malin completed his education at Carnegie Mellon University in Pittsburgh, PA, where he received a bachelor's in biological sciences, a master's in data mining and knowledge discovery, a master's in public policy and management, and a doctorate in computer science.