Art of Analytics: Employment Flares

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About the Insights

This data visualization represents claims made by a Service Provider against an Employer. The nodes in the middle of each small "explosion" represent the Service Provider, the nodes at the periphery represent Employers and the edges between them represent the relationships. The thickness of the edge is proportional to the value claimed.


The visualization was used to look at the relationship structures between service providers and employers. Service Providers help people find employment and also provide job seekers with ongoing support to retain their jobs. To be effective and to provide personalized and flexible services to job seekers, a Service Provider would typically have strong connections with a large number of Employers.


The visualization was used to look for unusual type of connections. For example:

  • An isolated group where a Service Provider is connected to many Employers but in a network separated from the rest of the graph.
  • One Service Provider connected to only one employer.
  • Loops where a Service Provider is also an employer.

Driver behind this business case is a government body, the Department of Employment, which is responsible for monitoring the way employment services are delivered. The providers liaise with local employers and registered training organizations, to provide the right mix of support for job seekers. The goal of this project was to investigate significant and systemic non-compliance that exists in the claims made.


About the Analytics

This visualization shows a network graph created using Teradata Aster Lens. Claims data from the Department of Employment were loaded into the Teradata Aster discovery platform.


Claims were classified then tested for veracity by chronology, geolocation and variation; and analysed longitudinally for processing and event anomalies. Network graphs were generated to observe patterns of collusion. This provided a quick way to see which Service Providers claimed money against which Employers.


The visualization was also used for comparison between different time periods. Similar graphs can be constructed on a periodic basis to see if new isolations or patterns appear over time in the network.

About the Analyst

Tatiana Bokareva is a Data Scientist with the Teradata Advanced Analytics team in Australia and New Zealand. Originally from Moscow, Tatiana now lives in Sydney with her 2 young Australians and she works with key clients in the financial services, government and telecommunications industries. Tatiana is a Teradata blogger and particularly enjoys combining her passion for analytics with her practical, hands on experience in fashion and retail! She is published extensively and has presented at many international conferences.


Tatiana has a Bachelor degree with Honours and PhD in Computer Science from the University of New South Wales. Tatiana was runner up for the Dean Postgraduate Research Award in Information and Communication sector. She was awarded the prestigious Women in Engineering Scholarship. During her PhD she was trained at NICTA (National ICT Australia).


Tatiana's PhD was in the area of the Internet of Things (IoT), namely her work evolved around building self-reliant, self-healing, fault tolerant sensor networks. She also held part time research appointments at the university.